Clinical review: Medication errors in critical care

PubMed Central

Moyen, Eric; Camiré, Eric; Stelfox, Henry Thomas

2008-01-01

Medication errors in critical care are frequent, serious, and predictable. Critically ill patients are prescribed twice as many medications as patients outside of the intensive care unit (ICU) and nearly all will suffer a potentially life-threatening error at some point during their stay. The aim of this article is to provide a basic review of medication errors in the ICU, identify risk factors for medication errors, and suggest strategies to prevent errors and manage their consequences. PMID:18373883

Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches

USGS Publications Warehouse

Nevers, Meredith B.; Whitman, Richard L.

2011-01-01

Efforts to improve public health protection in recreational swimming waters have focused on obtaining real-time estimates of water quality. Current monitoring techniques rely on the time-intensive culturing of fecal indicator bacteria (FIB) from water samples, but rapidly changing FIB concentrations result in management errors that lead to the public being exposed to high FIB concentrations (type II error) or beaches being closed despite acceptable water quality (type I error). Empirical predictive models may provide a rapid solution, but their effectiveness at improving health protection has not been adequately assessed. We sought to determine if emerging monitoring approaches could effectively reduce risk of illness exposure by minimizing management errors. We examined four monitoring approaches (inactive, current protocol, a single predictive model for all beaches, and individual models for each beach) with increasing refinement at 14 Chicago beaches using historical monitoring and hydrometeorological data and compared management outcomes using different standards for decision-making. Predictability (R2) of FIB concentration improved with model refinement at all beaches but one. Predictive models did not always reduce the number of management errors and therefore the overall illness burden. Use of a Chicago-specific single-sample standard-rather than the default 235 E. coli CFU/100 ml widely used-together with predictive modeling resulted in the greatest number of open beach days without any increase in public health risk. These results emphasize that emerging monitoring approaches such as empirical models are not equally applicable at all beaches, and combining monitoring approaches may expand beach access.

Nematode Damage Functions: The Problems of Experimental and Sampling Error

PubMed Central

Ferris, H.

1984-01-01

The development and use of pest damage functions involves measurement and experimental errors associated with cultural, environmental, and distributional factors. Damage predictions are more valuable if considered with associated probability. Collapsing population densities into a geometric series of population classes allows a pseudo-replication removal of experimental and sampling error in damage function development. Recognition of the nature of sampling error for aggregated populations allows assessment of probability associated with the population estimate. The product of the probabilities incorporated in the damage function and in the population estimate provides a basis for risk analysis of the yield loss prediction and the ensuing management decision. PMID:19295865

Modeling and predicting historical volatility in exchange rate markets

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-04-01

Volatility modeling and forecasting of currency exchange rate is an important task in several business risk management tasks; including treasury risk management, derivatives pricing, and portfolio risk evaluation. The purpose of this study is to present a simple and effective approach for predicting historical volatility of currency exchange rate. The approach is based on a limited set of technical indicators as inputs to the artificial neural networks (ANN). To show the effectiveness of the proposed approach, it was applied to forecast US/Canada and US/Euro exchange rates volatilities. The forecasting results show that our simple approach outperformed the conventional GARCH and EGARCH with different distribution assumptions, and also the hybrid GARCH and EGARCH with ANN in terms of mean absolute error, mean of squared errors, and Theil's inequality coefficient. Because of the simplicity and effectiveness of the approach, it is promising for US currency volatility prediction tasks.

Conflict effects without conflict in anterior cingulate cortex: multiple response effects and context specific representations

PubMed Central

Brown, Joshua W.

2009-01-01

The error likelihood computational model of anterior cingulate cortex (ACC) (Brown & Braver, 2005) has successfully predicted error likelihood effects, risk prediction effects, and how individual differences in conflict and error likelihood effects vary with trait differences in risk aversion. The same computational model now makes a further prediction that apparent conflict effects in ACC may result in part from an increasing number of simultaneously active responses, regardless of whether or not the cued responses are mutually incompatible. In Experiment 1, the model prediction was tested with a modification of the Eriksen flanker task, in which some task conditions require two otherwise mutually incompatible responses to be generated simultaneously. In that case, the two response processes are no longer in conflict with each other. The results showed small but significant medial PFC effects in the incongruent vs. congruent contrast, despite the absence of response conflict, consistent with model predictions. This is the multiple response effect. Nonetheless, actual response conflict led to greater ACC activation, suggesting that conflict effects are specific to particular task contexts. In Experiment 2, results from a change signal task suggested that the context dependence of conflict signals does not depend on error likelihood effects. Instead, inputs to ACC may reflect complex and task specific representations of motor acts, such as bimanual responses. Overall, the results suggest the existence of a richer set of motor signals monitored by medial PFC and are consistent with distinct effects of multiple responses, conflict, and error likelihood in medial PFC. PMID:19375509

Competition between learned reward and error outcome predictions in anterior cingulate cortex.

PubMed

Alexander, William H; Brown, Joshua W

2010-02-15

The anterior cingulate cortex (ACC) is implicated in performance monitoring and cognitive control. Non-human primate studies of ACC show prominent reward signals, but these are elusive in human studies, which instead show mainly conflict and error effects. Here we demonstrate distinct appetitive and aversive activity in human ACC. The error likelihood hypothesis suggests that ACC activity increases in proportion to the likelihood of an error, and ACC is also sensitive to the consequence magnitude of the predicted error. Previous work further showed that error likelihood effects reach a ceiling as the potential consequences of an error increase, possibly due to reductions in the average reward. We explored this issue by independently manipulating reward magnitude of task responses and error likelihood while controlling for potential error consequences in an Incentive Change Signal Task. The fMRI results ruled out a modulatory effect of expected reward on error likelihood effects in favor of a competition effect between expected reward and error likelihood. Dynamic causal modeling showed that error likelihood and expected reward signals are intrinsic to the ACC rather than received from elsewhere. These findings agree with interpretations of ACC activity as signaling both perceptions of risk and predicted reward. Copyright 2009 Elsevier Inc. All rights reserved.

Prediction Model for Predicting Powdery Mildew using ANN for Medicinal Plant— Picrorhiza kurrooa

NASA Astrophysics Data System (ADS)

Shivling, V. D.; Ghanshyam, C.; Kumar, Rakesh; Kumar, Sanjay; Sharma, Radhika; Kumar, Dinesh; Sharma, Atul; Sharma, Sudhir Kumar

2017-02-01

Plant disease fore casting system is an important system as it can be used for prediction of disease, further it can be used as an alert system to warn the farmers in advance so as to protect their crop from being getting infected. Fore casting system will predict the risk of infection for crop by using the environmental factors that favor in germination of disease. In this study an artificial neural network based system for predicting the risk of powdery mildew in Picrorhiza kurrooa was developed. For development, Levenberg-Marquardt backpropagation algorithm was used having a single hidden layer of ten nodes. Temperature and duration of wetness are the major environmental factors that favor infection. Experimental data was used as a training set and some percentage of data was used for testing and validation. The performance of the system was measured in the form of the coefficient of correlation (R), coefficient of determination (R2), mean square error and root mean square error. For simulating the network an inter face was developed. Using this interface the network was simulated by putting temperature and wetness duration so as to predict the level of risk at that particular value of the input data.

Risk Assessment Stability: A Revalidation Study of the Arizona Risk/Needs Assessment Instrument

ERIC Educational Resources Information Center

Schwalbe, Craig S.

2009-01-01

The actuarial method is the gold standard for risk assessment in child welfare, juvenile justice, and criminal justice. It produces risk classifications that are highly predictive and that may be robust to sampling error. This article reports a revalidation study of the Arizona Risk/Needs Assessment instrument, an actuarial instrument for juvenile…

Residential magnetic fields predicted from wiring configurations: II. Relationships To childhood leukemia.

PubMed

Thomas, D C; Bowman, J D; Jiang, L; Jiang, F; Peters, J M

1999-10-01

Case-control data on childhood leukemia in Los Angeles County were reanalyzed with residential magnetic fields predicted from the wiring configurations of nearby transmission and distribution lines. As described in a companion paper, the 24-h means of the magnetic field's magnitude in subjects' homes were predicted by a physically based regression model that had been fitted to 24-h measurements and wiring data. In addition, magnetic field exposures were adjusted for the most likely form of exposure assessment errors: classic errors for the 24-h measurements and Berkson errors for the predictions from wire configurations. Although the measured fields had no association with childhood leukemia (P for trend=.88), the risks were significant for predicted magnetic fields above 1.25 mG (odds ratio=2.00, 95% confidence interval=1.03-3.89), and a significant dose-response was seen (P for trend=.02). When exposures were determined by a combination of predictions and measurements that corrects for errors, the odds ratio (odd ratio=2.19, 95% confidence interval=1.12-4.31) and the trend (p =.007) showed somewhat greater significance. These findings support the hypothesis that magnetic fields from electrical lines are causally related to childhood leukemia but that this association has been inconsistent among epidemiologic studies due to different types of exposure assessment error. In these data, the leukemia risks from a child's residential magnetic field exposure appears to be better assessed by wire configurations than by 24-h area measurements. However, the predicted fields only partially account for the effect of the Wertheimer-Leeper wire code in a multivariate analysis and do not completely explain why these wire codes have been so often associated with childhood leukemia. The most plausible explanation for our findings is that the causal factor is another magnetic field exposure metric correlated to both wire code and the field's time-averaged magnitude. Copyright 1999 Wiley-Liss, Inc.

Self-reported medical, medication and laboratory error in eight countries: risk factors for chronically ill adults.

PubMed

Scobie, Andrea

2011-04-01

To identify risk factors associated with self-reported medical, medication and laboratory error in eight countries. The Commonwealth Fund's 2008 International Health Policy Survey of chronically ill patients in eight countries. None. A multi-country telephone survey was conducted between 3 March and 30 May 2008 with patients in Australia, Canada, France, Germany, the Netherlands, New Zealand, the UK and the USA who self-reported being chronically ill. A bivariate analysis was performed to determine significant explanatory variables of medical, medication and laboratory error (P < 0.01) for inclusion in a binary logistic regression model. The final regression model included eight risk factors for self-reported error: age 65 and under, education level of some college or less, presence of two or more chronic conditions, high prescription drug use (four+ drugs), four or more doctors seen within 2 years, a care coordination problem, poor doctor-patient communication and use of an emergency department. Risk factors with the greatest ability to predict experiencing an error encompassed issues with coordination of care and provider knowledge of a patient's medical history. The identification of these risk factors could help policymakers and organizations to proactively reduce the likelihood of error through greater examination of system- and organization-level practices.

Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

Neural substrates of updating the prediction through prediction error during decision making.

PubMed

Wang, Ying; Ma, Ning; He, Xiaosong; Li, Nan; Wei, Zhengde; Yang, Lizhuang; Zha, Rujing; Han, Long; Li, Xiaoming; Zhang, Daren; Liu, Ying; Zhang, Xiaochu

2017-08-15

Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making. Copyright © 2017 Elsevier Inc. All rights reserved.

Applying artificial neural networks to predict communication risks in the emergency department.

PubMed

Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

2015-10-01

To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error.

PubMed

Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M; Berman, D M; Blume-Jensen, P

2014-09-09

Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively. Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error. Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

Iowa radon leukaemia study: a hierarchical population risk model for spatially correlated exposure measured with error.

PubMed

Smith, Brian J; Zhang, Lixun; Field, R William

2007-11-10

This paper presents a Bayesian model that allows for the joint prediction of county-average radon levels and estimation of the associated leukaemia risk. The methods are motivated by radon data from an epidemiologic study of residential radon in Iowa that include 2726 outdoor and indoor measurements. Prediction of county-average radon is based on a geostatistical model for the radon data which assumes an underlying continuous spatial process. In the radon model, we account for uncertainties due to incomplete spatial coverage, spatial variability, characteristic differences between homes, and detector measurement error. The predicted radon averages are, in turn, included as a covariate in Poisson models for incident cases of acute lymphocytic (ALL), acute myelogenous (AML), chronic lymphocytic (CLL), and chronic myelogenous (CML) leukaemias reported to the Iowa cancer registry from 1973 to 2002. Since radon and leukaemia risk are modelled simultaneously in our approach, the resulting risk estimates accurately reflect uncertainties in the predicted radon exposure covariate. Posterior mean (95 per cent Bayesian credible interval) estimates of the relative risk associated with a 1 pCi/L increase in radon for ALL, AML, CLL, and CML are 0.91 (0.78-1.03), 1.01 (0.92-1.12), 1.06 (0.96-1.16), and 1.12 (0.98-1.27), respectively. Copyright 2007 John Wiley & Sons, Ltd.

When does speech sound disorder matter for literacy? The role of disordered speech errors, co-occurring language impairment and family risk of dyslexia.

PubMed

Hayiou-Thomas, Marianna E; Carroll, Julia M; Leavett, Ruth; Hulme, Charles; Snowling, Margaret J

2017-02-01

This study considers the role of early speech difficulties in literacy development, in the context of additional risk factors. Children were identified with speech sound disorder (SSD) at the age of 3½ years, on the basis of performance on the Diagnostic Evaluation of Articulation and Phonology. Their literacy skills were assessed at the start of formal reading instruction (age 5½), using measures of phoneme awareness, word-level reading and spelling; and 3 years later (age 8), using measures of word-level reading, spelling and reading comprehension. The presence of early SSD conferred a small but significant risk of poor phonemic skills and spelling at the age of 5½ and of poor word reading at the age of 8. Furthermore, within the group with SSD, the persistence of speech difficulties to the point of school entry was associated with poorer emergent literacy skills, and children with 'disordered' speech errors had poorer word reading skills than children whose speech errors indicated 'delay'. In contrast, the initial severity of SSD was not a significant predictor of reading development. Beyond the domain of speech, the presence of a co-occurring language impairment was strongly predictive of literacy skills and having a family risk of dyslexia predicted additional variance in literacy at both time-points. Early SSD alone has only modest effects on literacy development but when additional risk factors are present, these can have serious negative consequences, consistent with the view that multiple risks accumulate to predict reading disorders. © 2016 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

A variable structure fuzzy neural network model of squamous dysplasia and esophageal squamous cell carcinoma based on a global chaotic optimization algorithm.

PubMed

Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Malekzadeh, Reza

2013-02-07

Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error. Copyright © 2012 Elsevier Ltd. All rights reserved.

Advances in the assessment and prediction of interpersonal violence.

PubMed

Mills, Jeremy F

2005-02-01

This article underscores the weakness of clinical judgment as a mechanism for prediction with examples from other areas in the psychological literature. Clinical judgment has as its Achilles'heel the reliance on a person to incorporate multiple pieces of information while overcoming human judgment errors--a feat insurmountable thus far. The actuarial approach to risk assessment has overcome many of the weaknesses of clinical judgment and has been shown to be a much superior method. Nonetheless, the static/historical nature of the risk factors associated with most actuarial approaches is limiting. Advances in risk prediction will be found in part in the development of dynamic actuarial instruments that will measure both static/historical and changeable risk factors. The dynamic risk factors can be reevaluated on an ongoing basis, and it is proposed that the level of change in dynamic factors necessary to represent a significant change in overall risk will be an interactive function with static risk factors.

Error-related brain activity predicts cocaine use after treatment at 3-month follow-up.

PubMed

Marhe, Reshmi; van de Wetering, Ben J M; Franken, Ingmar H A

2013-04-15

Relapse after treatment is one of the most important problems in drug dependency. Several studies suggest that lack of cognitive control is one of the causes of relapse. In this study, a relative new electrophysiologic index of cognitive control, the error-related negativity, is investigated to examine its suitability as a predictor of relapse. The error-related negativity was measured in 57 cocaine-dependent patients during their first week in detoxification treatment. Data from 49 participants were used to predict cocaine use at 3-month follow-up. Cocaine use at follow-up was measured by means of self-reported days of cocaine use in the last month verified by urine screening. A multiple hierarchical regression model was used to examine the predictive value of the error-related negativity while controlling for addiction severity and self-reported craving in the week before treatment. The error-related negativity was the only significant predictor in the model and added 7.4% of explained variance to the control variables, resulting in a total of 33.4% explained variance in the prediction of days of cocaine use at follow-up. A reduced error-related negativity measured during the first week of treatment was associated with more days of cocaine use at 3-month follow-up. Moreover, the error-related negativity was a stronger predictor of recent cocaine use than addiction severity and craving. These results suggest that underactive error-related brain activity might help to identify patients who are at risk of relapse as early as in the first week of detoxification treatment. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

PubMed

Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

2011-06-30

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

A feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalization

NASA Astrophysics Data System (ADS)

Harudin, N.; Jamaludin, K. R.; Muhtazaruddin, M. Nabil; Ramlie, F.; Muhamad, Wan Zuki Azman Wan

2018-03-01

T-Method is one of the techniques governed under Mahalanobis Taguchi System that developed specifically for multivariate data predictions. Prediction using T-Method is always possible even with very limited sample size. The user of T-Method required to clearly understanding the population data trend since this method is not considering the effect of outliers within it. Outliers may cause apparent non-normality and the entire classical methods breakdown. There exist robust parameter estimate that provide satisfactory results when the data contain outliers, as well as when the data are free of them. The robust parameter estimates of location and scale measure called Shamos Bickel (SB) and Hodges Lehman (HL) which are used as a comparable method to calculate the mean and standard deviation of classical statistic is part of it. Embedding these into T-Method normalize stage feasibly help in enhancing the accuracy of the T-Method as well as analysing the robustness of T-method itself. However, the result of higher sample size case study shows that T-method is having lowest average error percentages (3.09%) on data with extreme outliers. HL and SB is having lowest error percentages (4.67%) for data without extreme outliers with minimum error differences compared to T-Method. The error percentages prediction trend is vice versa for lower sample size case study. The result shows that with minimum sample size, which outliers always be at low risk, T-Method is much better on that, while higher sample size with extreme outliers, T-Method as well show better prediction compared to others. For the case studies conducted in this research, it shows that normalization of T-Method is showing satisfactory results and it is not feasible to adapt HL and SB or normal mean and standard deviation into it since it’s only provide minimum effect of percentages errors. Normalization using T-method is still considered having lower risk towards outlier’s effect.

Prediction of BP reactivity to talking using hybrid soft computing approaches.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2014-01-01

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Predicting forest insect flight activity: A Bayesian network approach

PubMed Central

Pawson, Stephen M.; Marcot, Bruce G.; Woodberry, Owen G.

2017-01-01

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model’s predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways. PMID:28953904

In Your Face: Risk of Punishment Enhances Cognitive Control and Error-Related Activity in the Corrugator Supercilii Muscle.

PubMed

Lindström, Björn R; Mattsson-Mårn, Isak Berglund; Golkar, Armita; Olsson, Andreas

2013-01-01

Cognitive control is needed when mistakes have consequences, especially when such consequences are potentially harmful. However, little is known about how the aversive consequences of deficient control affect behavior. To address this issue, participants performed a two-choice response time task where error commissions were expected to be punished by electric shocks during certain blocks. By manipulating (1) the perceived punishment risk (no, low, high) associated with error commissions, and (2) response conflict (low, high), we showed that motivation to avoid punishment enhanced performance during high response conflict. As a novel index of the processes enabling successful cognitive control under threat, we explored electromyographic activity in the corrugator supercilii (cEMG) muscle of the upper face. The corrugator supercilii is partially controlled by the anterior midcingulate cortex (aMCC) which is sensitive to negative affect, pain and cognitive control. As hypothesized, the cEMG exhibited several key similarities with the core temporal and functional characteristics of the Error-Related Negativity (ERN) ERP component, the hallmark index of cognitive control elicited by performance errors, and which has been linked to the aMCC. The cEMG was amplified within 100 ms of error commissions (the same time-window as the ERN), particularly during the high punishment risk condition where errors would be most aversive. Furthermore, similar to the ERN, the magnitude of error cEMG predicted post-error response time slowing. Our results suggest that cEMG activity can serve as an index of avoidance motivated control, which is instrumental to adaptive cognitive control when consequences are potentially harmful.

In Your Face: Risk of Punishment Enhances Cognitive Control and Error-Related Activity in the Corrugator Supercilii Muscle

PubMed Central

Lindström, Björn R.; Mattsson-Mårn, Isak Berglund; Golkar, Armita; Olsson, Andreas

2013-01-01

Cognitive control is needed when mistakes have consequences, especially when such consequences are potentially harmful. However, little is known about how the aversive consequences of deficient control affect behavior. To address this issue, participants performed a two-choice response time task where error commissions were expected to be punished by electric shocks during certain blocks. By manipulating (1) the perceived punishment risk (no, low, high) associated with error commissions, and (2) response conflict (low, high), we showed that motivation to avoid punishment enhanced performance during high response conflict. As a novel index of the processes enabling successful cognitive control under threat, we explored electromyographic activity in the corrugator supercilii (cEMG) muscle of the upper face. The corrugator supercilii is partially controlled by the anterior midcingulate cortex (aMCC) which is sensitive to negative affect, pain and cognitive control. As hypothesized, the cEMG exhibited several key similarities with the core temporal and functional characteristics of the Error-Related Negativity (ERN) ERP component, the hallmark index of cognitive control elicited by performance errors, and which has been linked to the aMCC. The cEMG was amplified within 100 ms of error commissions (the same time-window as the ERN), particularly during the high punishment risk condition where errors would be most aversive. Furthermore, similar to the ERN, the magnitude of error cEMG predicted post-error response time slowing. Our results suggest that cEMG activity can serve as an index of avoidance motivated control, which is instrumental to adaptive cognitive control when consequences are potentially harmful. PMID:23840356

Phasic dopamine signals: from subjective reward value to formal economic utility

PubMed Central

Schultz, Wolfram; Carelli, Regina M; Wightman, R Mark

2015-01-01

Although rewards are physical stimuli and objects, their value for survival and reproduction is subjective. The phasic, neurophysiological and voltammetric dopamine reward prediction error response signals subjective reward value. The signal incorporates crucial reward aspects such as amount, probability, type, risk, delay and effort. Differences of dopamine release dynamics with temporal delay and effort in rodents may derive from methodological issues and require further study. Recent designs using concepts and behavioral tools from experimental economics allow to formally characterize the subjective value signal as economic utility and thus to establish a neuronal value function. With these properties, the dopamine response constitutes a utility prediction error signal. PMID:26719853

The Impact of Truth Surrogate Variance on Quality Assessment/Assurance in Wind Tunnel Testing

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2016-01-01

Minimum data volume requirements for wind tunnel testing are reviewed and shown to depend on error tolerance, response model complexity, random error variance in the measurement environment, and maximum acceptable levels of inference error risk. Distinctions are made between such related concepts as quality assurance and quality assessment in response surface modeling, as well as between precision and accuracy. Earlier research on the scaling of wind tunnel tests is extended to account for variance in the truth surrogates used at confirmation sites in the design space to validate proposed response models. A model adequacy metric is presented that represents the fraction of the design space within which model predictions can be expected to satisfy prescribed quality specifications. The impact of inference error on the assessment of response model residuals is reviewed. The number of sites where reasonably well-fitted response models actually predict inadequately is shown to be considerably less than the number of sites where residuals are out of tolerance. The significance of such inference error effects on common response model assessment strategies is examined.

Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greitzer, Frank L.; Frincke, Deborah A.

2010-09-01

The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data tomore » yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.« less

Prediction of fat-free body mass from bioelectrical impedance and anthropometry among 3-year-old children using DXA

PubMed Central

Ejlerskov, Katrine T.; Jensen, Signe M.; Christensen, Line B.; Ritz, Christian; Michaelsen, Kim F.; Mølgaard, Christian

2014-01-01

For 3-year-old children suitable methods to estimate body composition are sparse. We aimed to develop predictive equations for estimating fat-free mass (FFM) from bioelectrical impedance (BIA) and anthropometry using dual-energy X-ray absorptiometry (DXA) as reference method using data from 99 healthy 3-year-old Danish children. Predictive equations were derived from two multiple linear regression models, a comprehensive model (height2/resistance (RI), six anthropometric measurements) and a simple model (RI, height, weight). Their uncertainty was quantified by means of 10-fold cross-validation approach. Prediction error of FFM was 3.0% for both equations (root mean square error: 360 and 356 g, respectively). The derived equations produced BIA-based prediction of FFM and FM near DXA scan results. We suggest that the predictive equations can be applied in similar population samples aged 2–4 years. The derived equations may prove useful for studies linking body composition to early risk factors and early onset of obesity. PMID:24463487

Prediction of fat-free body mass from bioelectrical impedance and anthropometry among 3-year-old children using DXA.

PubMed

Ejlerskov, Katrine T; Jensen, Signe M; Christensen, Line B; Ritz, Christian; Michaelsen, Kim F; Mølgaard, Christian

2014-01-27

For 3-year-old children suitable methods to estimate body composition are sparse. We aimed to develop predictive equations for estimating fat-free mass (FFM) from bioelectrical impedance (BIA) and anthropometry using dual-energy X-ray absorptiometry (DXA) as reference method using data from 99 healthy 3-year-old Danish children. Predictive equations were derived from two multiple linear regression models, a comprehensive model (height(2)/resistance (RI), six anthropometric measurements) and a simple model (RI, height, weight). Their uncertainty was quantified by means of 10-fold cross-validation approach. Prediction error of FFM was 3.0% for both equations (root mean square error: 360 and 356 g, respectively). The derived equations produced BIA-based prediction of FFM and FM near DXA scan results. We suggest that the predictive equations can be applied in similar population samples aged 2-4 years. The derived equations may prove useful for studies linking body composition to early risk factors and early onset of obesity.

Mortality determinants and prediction of outcome in high risk newborns.

PubMed

Dalvi, R; Dalvi, B V; Birewar, N; Chari, G; Fernandez, A R

1990-06-01

The aim of this study was to determine independent patient-related predictors of mortality in high risk newborns admitted at our centre. The study population comprised 100 consecutive newborns each, from the premature unit (PU) and sick baby care unit (SBCU), respectively. Thirteen high risk factors (variables) for each of the two units, were entered into a multivariate regression analysis. Variables with independent predictive value for poor outcome (i.e., death) in PU were, weight less than 1 kg, hyaline membrane disease, neurologic problems, and intravenous therapy. High risk factors in SBCU included, blood gas abnormality, bleeding phenomena, recurrent convulsions, apnea, and congenital anomalies. Identification of these factors guided us in defining priority areas for improvement in our system of neonatal care. Also, based on these variables a simple predictive score for outcome was constructed. The prediction equation and the score were cross-validated by applying them to a 'test-set' of 100 newborns each for PU and SBCU. Results showed a comparable sensitivity, specificity and error rate.

Crash risk and aberrant driving behaviors among bus drivers: the role of personality and attitudes towards traffic safety.

PubMed

Mallia, Luca; Lazuras, Lambros; Violani, Cristiano; Lucidi, Fabio

2015-06-01

Several studies have shown that personality traits and attitudes toward traffic safety predict aberrant driving behaviors and crash involvement. However, this process has not been adequately investigated in professional drivers, such as bus drivers. The present study used a personality-attitudes model to assess whether personality traits predicted aberrant self-reported driving behaviors (driving violations, lapses, and errors) both directly and indirectly, through the effects of attitudes towards traffic safety in a large sample of bus drivers. Additionally, the relationship between aberrant self-reported driving behaviors and crash risk was also assessed. Three hundred and one bus drivers (mean age=39.1, SD=10.7 years) completed a structured and anonymous questionnaire measuring personality traits, attitudes toward traffic safety, self-reported aberrant driving behaviors (i.e., errors, lapses, and traffic violations), and accident risk in the last 12 months. Structural equation modeling analysis revealed that personality traits were associated to aberrant driving behaviors both directly and indirectly. In particular altruism, excitement seeking, and normlessness directly predicted bus drivers' attitudes toward traffic safety which, in turn, were negatively associated with the three types of self-reported aberrant driving behaviors. Personality traits relevant to emotionality directly predicted bus drivers' aberrant driving behaviors, without any mediation of attitudes. Finally, only self-reported violations were related to bus drivers' accident risk. The present findings suggest that the hypothesized personality-attitudes model accounts for aberrant driving behaviors in bus drivers, and provide the empirical basis for evidence-based road safety interventions in the context of public transport. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cost Risk Analysis Based on Perception of the Engineering Process

NASA Technical Reports Server (NTRS)

Dean, Edwin B.; Wood, Darrell A.; Moore, Arlene A.; Bogart, Edward H.

1986-01-01

In most cost estimating applications at the NASA Langley Research Center (LaRC), it is desirable to present predicted cost as a range of possible costs rather than a single predicted cost. A cost risk analysis generates a range of cost for a project and assigns a probability level to each cost value in the range. Constructing a cost risk curve requires a good estimate of the expected cost of a project. It must also include a good estimate of expected variance of the cost. Many cost risk analyses are based upon an expert's knowledge of the cost of similar projects in the past. In a common scenario, a manager or engineer, asked to estimate the cost of a project in his area of expertise, will gather historical cost data from a similar completed project. The cost of the completed project is adjusted using the perceived technical and economic differences between the two projects. This allows errors from at least three sources. The historical cost data may be in error by some unknown amount. The managers' evaluation of the new project and its similarity to the old project may be in error. The factors used to adjust the cost of the old project may not correctly reflect the differences. Some risk analyses are based on untested hypotheses about the form of the statistical distribution that underlies the distribution of possible cost. The usual problem is not just to come up with an estimate of the cost of a project, but to predict the range of values into which the cost may fall and with what level of confidence the prediction is made. Risk analysis techniques that assume the shape of the underlying cost distribution and derive the risk curve from a single estimate plus and minus some amount usually fail to take into account the actual magnitude of the uncertainty in cost due to technical factors in the project itself. This paper addresses a cost risk method that is based on parametric estimates of the technical factors involved in the project being costed. The engineering process parameters are elicited from the engineer/expert on the project and are based on that expert's technical knowledge. These are converted by a parametric cost model into a cost estimate. The method discussed makes no assumptions about the distribution underlying the distribution of possible costs, and is not tied to the analysis of previous projects, except through the expert calibrations performed by the parametric cost analyst.

Personality domains and traits that predict self-reported aberrant driving behaviours in a southeastern US university sample.

PubMed

Beanland, Vanessa; Sellbom, Martin; Johnson, Alexandria K

2014-11-01

Personality traits are meaningful predictors of many significant life outcomes, including mortality. Several studies have investigated the relationship between specific personality traits and driving behaviours, e.g., aggression and speeding, in an attempt to identify traits associated with elevated crash risk. These studies, while valuable, are limited in that they examine only a narrow range of personality constructs and thus do not necessarily reveal which traits in constellation best predict aberrant driving behaviours. The primary aim of this study was to use a comprehensive measure of personality to investigate which personality traits are most predictive of four types of aberrant driving behaviour (Aggressive Violations, Ordinary Violations, Errors, Lapses) as indicated by the Manchester Driver Behaviour Questionnaire (DBQ). We recruited 285 young adults (67% female) from a university in the southeastern US. They completed self-report questionnaires including the DBQ and the Personality Inventory for DSM-5, which indexes 5 broad personality domains (Antagonism, Detachment, Disinhibition, Negative Affectivity, Psychoticism) and 25 specific trait facets. Confirmatory factor analysis showed adequate evidence for the DBQ internal structure. Structural regression analyses revealed that the personality domains of Antagonism and Negative Affectivity best predicted both Aggressive Violations and Ordinary Violations, whereas the best predictors of both Errors and Lapses were Negative Affectivity, Disinhibition and to a lesser extent Antagonism. A more nuanced analysis of trait facets revealed that Hostility was the best predictor of Aggressive Violations; Risk-taking and Hostility of Ordinary Violations; Irresponsibility, Separation Insecurity and Attention Seeking of Errors; and Perseveration and Irresponsibility of Lapses. Copyright © 2014 Elsevier Ltd. All rights reserved.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

A Risk Score Model for Evaluation and Management of Patients with Thyroid Nodules.

PubMed

Zhang, Yongwen; Meng, Fanrong; Hong, Lianqing; Chu, Lanfang

2018-06-12

The study is aimed to establish a simplified and practical tool for analyzing thyroid nodules. A novel risk score model was designed, risk factors including patient history, patient characteristics, physical examination, symptoms of compression, thyroid function, ultrasonography (US) of thyroid and cervical lymph nodes were evaluated and classified into high risk factors, intermediate risk factors, and low risk factors. A total of 243 thyroid nodules in 162 patients were assessed with risk score system and Thyroid Imaging-Reporting and Data System (TI-RADS). The diagnostic performance of risk score system and TI-RADS was compared. The accuracy in the diagnosis of thyroid nodules was 89.3% for risk score system, 74.9% for TI-RADS respectively. The specificity, accuracy and positive predictive value (PPV) of risk score system were significantly higher than the TI-RADS system (χ 2 =26.287, 17.151, 11.983; p <0.05), statistically significant differences were not observed in the sensitivity and negative predictive value (NPV) between the risk score system and TI-RADS (χ 2 =1.276, 0.290; p>0.05). The area under the curve (AUC) for risk score diagnosis system was 0.963, standard error 0.014, 95% confidence interval (CI)=0.934-0.991, the AUC for TI-RADS diagnosis system was 0.912 with standard error 0.021, 95% CI=0.871-0.953, the AUC for risk score system was significantly different from that of TI-RADS (Z=2.02; p <0.05). Risk score model is a reliable, simplified and cost-effective diagnostic tool used in diagnosis of thyroid cancer. The higher the score is, the higher the risk of malignancy will be. © Georg Thieme Verlag KG Stuttgart · New York.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE PAGES

Wang, Yan; Swiler, Laura

2017-09-07

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yan; Swiler, Laura

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Gene Expression Profiling Predicts the Development of Oral Cancer

PubMed Central

Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

2011-01-01

Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

Intelligent Planning for Laser Refractive Surgeries

NASA Astrophysics Data System (ADS)

Wang, Wei; Yue, Yong; Elsheikh, Ahmed; Bao, Fangjun

2018-02-01

Refractive error is one of leading ophthalmic diseases for both genders all over the world. Laser refractive correction surgery, e.g., laser in-situ keratomileusis (LASIK), has been commonly used worldwide. The prediction of surgical parameters, e.g., corneal ablation depth, depends on the doctor’s experience, theoretical formula and surgery reference manual in the preoperative diagnosis. The error of prediction may present a potential surgical risk and complication. Being aware of the surgery parameters is important because these can be used to estimate a patient’s post-operative visual quality and help the surgeon plan a suitable treatment. Therefore, in this paper we discuss data mining techniques that can be utilized for the prediction of laser refractive correction surgery parameters. It can provide the surgeon with a reference for possible surgical parameters and outcomes of the patient before the laser refractive correction surgery.

Wisconsin Card Sorting Test performance and impulsivity in patients with temporal lobe epilepsy: suicidal risk and suicide attempts.

PubMed

Garcia Espinosa, Arlety; Andrade Machado, René; Borges González, Susana; García González, María Eugenia; Pérez Montoto, Ariadna; Toledo Sotomayor, Guillermo

2010-01-01

The goal of the study described here was to determine if executive dysfunction and impulsivity are related to risk for suicide and suicide attempts in patients with temporal lobe epilepsy. Forty-two patients with temporal lobe epilepsy were recruited. A detailed medical history, neurological examination, serial EEGs, Mini-International Neuropsychiatric Interview, executive function, and MRI were assessed. Multiple regression analysis was carried out to examine predictive associations between clinical variables and Wisconsin Card Sorting Test measures. Patients' scores on the Risk for Suicide Scale (n=24) were greater than 7, which means they had the highest relative risk for suicide attempts. Family history of psychiatric disease, current major depressive episode, left temporal lobe epilepsy, and perseverative responses and total errors on the Wisconsin Card Sorting Test increased by 6.3 and 7.5 suicide risk and suicide attempts, respectively. Executive dysfunction (specifically perseverative responses and more total errors) contributed greatly to suicide risk. Executive performance has a major impact on suicide risk and suicide attempts in patients with temporal lobe epilepsy. 2009 Elsevier Inc. All rights reserved.

Dopamine reward prediction error coding.

PubMed

Schultz, Wolfram

2016-03-01

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

Dopamine reward prediction error coding

PubMed Central

Schultz, Wolfram

2016-01-01

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards—an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware. PMID:27069377

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PubMed Central

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-01-01

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

PubMed

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-09-29

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Predicting Space Weather Effects on Close Approach Events

NASA Technical Reports Server (NTRS)

Hejduk, Matthew D.; Newman, Lauri K.; Besser, Rebecca L.; Pachura, Daniel A.

2015-01-01

The NASA Robotic Conjunction Assessment Risk Analysis (CARA) team sends ephemeris data to the Joint Space Operations Center (JSpOC) for conjunction assessment screening against the JSpOC high accuracy catalog and then assesses risk posed to protected assets from predicted close approaches. Since most spacecraft supported by the CARA team are located in LEO orbits, atmospheric drag is the primary source of state estimate uncertainty. Drag magnitude and uncertainty is directly governed by atmospheric density and thus space weather. At present the actual effect of space weather on atmospheric density cannot be accurately predicted because most atmospheric density models are empirical in nature, which do not perform well in prediction. The Jacchia-Bowman-HASDM 2009 (JBH09) atmospheric density model used at the JSpOC employs a solar storm active compensation feature that predicts storm sizes and arrival times and thus the resulting neutral density alterations. With this feature, estimation errors can occur in either direction (i.e., over- or under-estimation of density and thus drag). Although the exact effect of a solar storm on atmospheric drag cannot be determined, one can explore the effects of JBH09 model error on conjuncting objects' trajectories to determine if a conjunction is likely to become riskier, less risky, or pass unaffected. The CARA team has constructed a Space Weather Trade-Space tool that systematically alters the drag situation for the conjuncting objects and recalculates the probability of collision for each case to determine the range of possible effects on the collision risk. In addition to a review of the theory and the particulars of the tool, the different types of observed output will be explained, along with statistics of their frequency.

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.

PubMed

Li, Hui; Zhu, Yitan; Burnside, Elizabeth S; Drukker, Karen; Hoadley, Katherine A; Fan, Cheng; Conzen, Suzanne D; Whitman, Gary J; Sutton, Elizabeth J; Net, Jose M; Ganott, Marie; Huang, Erich; Morris, Elizabeth A; Perou, Charles M; Ji, Yuan; Giger, Maryellen L

2016-11-01

Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R 2 = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. © RSNA, 2016 Online supplemental material is available for this article.

Altered neural encoding of prediction errors in assault-related posttraumatic stress disorder.

PubMed

Ross, Marisa C; Lenow, Jennifer K; Kilts, Clinton D; Cisler, Josh M

2018-05-12

Posttraumatic stress disorder (PTSD) is widely associated with deficits in extinguishing learned fear responses, which relies on mechanisms of reinforcement learning (e.g., updating expectations based on prediction errors). However, the degree to which PTSD is associated with impairments in general reinforcement learning (i.e., outside of the context of fear stimuli) remains poorly understood. Here, we investigate brain and behavioral differences in general reinforcement learning between adult women with and without a current diagnosis of PTSD. 29 adult females (15 PTSD with exposure to assaultive violence, 14 controls) underwent a neutral reinforcement-learning task (i.e., two arm bandit task) during fMRI. We modeled participant behavior using different adaptations of the Rescorla-Wagner (RW) model and used Independent Component Analysis to identify timecourses for large-scale a priori brain networks. We found that an anticorrelated and risk sensitive RW model best fit participant behavior, with no differences in computational parameters between groups. Women in the PTSD group demonstrated significantly less neural encoding of prediction errors in both a ventral striatum/mPFC and anterior insula network compared to healthy controls. Weakened encoding of prediction errors in the ventral striatum/mPFC and anterior insula during a general reinforcement learning task, outside of the context of fear stimuli, suggests the possibility of a broader conceptualization of learning differences in PTSD than currently proposed in current neurocircuitry models of PTSD. Copyright © 2018 Elsevier Ltd. All rights reserved.

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

PubMed

Zhao, Sihai Dave

2017-06-01

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa. © 2016, The International Biometric Society.

Effects of exposure estimation errors on estimated exposure-response relations for PM2.5.

PubMed

Cox, Louis Anthony Tony

2018-07-01

Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes, from autism and auto theft to elderly mortality, suicide, and violent crime, have been widely reported. Influential articles have argued that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes. Yet, other studies have found that reducing black smoke and other particulate matter by as much as 70% and dozens of micrograms per cubic meter has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them. This paper examines whether this disconnect between association and causation might be explained in part by ignored estimation errors in estimated exposure concentrations. We use EPA air quality monitor data from the Los Angeles area of California to examine the shapes of estimated C-R functions for PM2.5 when the true C-R functions are assumed to be step functions with well-defined response thresholds. The estimated C-R functions mistakenly show risk as smoothly increasing with concentrations even well below the response thresholds, thus incorrectly predicting substantial risk reductions from reductions in concentrations that do not affect health risks. We conclude that ignored estimation errors obscure the shapes of true C-R functions, including possible thresholds, possibly leading to unrealistic predictions of the changes in risk caused by changing exposures. Instead of estimating improvements in public health per unit reduction (e.g., per 10 µg/m 3 decrease) in average PM2.5 concentrations, it may be essential to consider how interventions change the distributions of exposure concentrations. Copyright © 2018 Elsevier Inc. All rights reserved.

Hydrologic Design in the Anthropocene

NASA Astrophysics Data System (ADS)

Vogel, R. M.; Farmer, W. H.; Read, L.

2014-12-01

In an era dubbed the Anthropocene, the natural world is being transformed by a myriad of human influences. As anthropogenic impacts permeate hydrologic systems, hydrologists are challenged to fully account for such changes and develop new methods of hydrologic design. Deterministic watershed models (DWM), which can account for the impacts of changes in land use, climate and infrastructure, are becoming increasing popular for the design of flood and/or drought protection measures. As with all models that are calibrated to existing datasets, DWMs are subject to model error or uncertainty. In practice, the model error component of DWM predictions is typically ignored yet DWM simulations which ignore model error produce model output which cannot reproduce the statistical properties of the observations they are intended to replicate. In the context of hydrologic design, we demonstrate how ignoring model error can lead to systematic downward bias in flood quantiles, upward bias in drought quantiles and upward bias in water supply yields. By reincorporating model error, we document how DWM models can be used to generate results that mimic actual observations and preserve their statistical behavior. In addition to use of DWM for improved predictions in a changing world, improved communication of the risk and reliability is also needed. Traditional statements of risk and reliability in hydrologic design have been characterized by return periods, but such statements often assume that the annual probability of experiencing a design event remains constant throughout the project horizon. We document the general impact of nonstationarity on the average return period and reliability in the context of hydrologic design. Our analyses reveal that return periods do not provide meaningful expressions of the likelihood of future hydrologic events. Instead, knowledge of system reliability over future planning horizons can more effectively prepare society and communicate the likelihood of future hydrologic events of interest.

Predicting risk of trace element pollution from municipal roads using site-specific soil samples and remotely sensed data.

PubMed

Reeves, Mari Kathryn; Perdue, Margaret; Munk, Lee Ann; Hagedorn, Birgit

2018-07-15

Studies of environmental processes exhibit spatial variation within data sets. The ability to derive predictions of risk from field data is a critical path forward in understanding the data and applying the information to land and resource management. Thanks to recent advances in predictive modeling, open source software, and computing, the power to do this is within grasp. This article provides an example of how we predicted relative trace element pollution risk from roads across a region by combining site specific trace element data in soils with regional land cover and planning information in a predictive model framework. In the Kenai Peninsula of Alaska, we sampled 36 sites (191 soil samples) adjacent to roads for trace elements. We then combined this site specific data with freely-available land cover and urban planning data to derive a predictive model of landscape scale environmental risk. We used six different model algorithms to analyze the dataset, comparing these in terms of their predictive abilities and the variables identified as important. Based on comparable predictive abilities (mean R 2 from 30 to 35% and mean root mean square error from 65 to 68%), we averaged all six model outputs to predict relative levels of trace element deposition in soils-given the road surface, traffic volume, sample distance from the road, land cover category, and impervious surface percentage. Mapped predictions of environmental risk from toxic trace element pollution can show land managers and transportation planners where to prioritize road renewal or maintenance by each road segment's relative environmental and human health risk. Published by Elsevier B.V.

Computational substrates of norms and their violations during social exchange.

PubMed

Xiang, Ting; Lohrenz, Terry; Montague, P Read

2013-01-16

Social norms in humans constrain individual behaviors to establish shared expectations within a social group. Previous work has probed social norm violations and the feelings that such violations engender; however, a computational rendering of the underlying neural and emotional responses has been lacking. We probed norm violations using a two-party, repeated fairness game (ultimatum game) where proposers offer a split of a monetary resource to a responder who either accepts or rejects the offer. Using a norm-training paradigm where subject groups are preadapted to either high or low offers, we demonstrate that unpredictable shifts in expected offers creates a difference in rejection rates exhibited by the two responder groups for otherwise identical offers. We constructed an ideal observer model that identified neural correlates of norm prediction errors in the ventral striatum and anterior insula, regions that also showed strong responses to variance-prediction errors generated by the same model. Subjective feelings about offers correlated with these norm prediction errors, and the two signals displayed overlapping, but not identical, neural correlates in striatum, insula, and medial orbitofrontal cortex. These results provide evidence for the hypothesis that responses in anterior insula can encode information about social norm violations that correlate with changes in overt behavior (changes in rejection rates). Together, these results demonstrate that the brain regions involved in reward prediction and risk prediction are also recruited in signaling social norm violations.

Computational Substrates of Norms and Their Violations during Social Exchange

PubMed Central

Xiang, Ting; Lohrenz, Terry; Montague, P. Read

2013-01-01

Social norms in humans constrain individual behaviors to establish shared expectations within a social group. Previous work has probed social norm violations and the feelings that such violations engender; however, a computational rendering of the underlying neural and emotional responses has been lacking. We probed norm violations using a two-party, repeated fairness game (ultimatum game) where proposers offer a split of a monetary resource to a responder who either accepts or rejects the offer. Using a norm-training paradigm where subject groups are preadapted to either high or low offers, we demonstrate that unpredictable shifts in expected offers creates a difference in rejection rates exhibited by the two responder groups for otherwise identical offers. We constructed an ideal observer model that identified neural correlates of norm prediction errors in the ventral striatum and anterior insula, regions that also showed strong responses to variance-prediction errors generated by the same model. Subjective feelings about offers correlated with these norm prediction errors, and the two signals displayed overlapping, but not identical, neural correlates in striatum, insula, and medial orbitofrontal cortex. These results provide evidence for the hypothesis that responses in anterior insula can encode information about social norm violations that correlate with changes in overt behavior (changes in rejection rates). Together, these results demonstrate that the brain regions involved in reward prediction and risk prediction are also recruited in signaling social norm violations. PMID:23325247

Case-Mix for Performance Management: A Risk Algorithm Based on ICD-10-CM.

PubMed

Gao, Jian; Moran, Eileen; Almenoff, Peter L

2018-06-01

Accurate risk adjustment is the key to a reliable comparison of cost and quality performance among providers and hospitals. However, the existing case-mix algorithms based on age, sex, and diagnoses can only explain up to 50% of the cost variation. More accurate risk adjustment is desired for provider performance assessment and improvement. To develop a case-mix algorithm that hospitals and payers can use to measure and compare cost and quality performance of their providers. All 6,048,895 patients with valid diagnoses and cost recorded in the US Veterans health care system in fiscal year 2016 were included in this study. The dependent variable was total cost at the patient level, and the explanatory variables were age, sex, and comorbidities represented by 762 clinically homogeneous groups, which were created by expanding the 283 categories from Clinical Classifications Software based on ICD-10-CM codes. The split-sample method was used to assess model overfitting and coefficient stability. The predictive power of the algorithms was ascertained by comparing the R, mean absolute percentage error, root mean square error, predictive ratios, and c-statistics. The expansion of the Clinical Classifications Software categories resulted in higher predictive power. The R reached 0.72 and 0.52 for the transformed and raw scale cost, respectively. The case-mix algorithm we developed based on age, sex, and diagnoses outperformed the existing case-mix models reported in the literature. The method developed in this study can be used by other health systems to produce tailored risk models for their specific purpose.

A Tuned Single Parameter for Representing Conjunction Risk

NASA Technical Reports Server (NTRS)

Plakaloic, D.; Hejduk, M. D.; Frigm, R. C.; Newman, L. K.

2011-01-01

Satellite conjunction assessment risk analysis is a subjective enterprise that can benefit from quantitative aids and, to this end, NASA/GSFC has developed a fuzzy logic construct - called the F-value - to attempt to provide a statement of conjunction risk that amalgamates multiple indices and yields a more stable intra-event assessment. This construct has now sustained an extended tuning procedure against heuristic analyst assessment of event risk. The tuning effort has resulted in modifications to the calculation procedure and the adjustment of tuning coefficients, producing a construct with both more predictive force and a better statement of its error.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.

PubMed

Emura, Takeshi; Nakatochi, Masahiro; Matsui, Shigeyuki; Michimae, Hirofumi; Rondeau, Virginie

2017-01-01

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

Development of Physics-Based Hurricane Wave Response Functions: Application to Selected Sites on the U.S. Gulf Coast

NASA Astrophysics Data System (ADS)

McLaughlin, P. W.; Kaihatu, J. M.; Irish, J. L.; Taylor, N. R.; Slinn, D.

2013-12-01

Recent hurricane activity in the Gulf of Mexico has led to a need for accurate, computationally efficient prediction of hurricane damage so that communities can better assess risk of local socio-economic disruption. This study focuses on developing robust, physics based non-dimensional equations that accurately predict maximum significant wave height at different locations near a given hurricane track. These equations (denoted as Wave Response Functions, or WRFs) were developed from presumed physical dependencies between wave heights and hurricane characteristics and fit with data from numerical models of waves and surge under hurricane conditions. After curve fitting, constraints which correct for fully developed sea state were used to limit the wind wave growth. When applied to the region near Gulfport, MS, back prediction of maximum significant wave height yielded root mean square errors between 0.22-0.42 (m) at open coast stations and 0.07-0.30 (m) at bay stations when compared to the numerical model data. The WRF method was also applied to Corpus Christi, TX and Panama City, FL with similar results. Back prediction errors will be included in uncertainty evaluations connected to risk calculations using joint probability methods. These methods require thousands of simulations to quantify extreme value statistics, thus requiring the use of reduced methods such as the WRF to represent the relevant physical processes.

An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images

NASA Astrophysics Data System (ADS)

Kierkels, R. G. J.; den Otter, L. A.; Korevaar, E. W.; Langendijk, J. A.; van der Schaaf, A.; Knopf, A. C.; Sijtsema, N. M.

2018-02-01

A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r  ⩾  0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.

Combining inferences from models of capture efficiency, detectability, and suitable habitat to classify landscapes for conservation of threatened bull trout

USGS Publications Warehouse

Peterson, J.; Dunham, J.B.

2003-01-01

Effective conservation efforts for at-risk species require knowledge of the locations of existing populations. Species presence can be estimated directly by conducting field-sampling surveys or alternatively by developing predictive models. Direct surveys can be expensive and inefficient, particularly for rare and difficult-to-sample species, and models of species presence may produce biased predictions. We present a Bayesian approach that combines sampling and model-based inferences for estimating species presence. The accuracy and cost-effectiveness of this approach were compared to those of sampling surveys and predictive models for estimating the presence of the threatened bull trout ( Salvelinus confluentus ) via simulation with existing models and empirical sampling data. Simulations indicated that a sampling-only approach would be the most effective and would result in the lowest presence and absence misclassification error rates for three thresholds of detection probability. When sampling effort was considered, however, the combined approach resulted in the lowest error rates per unit of sampling effort. Hence, lower probability-of-detection thresholds can be specified with the combined approach, resulting in lower misclassification error rates and improved cost-effectiveness.

Evaluating concentration estimation errors in ELISA microarray experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Don S.; White, Amanda M.; Varnum, Susan M.

Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Althoughmore » propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.« less

Correction to Hilton et al. (2004)

ERIC Educational Resources Information Center

Hilton, N. Zoe; Harris, Grant T.; Rice, Marnie E.; Lang, Carol; Cormier, Catherine A.; Lines, Kathryn J.

2005-01-01

This paper reports errors in the article "A Brief Actuarial Assessment for the Prediction of Wife Assault Recidivism: The Ontario Domestic Assault Risk Assessment," by N. Zoe Hilton, Grant T. Harris, Marnie E. Rice, Carol Lang, Catherine A. Cormier, and Kathryn J. Lines (Psychological Assessment, 2004, Vol. 16, No. 3, pp. 267-275). On page 272,…

Performance of Reclassification Statistics in Comparing Risk Prediction Models

PubMed Central

Paynter, Nina P.

2012-01-01

Concerns have been raised about the use of traditional measures of model fit in evaluating risk prediction models for clinical use, and reclassification tables have been suggested as an alternative means of assessing the clinical utility of a model. Several measures based on the table have been proposed, including the reclassification calibration (RC) statistic, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI), but the performance of these in practical settings has not been fully examined. We used simulations to estimate the type I error and power for these statistics in a number of scenarios, as well as the impact of the number and type of categories, when adding a new marker to an established or reference model. The type I error was found to be reasonable in most settings, and power was highest for the IDI, which was similar to the test of association. The relative power of the RC statistic, a test of calibration, and the NRI, a test of discrimination, varied depending on the model assumptions. These tools provide unique but complementary information. PMID:21294152

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Relationships of Measurement Error and Prediction Error in Observed-Score Regression

ERIC Educational Resources Information Center

Moses, Tim

2012-01-01

The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor…

The role of model errors represented by nonlinear forcing singular vector tendency error in causing the "spring predictability barrier" within ENSO predictions

NASA Astrophysics Data System (ADS)

Duan, Wansuo; Zhao, Peng

2017-04-01

Within the Zebiak-Cane model, the nonlinear forcing singular vector (NFSV) approach is used to investigate the role of model errors in the "Spring Predictability Barrier" (SPB) phenomenon within ENSO predictions. NFSV-related errors have the largest negative effect on the uncertainties of El Niño predictions. NFSV errors can be classified into two types: the first is characterized by a zonal dipolar pattern of SST anomalies (SSTA), with the western poles centered in the equatorial central-western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite the first type. The first type of error tends to have the worst effects on El Niño growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV-related errors exhibits prominent seasonality, with the fastest error growth in the spring and/or summer seasons; hence, these errors result in a significant SPB related to El Niño events. The linear counterpart of NFSVs, the (linear) forcing singular vector (FSV), induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate a SPB for El Niño events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Niño events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Niño predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.

Self-Reported and Observed Punitive Parenting Prospectively Predicts Increased Error-Related Brain Activity in Six-Year-Old Children.

PubMed

Meyer, Alexandria; Proudfit, Greg Hajcak; Bufferd, Sara J; Kujawa, Autumn J; Laptook, Rebecca S; Torpey, Dana C; Klein, Daniel N

2015-07-01

The error-related negativity (ERN) is a negative deflection in the event-related potential (ERP) occurring approximately 50 ms after error commission at fronto-central electrode sites and is thought to reflect the activation of a generic error monitoring system. Several studies have reported an increased ERN in clinically anxious children, and suggest that anxious children are more sensitive to error commission--although the mechanisms underlying this association are not clear. We have previously found that punishing errors results in a larger ERN, an effect that persists after punishment ends. It is possible that learning-related experiences that impact sensitivity to errors may lead to an increased ERN. In particular, punitive parenting might sensitize children to errors and increase their ERN. We tested this possibility in the current study by prospectively examining the relationship between parenting style during early childhood and children's ERN approximately 3 years later. Initially, 295 parents and children (approximately 3 years old) participated in a structured observational measure of parenting behavior, and parents completed a self-report measure of parenting style. At a follow-up assessment approximately 3 years later, the ERN was elicited during a Go/No-Go task, and diagnostic interviews were completed with parents to assess child psychopathology. Results suggested that both observational measures of hostile parenting and self-report measures of authoritarian parenting style uniquely predicted a larger ERN in children 3 years later. We previously reported that children in this sample with anxiety disorders were characterized by an increased ERN. A mediation analysis indicated that ERN magnitude mediated the relationship between harsh parenting and child anxiety disorder. Results suggest that parenting may shape children's error processing through environmental conditioning and thereby risk for anxiety, although future work is needed to confirm this hypothesis.

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

PubMed Central

Zhang, Hongpo

2018-01-01

Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN) is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart) and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively. PMID:29854369

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

USGS Publications Warehouse

Schleier, Jerome J.; Peterson, Robert K.D.; Irvine, Kathryn M.; Marshall, Lucy M.; Weaver, David K.; Preftakes, Collin J.

2012-01-01

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

Optimal reentry prediction of space objects from LEO using RSM and GA

NASA Astrophysics Data System (ADS)

Mutyalarao, M.; Raj, M. Xavier James

2012-07-01

The accurate estimation of the orbital life time (OLT) of decaying near-Earth objects is of considerable importance for the prediction of risk object re-entry time and hazard assessment as well as for mitigation strategies. Recently, due to the reentries of large number of risk objects, which poses threat to the human life and property, a great concern is developed in the space scientific community all over the World. The evolution of objects in Low Earth Orbit (LEO) is determined by a complex interplay of the perturbing forces, mainly due to atmospheric drag and Earth gravity. These orbits are mostly in low eccentric (eccentricity < 0.2) and have variations in perigee and apogee altitudes due to perturbations during a revolution. The changes in the perigee and apogee altitudes of these orbits are mainly due to the gravitational perturbations of the Earth and the atmospheric density. It has become necessary to use extremely complex force models to match with the present operational requirements and observational techniques. Further the re-entry time of the objects in such orbits is sensitive to the initial conditions. In this paper the problem of predicting re-entry time is attempted as an optimal estimation problem. It is known that the errors are more in eccentricity for the observations based on two line elements (TLEs). Thus two parameters, initial eccentricity and ballistic coefficient, are chosen for optimal estimation. These two parameters are computed with response surface method (RSM) using a genetic algorithm (GA) for the selected time zones, based on rough linear variation of response parameter, the mean semi-major axis during orbit evolution. Error minimization between the observed and predicted mean Semi-major axis is achieved by the application of an optimization algorithm such as Genetic Algorithm (GA). The basic feature of the present approach is that the model and measurement errors are accountable in terms of adjusting the ballistic coefficient and eccentricity. The methodology is tested with the recently reentered objects ROSAT and PHOBOS GRUNT satellites. The study reveals a good agreement with the actual reentry time of these objects. It is also observed that the absolute percentage error in re-entry prediction time for all the two objects is found to be very less. Keywords: low eccentric, Response surface method, Genetic algorithm, apogee altitude, Ballistic coefficient

Accuracy of a nomogram for prediction of lymph-node metastasis detected with conventional histopathology and ultrastaging in endometrial cancer

PubMed Central

Koskas, M; Chereau, E; Ballester, M; Dubernard, G; Lécuru, F; Heitz, D; Mathevet, P; Marret, H; Querleu, D; Golfier, F; Leblanc, E; Luton, D; Rouzier, R; Daraï, E

2013-01-01

Background: We developed a nomogram based on five clinical and pathological characteristics to predict lymph-node (LN) metastasis with a high concordance probability in endometrial cancer. Sentinel LN (SLN) biopsy has been suggested as a compromise between systematic lymphadenectomy and no dissection in patients with low-risk endometrial cancer. Methods: Patients with stage I–II endometrial cancer had pelvic SLN and systematic pelvic-node dissection. All LNs were histopathologically examined, and the SLNs were examined by immunohistochemistry. We compared the accuracy of the nomogram at predicting LN detected with conventional histopathology (macrometastasis) and ultrastaging procedure using SLN (micrometastasis). Results: Thirty-eight of the 187 patients (20%) had pelvic LN metastases, 20 had macrometastases and 18 had micrometastases. For the prediction of macrometastases, the nomogram showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.76, and was well calibrated (average error =2.1%). For the prediction of micro- and macrometastases, the nomogram showed poorer discrimination, with an AUC of 0.67, and was less well calibrated (average error =10.9%). Conclusion: Our nomogram is accurate at predicting LN macrometastases but less accurate at predicting micrometastases. Our results suggest that micrometastases are an ‘intermediate state' between disease-free LN and macrometastasis. PMID:23481184

Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.

PubMed

Baba, Hiromi; Takahara, Jun-ichi; Yamashita, Fumiyoshi; Hashida, Mitsuru

2015-11-01

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

Experimental determination of solvent-water partition coefficients and Abraham parameters for munition constituents.

PubMed

Liang, Yuzhen; Kuo, Dave T F; Allen, Herbert E; Di Toro, Dominic M

2016-10-01

There is concern about the environmental fate and effects of munition constituents (MCs). Polyparameter linear free energy relationships (pp-LFERs) that employ Abraham solute parameters can aid in evaluating the risk of MCs to the environment. However, poor predictions using pp-LFERs and ABSOLV estimated Abraham solute parameters are found for some key physico-chemical properties. In this work, the Abraham solute parameters are determined using experimental partition coefficients in various solvent-water systems. The compounds investigated include hexahydro-1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX), hexahydro-1-nitroso-3,5-dinitro-1,3,5-triazine (MNX), hexahydro-1,3,5-trinitroso-1,3,5-triazine (TNX), hexahydro-1,3-dinitroso-5- nitro-1,3,5-triazine (DNX), 2,4,6-trinitrotoluene (TNT), 1,3,5-trinitrobenzene (TNB), and 4-nitroanisole. The solvents in the solvent-water systems are hexane, dichloromethane, trichloromethane, octanol, and toluene. The only available reported solvent-water partition coefficients are for octanol-water for some of the investigated compounds and they are in good agreement with the experimental measurements from this study. Solvent-water partition coefficients fitted using experimentally derived solute parameters from this study have significantly smaller root mean square errors (RMSE = 0.38) than predictions using ABSOLV estimated solute parameters (RMSE = 3.56) for the investigated compounds. Additionally, the predictions for various physico-chemical properties using the experimentally derived solute parameters agree with available literature reported values with prediction errors within 0.79 log units except for water solubility of RDX and HMX with errors of 1.48 and 2.16 log units respectively. However, predictions using ABSOLV estimated solute parameters have larger prediction errors of up to 7.68 log units. This large discrepancy is probably due to the missing R2NNO2 and R2NNO2 functional groups in the ABSOLV fragment database. Copyright © 2016. Published by Elsevier Ltd.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses.

PubMed

Samoli, Evangelia; Butland, Barbara K

2017-12-01

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data. We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

The six-minute walk test predicts cardiorespiratory fitness in individuals with aneurysmal subarachnoid hemorrhage.

PubMed

Harmsen, Wouter J; Ribbers, Gerard M; Slaman, Jorrit; Heijenbrok-Kal, Majanka H; Khajeh, Ladbon; van Kooten, Fop; Neggers, Sebastiaan J C M M; van den Berg-Emons, Rita J

2017-05-01

Peak oxygen uptake (VO 2peak ) established during progressive cardiopulmonary exercise testing (CPET) is the "gold-standard" for cardiorespiratory fitness. However, CPET measurements may be limited in patients with aneurysmal subarachnoid hemorrhage (a-SAH) by disease-related complaints, such as cardiovascular health-risks or anxiety. Furthermore, CPET with gas-exchange analyses require specialized knowledge and infrastructure with limited availability in most rehabilitation facilities. To determine whether an easy-to-administer six-minute walk test (6MWT) is a valid clinical alternative to progressive CPET in order to predict VO 2peak in individuals with a-SAH. Twenty-seven patients performed the 6MWT and CPET with gas-exchange analyses on a cycle ergometer. Univariate and multivariate regression models were made to investigate the predictability of VO 2peak from the six-minute walk distance (6MWD). Univariate regression showed that the 6MWD was strongly related to VO 2peak (r = 0.75, p < 0.001), with an explained variance of 56% and a prediction error of 4.12 ml/kg/min, representing 18% of mean VO 2peak . Adding age and sex to an extended multivariate regression model improved this relationship (r = 0.82, p < 0.001), with an explained variance of 67% and a prediction error of 3.67 ml/kg/min corresponding to 16% of mean VO 2peak . The 6MWT is an easy-to-administer submaximal exercise test that can be selected to estimate cardiorespiratory fitness at an aggregated level, in groups of patients with a-SAH, which may help to evaluate interventions in a clinical or research setting. However, the relatively large prediction error does not allow for an accurate prediction in individual patients.

Brain and behavioral evidence for altered social learning mechanisms among women with assault-related posttraumatic stress disorder.

PubMed

Cisler, Josh M; Bush, Keith; Scott Steele, J; Lenow, Jennifer K; Smitherman, Sonet; Kilts, Clinton D

2015-04-01

Current neurocircuitry models of PTSD focus on the neural mechanisms that mediate hypervigilance for threat and fear inhibition/extinction learning. Less focus has been directed towards explaining social deficits and heightened risk of revictimization observed among individuals with PTSD related to physical or sexual assault. The purpose of the present study was to foster more comprehensive theoretical models of PTSD by testing the hypothesis that assault-related PTSD is associated with behavioral impairments in a social trust and reciprocity task and corresponding alterations in the neural encoding of social learning mechanisms. Adult women with assault-related PTSD (n = 25) and control women (n = 15) completed a multi-trial trust game outside of the MRI scanner. A subset of these participants (15 with PTSD and 14 controls) also completed a social and non-social reinforcement learning task during 3T fMRI. Brain regions that encoded the computationally modeled parameters of value expectation, prediction error, and volatility (i.e., uncertainty) were defined and compared between groups. The PTSD group demonstrated slower learning rates during the trust game and social prediction errors had a lesser impact on subsequent investment decisions. PTSD was also associated with greater encoding of negative expected social outcomes in perigenual anterior cingulate cortex and bilateral middle frontal gyri, and greater encoding of social prediction errors in the left temporoparietal junction. These data suggest mechanisms of PTSD-related deficits in social functioning and heightened risk for re-victimization in assault victims; however, comorbidity in the PTSD group and the lack of a trauma-exposed control group temper conclusions about PTSD specifically. Copyright © 2015 Elsevier Ltd. All rights reserved.

Compound Stimulus Presentation Does Not Deepen Extinction in Human Causal Learning

PubMed Central

Griffiths, Oren; Holmes, Nathan; Westbrook, R. Fred

2017-01-01

Models of associative learning have proposed that cue-outcome learning critically depends on the degree of prediction error encountered during training. Two experiments examined the role of error-driven extinction learning in a human causal learning task. Target cues underwent extinction in the presence of additional cues, which differed in the degree to which they predicted the outcome, thereby manipulating outcome expectancy and, in the absence of any change in reinforcement, prediction error. These prediction error manipulations have each been shown to modulate extinction learning in aversive conditioning studies. While both manipulations resulted in increased prediction error during training, neither enhanced extinction in the present human learning task (one manipulation resulted in less extinction at test). The results are discussed with reference to the types of associations that are regulated by prediction error, the types of error terms involved in their regulation, and how these interact with parameters involved in training. PMID:28232809

Influence of precision of emission characteristic parameters on model prediction error of VOCs/formaldehyde from dry building material.

PubMed

Wei, Wenjuan; Xiong, Jianyin; Zhang, Yinping

2013-01-01

Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

PubMed Central

Schiffer, Anne-Marike; Ahlheim, Christiane; Wurm, Moritz F.; Schubotz, Ricarda I.

2012-01-01

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts. PMID:22570715

The application of SHERPA (Systematic Human Error Reduction and Prediction Approach) in the development of compensatory cognitive rehabilitation strategies for stroke patients with left and right brain damage.

PubMed

Hughes, Charmayne M L; Baber, Chris; Bienkiewicz, Marta; Worthington, Andrew; Hazell, Alexa; Hermsdörfer, Joachim

2015-01-01

Approximately 33% of stroke patients have difficulty performing activities of daily living, often committing errors during the planning and execution of such activities. The objective of this study was to evaluate the ability of the human error identification (HEI) technique SHERPA (Systematic Human Error Reduction and Prediction Approach) to predict errors during the performance of daily activities in stroke patients with left and right hemisphere lesions. Using SHERPA we successfully predicted 36 of the 38 observed errors, with analysis indicating that the proportion of predicted and observed errors was similar for all sub-tasks and severity levels. HEI results were used to develop compensatory cognitive strategies that clinicians could employ to reduce or prevent errors from occurring. This study provides evidence for the reliability and validity of SHERPA in the design of cognitive rehabilitation strategies in stroke populations.

Understanding seasonal variability of uncertainty in hydrological prediction

NASA Astrophysics Data System (ADS)

Li, M.; Wang, Q. J.

2012-04-01

Understanding uncertainty in hydrological prediction can be highly valuable for improving the reliability of streamflow prediction. In this study, a monthly water balance model, WAPABA, in a Bayesian joint probability with error models are presented to investigate the seasonal dependency of prediction error structure. A seasonal invariant error model, analogous to traditional time series analysis, uses constant parameters for model error and account for no seasonal variations. In contrast, a seasonal variant error model uses a different set of parameters for bias, variance and autocorrelation for each individual calendar month. Potential connection amongst model parameters from similar months is not considered within the seasonal variant model and could result in over-fitting and over-parameterization. A hierarchical error model further applies some distributional restrictions on model parameters within a Bayesian hierarchical framework. An iterative algorithm is implemented to expedite the maximum a posterior (MAP) estimation of a hierarchical error model. Three error models are applied to forecasting streamflow at a catchment in southeast Australia in a cross-validation analysis. This study also presents a number of statistical measures and graphical tools to compare the predictive skills of different error models. From probability integral transform histograms and other diagnostic graphs, the hierarchical error model conforms better to reliability when compared to the seasonal invariant error model. The hierarchical error model also generally provides the most accurate mean prediction in terms of the Nash-Sutcliffe model efficiency coefficient and the best probabilistic prediction in terms of the continuous ranked probability score (CRPS). The model parameters of the seasonal variant error model are very sensitive to each cross validation, while the hierarchical error model produces much more robust and reliable model parameters. Furthermore, the result of the hierarchical error model shows that most of model parameters are not seasonal variant except for error bias. The seasonal variant error model is likely to use more parameters than necessary to maximize the posterior likelihood. The model flexibility and robustness indicates that the hierarchical error model has great potential for future streamflow predictions.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

Temporal-difference prediction errors and Pavlovian fear conditioning: role of NMDA and opioid receptors.

PubMed

Cole, Sindy; McNally, Gavan P

2007-10-01

Three experiments studied temporal-difference (TD) prediction errors during Pavlovian fear conditioning. In Stage I, rats received conditioned stimulus A (CSA) paired with shock. In Stage II, they received pairings of CSA and CSB with shock that blocked learning to CSB. In Stage III, a serial overlapping compound, CSB --> CSA, was followed by shock. The change in intratrial durations supported fear learning to CSB but reduced fear of CSA, revealing the operation of TD prediction errors. N-methyl- D-aspartate (NMDA) receptor antagonism prior to Stage III prevented learning, whereas opioid receptor antagonism selectively affected predictive learning. These findings support a role for TD prediction errors in fear conditioning. They suggest that NMDA receptors contribute to fear learning by acting on the product of predictive error, whereas opioid receptors contribute to predictive error. (PsycINFO Database Record (c) 2007 APA, all rights reserved).

Dopamine neurons share common response function for reward prediction error

PubMed Central

Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

2016-01-01

Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically-identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found striking homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we could describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal. PMID:26854803

Temporal specificity of reward prediction errors signaled by putative dopamine neurons in rat VTA depends on ventral striatum

PubMed Central

Takahashi, Yuji K.; Langdon, Angela J.; Niv, Yael; Schoenbaum, Geoffrey

2016-01-01

Summary Dopamine neurons signal reward prediction errors. This requires accurate reward predictions. It has been suggested that the ventral striatum provides these predictions. Here we tested this hypothesis by recording from putative dopamine neurons in the VTA of rats performing a task in which prediction errors were induced by shifting reward timing or number. In controls, the neurons exhibited error signals in response to both manipulations. However, dopamine neurons in rats with ipsilateral ventral striatal lesions exhibited errors only to changes in number and failed to respond to changes in timing of reward. These results, supported by computational modeling, indicate that predictions about the temporal specificity and the number of expected rewards are dissociable, and that dopaminergic prediction-error signals rely on the ventral striatum for the former but not the latter. PMID:27292535

The Pediatric Risk of Mortality Score: Update 2015

PubMed Central

Pollack, Murray M.; Holubkov, Richard; Funai, Tomohiko; Dean, J. Michael; Berger, John T.; Wessel, David L.; Meert, Kathleen; Berg, Robert A.; Newth, Christopher J. L.; Harrison, Rick E.; Carcillo, Joseph; Dalton, Heidi; Shanley, Thomas; Jenkins, Tammara L.; Tamburro, Robert

2016-01-01

Objectives Severity of illness measures have long been used in pediatric critical care. The Pediatric Risk of Mortality is a physiologically based score used to quantify physiologic status, and when combined with other independent variables, it can compute expected mortality risk and expected morbidity risk. Although the physiologic ranges for the Pediatric Risk of Mortality variables have not changed, recent Pediatric Risk of Mortality data collection improvements have been made to adapt to new practice patterns, minimize bias, and reduce potential sources of error. These include changing the outcome to hospital survival/death for the first PICU admission only, shortening the data collection period and altering the Pediatric Risk of Mortality data collection period for patients admitted for “optimizing” care before cardiac surgery or interventional catheterization. This analysis incorporates those changes, assesses the potential for Pediatric Risk of Mortality physiologic variable subcategories to improve score performance, and recalibrates the Pediatric Risk of Mortality score, placing the algorithms (Pediatric Risk of Mortality IV) in the public domain. Design Prospective cohort study from December 4, 2011, to April 7, 2013. Measurements and Main Results Among 10,078 admissions, the unadjusted mortality rate was 2.7% (site range, 1.3–5.0%). Data were divided into derivation (75%) and validation (25%) sets. The new Pediatric Risk of Mortality prediction algorithm (Pediatric Risk of Mortality IV) includes the same Pediatric Risk of Mortality physiologic variable ranges with the subcategories of neurologic and nonneurologic Pediatric Risk of Mortality scores, age, admission source, cardiopulmonary arrest within 24 hours before admission, cancer, and low-risk systems of primary dysfunction. The area under the receiver operating characteristic curve for the development and validation sets was 0.88 ± 0.013 and 0.90 ± 0.018, respectively. The Hosmer-Lemeshow goodness of fit statistics indicated adequate model fit for both the development (p = 0.39) and validation (p = 0.50) sets. Conclusions The new Pediatric Risk of Mortality data collection methods include significant improvements that minimize the potential for bias and errors, and the new Pediatric Risk of Mortality IV algorithm for survival and death has excellent prediction performance. PMID:26492059

Visuomotor adaptation needs a validation of prediction error by feedback error

PubMed Central

Gaveau, Valérie; Prablanc, Claude; Laurent, Damien; Rossetti, Yves; Priot, Anne-Emmanuelle

2014-01-01

The processes underlying short-term plasticity induced by visuomotor adaptation to a shifted visual field are still debated. Two main sources of error can induce motor adaptation: reaching feedback errors, which correspond to visually perceived discrepancies between hand and target positions, and errors between predicted and actual visual reafferences of the moving hand. These two sources of error are closely intertwined and difficult to disentangle, as both the target and the reaching limb are simultaneously visible. Accordingly, the goal of the present study was to clarify the relative contributions of these two types of errors during a pointing task under prism-displaced vision. In “terminal feedback error” condition, viewing of their hand by subjects was allowed only at movement end, simultaneously with viewing of the target. In “movement prediction error” condition, viewing of the hand was limited to movement duration, in the absence of any visual target, and error signals arose solely from comparisons between predicted and actual reafferences of the hand. In order to prevent intentional corrections of errors, a subthreshold, progressive stepwise increase in prism deviation was used, so that subjects remained unaware of the visual deviation applied in both conditions. An adaptive aftereffect was observed in the “terminal feedback error” condition only. As far as subjects remained unaware of the optical deviation and self-assigned pointing errors, prediction error alone was insufficient to induce adaptation. These results indicate a critical role of hand-to-target feedback error signals in visuomotor adaptation; consistent with recent neurophysiological findings, they suggest that a combination of feedback and prediction error signals is necessary for eliciting aftereffects. They also suggest that feedback error updates the prediction of reafferences when a visual perturbation is introduced gradually and cognitive factors are eliminated or strongly attenuated. PMID:25408644

Correlation of clinical predictions and surgical results in maxillary superior repositioning.

PubMed

Tabrizi, Reza; Zamiri, Barbad; Kazemi, Hamidreza

2014-05-01

This is a prospective study to evaluate the accuracy of clinical predictions related to surgical results in subjects who underwent maxillary superior repositioning without anterior-posterior movement. Surgeons' predictions according to clinical (tooth show at rest and at the maximum smile) and cephalometric evaluation were documented for the amount of maxillary superior repositioning. Overcorrection or undercorrection was documented for every subject 1 year after the operations. Receiver operating characteristic curve test was used to find a cutoff point in prediction errors and to determine positive predictive value (PPV) and negative predictive value. Forty subjects (14 males and 26 females) were studied. Results showed a significant difference between changes in the tooth show at rest and at the maximum smile line before and after surgery. Analysis of the data demonstrated no correlation between the predictive data and the surgical results. The incidence of undercorrection (25%) was more common than overcorrection (7.5%). The cutoff point for errors in predictions was 5 mm for tooth show at rest and 15 mm at the maximum smile. When the amount of the presurgical tooth show at rest was more than 5 mm, 50.5% of clinical predictions did not match the clinical results (PPV), and 75% of clinical predictions showed the same results when the tooth show was less than 5 mm (negative predictive value). When the amount of presurgical tooth shown in the maximum smile line was more than 15 mm, 75% of clinical predictions did not match with clinical results (PPV), and 25% of the predictions had the same results because the tooth show at the maximum smile was lower than 15 mm. Clinical predictions according to the tooth show at rest and at the maximum smile have a poor correlation with clinical results in maxillary superior repositioning for vertical maxillary excess. The risk of errors in predictions increased when the amount of superior repositioning of the maxilla increased. Generally, surgeons have a tendency to undercorrect rather than overcorrect, although clinical prediction is an original guideline for surgeons, and it may be associated with variable clinical results.

Cognition in Space Workshop. 1; Metrics and Models

NASA Technical Reports Server (NTRS)

Woolford, Barbara; Fielder, Edna

2005-01-01

"Cognition in Space Workshop I: Metrics and Models" was the first in a series of workshops sponsored by NASA to develop an integrated research and development plan supporting human cognition in space exploration. The workshop was held in Chandler, Arizona, October 25-27, 2004. The participants represented academia, government agencies, and medical centers. This workshop addressed the following goal of the NASA Human System Integration Program for Exploration: to develop a program to manage risks due to human performance and human error, specifically ones tied to cognition. Risks range from catastrophic error to degradation of efficiency and failure to accomplish mission goals. Cognition itself includes memory, decision making, initiation of motor responses, sensation, and perception. Four subgoals were also defined at the workshop as follows: (1) NASA needs to develop a human-centered design process that incorporates standards for human cognition, human performance, and assessment of human interfaces; (2) NASA needs to identify and assess factors that increase risks associated with cognition; (3) NASA needs to predict risks associated with cognition; and (4) NASA needs to mitigate risk, both prior to actual missions and in real time. This report develops the material relating to these four subgoals.

The Driver Behaviour Questionnaire: a North American analysis.

PubMed

Cordazzo, Sheila T D; Scialfa, Charles T; Bubric, Katherine; Ross, Rachel Jones

2014-09-01

The Driver Behaviour Questionnaire (DBQ), originally developed in Britain by Reason et al. [Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the road: A real distinction? Ergonomics, 33, 1315-1332] is one of the most widely used instruments for measuring driver behaviors linked to collision risk. The goals of the study were to adapt the DBQ for a North American driving population, assess the component structure of the items, and to determine whether scores on the DBQ could predict self-reported traffic collisions. Of the original Reason et al. items, our data indicate a two-component solution involving errors and violations. Evidence for a Lapses component was not found. The 20 items most closely resembling those of Parker et al. [Parker, D., Reason, J. T., Manstead, A. S. R., & Stradling, S. G. (1995). Driving errors, driving violations and accident involvement. Ergonomics, 38, 1036-1048] yielded a solution with 3 orthogonal components that reflect errors, lapses, and violations. Although violations and Lapses were positively and significantly correlated with self-reported collision involvement, the classification accuracy of the resulting models was quite poor. A North American DBQ has the same component structure as reported previously, but has limited ability to predict self-reported collisions. Copyright © 2014 National Safety Council and Elsevier Ltd. All rights reserved.

Effects of skilled nursing facility structure and process factors on medication errors during nursing home admission.

PubMed

Lane, Sandi J; Troyer, Jennifer L; Dienemann, Jacqueline A; Laditka, Sarah B; Blanchette, Christopher M

2014-01-01

Older adults are at greatest risk of medication errors during the transition period of the first 7 days after admission and readmission to a skilled nursing facility (SNF). The aim of this study was to evaluate structure- and process-related factors that contribute to medication errors and harm during transition periods at a SNF. Data for medication errors and potential medication errors during the 7-day transition period for residents entering North Carolina SNFs were from the Medication Error Quality Initiative-Individual Error database from October 2006 to September 2007. The impact of SNF structure and process measures on the number of reported medication errors and harm from errors were examined using bivariate and multivariate model methods. A total of 138 SNFs reported 581 transition period medication errors; 73 (12.6%) caused harm. Chain affiliation was associated with a reduction in the volume of errors during the transition period. One third of all reported transition errors occurred during the medication administration phase of the medication use process, where dose omissions were the most common type of error; however, dose omissions caused harm less often than wrong-dose errors did. Prescribing errors were much less common than administration errors but were much more likely to cause harm. Both structure and process measures of quality were related to the volume of medication errors.However, process quality measures may play a more important role in predicting harm from errors during the transition of a resident into an SNF. Medication errors during transition could be reduced by improving both prescribing processes and transcription and documentation of orders.

Interactions of timing and prediction error learning.

PubMed

Kirkpatrick, Kimberly

2014-01-01

Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields. Copyright © 2013 Elsevier B.V. All rights reserved.

Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

PubMed

Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

2013-03-27

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

Influence of Precision of Emission Characteristic Parameters on Model Prediction Error of VOCs/Formaldehyde from Dry Building Material

PubMed Central

Wei, Wenjuan; Xiong, Jianyin; Zhang, Yinping

2013-01-01

Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C. PMID:24312497

Dopaminergic Modulation of Decision Making and Subjective Well-Being.

PubMed

Rutledge, Robb B; Skandali, Nikolina; Dayan, Peter; Dolan, Raymond J

2015-07-08

The neuromodulator dopamine has a well established role in reporting appetitive prediction errors that are widely considered in terms of learning. However, across a wide variety of contexts, both phasic and tonic aspects of dopamine are likely to exert more immediate effects that have been less well characterized. Of particular interest is dopamine's influence on economic risk taking and on subjective well-being, a quantity known to be substantially affected by prediction errors resulting from the outcomes of risky choices. By boosting dopamine levels using levodopa (l-DOPA) as human subjects made economic decisions and repeatedly reported their momentary happiness, we show here an effect on both choices and happiness. Boosting dopamine levels increased the number of risky options chosen in trials involving potential gains but not trials involving potential losses. This effect could be better captured as increased Pavlovian approach in an approach-avoidance decision model than as a change in risk preferences within an established prospect theory model. Boosting dopamine also increased happiness resulting from some rewards. Our findings thus identify specific novel influences of dopamine on decision making and emotion that are distinct from its established role in learning. Copyright © 2015 Rutledge et al.

Dopaminergic Modulation of Decision Making and Subjective Well-Being

PubMed Central

Skandali, Nikolina; Dayan, Peter; Dolan, Raymond J.

2015-01-01

The neuromodulator dopamine has a well established role in reporting appetitive prediction errors that are widely considered in terms of learning. However, across a wide variety of contexts, both phasic and tonic aspects of dopamine are likely to exert more immediate effects that have been less well characterized. Of particular interest is dopamine's influence on economic risk taking and on subjective well-being, a quantity known to be substantially affected by prediction errors resulting from the outcomes of risky choices. By boosting dopamine levels using levodopa (l-DOPA) as human subjects made economic decisions and repeatedly reported their momentary happiness, we show here an effect on both choices and happiness. Boosting dopamine levels increased the number of risky options chosen in trials involving potential gains but not trials involving potential losses. This effect could be better captured as increased Pavlovian approach in an approach–avoidance decision model than as a change in risk preferences within an established prospect theory model. Boosting dopamine also increased happiness resulting from some rewards. Our findings thus identify specific novel influences of dopamine on decision making and emotion that are distinct from its established role in learning. PMID:26156984

The use of logistic regression to enhance risk assessment and decision making by mental health administrators.

PubMed

Menditto, Anthony A; Linhorst, Donald M; Coleman, James C; Beck, Niels C

2006-04-01

Development of policies and procedures to contend with the risks presented by elopement, aggression, and suicidal behaviors are long-standing challenges for mental health administrators. Guidance in making such judgments can be obtained through the use of a multivariate statistical technique known as logistic regression. This procedure can be used to develop a predictive equation that is mathematically formulated to use the best combination of predictors, rather than considering just one factor at a time. This paper presents an overview of logistic regression and its utility in mental health administrative decision making. A case example of its application is presented using data on elopements from Missouri's long-term state psychiatric hospitals. Ultimately, the use of statistical prediction analyses tempered with differential qualitative weighting of classification errors can augment decision-making processes in a manner that provides guidance and flexibility while wrestling with the complex problem of risk assessment and decision making.

The modulation of savouring by prediction error and its effects on choice

PubMed Central

Iigaya, Kiyohito; Story, Giles W; Kurth-Nelson, Zeb; Dolan, Raymond J; Dayan, Peter

2016-01-01

When people anticipate uncertain future outcomes, they often prefer to know their fate in advance. Inspired by an idea in behavioral economics that the anticipation of rewards is itself attractive, we hypothesized that this preference of advance information arises because reward prediction errors carried by such information can boost the level of anticipation. We designed new empirical behavioral studies to test this proposal, and confirmed that subjects preferred advance reward information more strongly when they had to wait for rewards for a longer time. We formulated our proposal in a reinforcement-learning model, and we showed that our model could account for a wide range of existing neuronal and behavioral data, without appealing to ambiguous notions such as an explicit value for information. We suggest that such boosted anticipation significantly drives risk-seeking behaviors, most pertinently in gambling. DOI: http://dx.doi.org/10.7554/eLife.13747.001 PMID:27101365

Late-Onset Alzheimer's Disease Polygenic Risk Profile Score Predicts Hippocampal Function.

PubMed

Xiao, Ena; Chen, Qiang; Goldman, Aaron L; Tan, Hao Yang; Healy, Kaitlin; Zoltick, Brad; Das, Saumitra; Kolachana, Bhaskar; Callicott, Joseph H; Dickinson, Dwight; Berman, Karen F; Weinberger, Daniel R; Mattay, Venkata S

2017-11-01

We explored the cumulative effect of several late-onset Alzheimer's disease (LOAD) risk loci using a polygenic risk profile score (RPS) approach on measures of hippocampal function, cognition, and brain morphometry. In a sample of 231 healthy control subjects (19-55 years of age), we used an RPS to study the effect of several LOAD risk loci reported in a recent meta-analysis on hippocampal function (determined by its engagement with blood oxygen level-dependent functional magnetic resonance imaging during episodic memory) and several cognitive metrics. We also studied effects on brain morphometry in an overlapping sample of 280 subjects. There was almost no significant association of LOAD-RPS with cognitive or morphometric measures. However, there was a significant negative relationship between LOAD-RPS and hippocampal function (familywise error [small volume correction-hippocampal region of interest] p < .05). There were also similar associations for risk score based on APOE haplotype, and for a combined LOAD-RPS + APOE haplotype risk profile score (p < .05 familywise error [small volume correction-hippocampal region of interest]). Of the 29 individual single nucleotide polymorphisms used in calculating LOAD-RPS, variants in CLU, PICALM, BCL3, PVRL2, and RELB showed strong effects (p < .05 familywise error [small volume correction-hippocampal region of interest]) on hippocampal function, though none survived further correction for the number of single nucleotide polymorphisms tested. There is a cumulative deleterious effect of LOAD risk genes on hippocampal function even in healthy volunteers. The effect of LOAD-RPS on hippocampal function in the relative absence of any effect on cognitive and morphometric measures is consistent with the reported temporal characteristics of LOAD biomarkers with the earlier manifestation of synaptic dysfunction before morphometric and cognitive changes. Copyright © 2017 Society of Biological Psychiatry. All rights reserved.

Predicting the thermal/structural performance of the atmospheric trace molecules spectroscopy /ATMOS/ Fourier transform spectrometer

NASA Technical Reports Server (NTRS)

Miller, J. M.

1980-01-01

ATMOS is a Fourier transform spectrometer to measure atmospheric trace molecules over a spectral range of 2-16 microns. Assessment of the system performance of ATMOS includes evaluations of optical system errors induced by thermal and structural effects. In order to assess the optical system errors induced from thermal and structural effects, error budgets are assembled during system engineering tasks and line of sight and wavefront deformations predictions (using operational thermal and vibration environments and computer models) are subsequently compared to the error budgets. This paper discusses the thermal/structural error budgets, modelling and analysis methods used to predict thermal/structural induced errors and the comparisons that show that predictions are within the error budgets.

Disrupted prediction-error signal in psychosis: evidence for an associative account of delusions

PubMed Central

Corlett, P. R.; Murray, G. K.; Honey, G. D.; Aitken, M. R. F.; Shanks, D. R.; Robbins, T.W.; Bullmore, E.T.; Dickinson, A.; Fletcher, P. C.

2012-01-01

Delusions are maladaptive beliefs about the world. Based upon experimental evidence that prediction error—a mismatch between expectancy and outcome—drives belief formation, this study examined the possibility that delusions form because of disrupted prediction-error processing. We used fMRI to determine prediction-error-related brain responses in 12 healthy subjects and 12 individuals (7 males) with delusional beliefs. Frontal cortex responses in the patient group were suggestive of disrupted prediction-error processing. Furthermore, across subjects, the extent of disruption was significantly related to an individual’s propensity to delusion formation. Our results support a neurobiological theory of delusion formation that implicates aberrant prediction-error signalling, disrupted attentional allocation and associative learning in the formation of delusional beliefs. PMID:17690132

Improved forecasts of winter weather extremes over midlatitudes with extra Arctic observations

NASA Astrophysics Data System (ADS)

Sato, Kazutoshi; Inoue, Jun; Yamazaki, Akira; Kim, Joo-Hong; Maturilli, Marion; Dethloff, Klaus; Hudson, Stephen R.; Granskog, Mats A.

2017-02-01

Recent cold winter extremes over Eurasia and North America have been considered to be a consequence of a warming Arctic. More accurate weather forecasts are required to reduce human and socioeconomic damages associated with severe winters. However, the sparse observing network over the Arctic brings errors in initializing a weather prediction model, which might impact accuracy of prediction results at midlatitudes. Here we show that additional Arctic radiosonde observations from the Norwegian young sea ICE expedition (N-ICE2015) drifting ice camps and existing land stations during winter improved forecast skill and reduced uncertainties of weather extremes at midlatitudes of the Northern Hemisphere. For two winter storms over East Asia and North America in February 2015, ensemble forecast experiments were performed with initial conditions taken from an ensemble atmospheric reanalysis in which the observation data were assimilated. The observations reduced errors in initial conditions in the upper troposphere over the Arctic region, yielding more precise prediction of the locations and strengths of upper troughs and surface synoptic disturbances. Errors and uncertainties of predicted upper troughs at midlatitudes would be brought with upper level high potential vorticity (PV) intruding southward from the observed Arctic region. This is because the PV contained a "signal" of the additional Arctic observations as it moved along an isentropic surface. This suggests that a coordinated sustainable Arctic observing network would be effective not only for regional weather services but also for reducing weather risks in locations distant from the Arctic.

Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia

PubMed Central

Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew

2013-01-01

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously. PMID:23536092

Risk managers, physicians, and disclosure of harmful medical errors.

PubMed

Loren, David J; Garbutt, Jane; Dunagan, W Claiborne; Bommarito, Kerry M; Ebers, Alison G; Levinson, Wendy; Waterman, Amy D; Fraser, Victoria J; Summy, Elizabeth A; Gallagher, Thomas H

2010-03-01

Physicians are encouraged to disclose medical errors to patients, which often requires close collaboration between physicians and risk managers. An anonymous national survey of 2,988 healthcare facility-based risk managers was conducted between November 2004 and March 2005, and results were compared with those of a previous survey (conducted between July 2003 and March 2004) of 1,311 medical physicians in Washington and Missouri. Both surveys included an error-disclosure scenario for an obvious and a less obvious error with scripted response options. More risk managers than physicians were aware that an error-reporting system was present at their hospital (81% versus 39%, p < .001) and believed that mechanisms to inform physicians about errors in their hospital were adequate (51% versus 17%, p < .001). More risk managers than physicians strongly agreed that serious errors should be disclosed to patients (70% versus 49%, p < .001). Across both error scenario, risk managers were more likely than physicians to definitely recommend that the error be disclosed (76% versus 50%, p < .001) and to provide full details about how the error would be prevented in the future (62% versus 51%, p < .001). However, physicians were more likely than risk managers to provide a full apology recognizing the harm caused by the error (39% versus 21%, p < .001). Risk managers have more favorable attitudes about disclosing errors to patients compared with physicians but are less supportive of providing a full apology. These differences may create conflicts between risk managers and physicians regarding disclosure. Health care institutions should promote greater collaboration between these two key participants in disclosure conversations.

Essays in financial economics and econometrics

NASA Astrophysics Data System (ADS)

La Spada, Gabriele

Chapter 1 (my job market paper) asks the following question: Do asset managers reach for yield because of competitive pressures in a low rate environment? I propose a tournament model of money market funds (MMFs) to study this issue. I show that funds with different costs of default respond differently to changes in interest rates, and that it is important to distinguish the role of risk-free rates from that of risk premia. An increase in the risk premium leads funds with lower default costs to increase risk-taking, while funds with higher default costs reduce risk-taking. Without changes in the premium, low risk-free rates reduce risk-taking. My empirical analysis shows that these predictions are consistent with the risk-taking of MMFs during the 2006--2008 period. Chapter 2, co-authored with Fabrizio Lillo and published in Studies in Nonlinear Dynamics and Econometrics (2014), studies the effect of round-off error (or discretization) on stationary Gaussian long-memory process. For large lags, the autocovariance is rescaled by a factor smaller than one, and we compute this factor exactly. Hence, the discretized process has the same Hurst exponent as the underlying one. We show that in presence of round-off error, two common estimators of the Hurst exponent, the local Whittle (LW) estimator and the detrended fluctuation analysis (DFA), are severely negatively biased in finite samples. We derive conditions for consistency and asymptotic normality of the LW estimator applied to discretized processes and compute the asymptotic properties of the DFA for generic long-memory processes that encompass discretized processes. Chapter 3, co-authored with Fabrizio Lillo, studies the effect of round-off error on integrated Gaussian processes with possibly correlated increments. We derive the variance and kurtosis of the realized increment process in the limit of both "small" and "large" round-off errors, and its autocovariance for large lags. We propose novel estimators for the variance and lag-one autocorrelation of the underlying, unobserved increment process. We also show that for fractionally integrated processes, the realized increments have the same Hurst exponent as the underlying ones, but the LW estimator applied to the realized series is severely negatively biased in medium-sized samples.

New dimension analyses with error analysis for quaking aspen and black spruce

NASA Technical Reports Server (NTRS)

Woods, K. D.; Botkin, D. B.; Feiveson, A. H.

1987-01-01

Dimension analysis for black spruce in wetland stands and trembling aspen are reported, including new approaches in error analysis. Biomass estimates for sacrificed trees have standard errors of 1 to 3%; standard errors for leaf areas are 10 to 20%. Bole biomass estimation accounts for most of the error for biomass, while estimation of branch characteristics and area/weight ratios accounts for the leaf area error. Error analysis provides insight for cost effective design of future analyses. Predictive equations for biomass and leaf area, with empirically derived estimators of prediction error, are given. Systematic prediction errors for small aspen trees and for leaf area of spruce from different site-types suggest a need for different predictive models within species. Predictive equations are compared with published equations; significant differences may be due to species responses to regional or site differences. Proportional contributions of component biomass in aspen change in ways related to tree size and stand development. Spruce maintains comparatively constant proportions with size, but shows changes corresponding to site. This suggests greater morphological plasticity of aspen and significance for spruce of nutrient conditions.

Frontal Theta Links Prediction Errors to Behavioral Adaptation in Reinforcement Learning

PubMed Central

Cavanagh, James F.; Frank, Michael J.; Klein, Theresa J.; Allen, John J.B.

2009-01-01

Investigations into action monitoring have consistently detailed a fronto-central voltage deflection in the Event-Related Potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the Feedback Related Negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Medio-frontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations: with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice. PMID:19969093

Association between split selection instability and predictive error in survival trees.

PubMed

Radespiel-Tröger, M; Gefeller, O; Rabenstein, T; Hothorn, T

2006-01-01

To evaluate split selection instability in six survival tree algorithms and its relationship with predictive error by means of a bootstrap study. We study the following algorithms: logrank statistic with multivariate p-value adjustment without pruning (LR), Kaplan-Meier distance of survival curves (KM), martingale residuals (MR), Poisson regression for censored data (PR), within-node impurity (WI), and exponential log-likelihood loss (XL). With the exception of LR, initial trees are pruned by using split-complexity, and final trees are selected by means of cross-validation. We employ a real dataset from a clinical study of patients with gallbladder stones. The predictive error is evaluated using the integrated Brier score for censored data. The relationship between split selection instability and predictive error is evaluated by means of box-percentile plots, covariate and cutpoint selection entropy, and cutpoint selection coefficients of variation, respectively, in the root node. We found a positive association between covariate selection instability and predictive error in the root node. LR yields the lowest predictive error, while KM and MR yield the highest predictive error. The predictive error of survival trees is related to split selection instability. Based on the low predictive error of LR, we recommend the use of this algorithm for the construction of survival trees. Unpruned survival trees with multivariate p-value adjustment can perform equally well compared to pruned trees. The analysis of split selection instability can be used to communicate the results of tree-based analyses to clinicians and to support the application of survival trees.

A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis

PubMed Central

Reyes, Mauricio; Zysset, Philippe

2017-01-01

Osteoporosis leads to hip fractures in aging populations and is diagnosed by modern medical imaging techniques such as quantitative computed tomography (QCT). Hip fracture sites involve trabecular bone, whose strength is determined by volume fraction and orientation, known as fabric. However, bone fabric cannot be reliably assessed in clinical QCT images of proximal femur. Accordingly, we propose a novel registration-based estimation of bone fabric designed to preserve tensor properties of bone fabric and to map bone fabric by a global and local decomposition of the gradient of a non-rigid image registration transformation. Furthermore, no comprehensive analysis on the critical components of this methodology has been previously conducted. Hence, the aim of this work was to identify the best registration-based strategy to assign bone fabric to the QCT image of a patient’s proximal femur. The normalized correlation coefficient and curvature-based regularization were used for image-based registration and the Frobenius norm of the stretch tensor of the local gradient was selected to quantify the distance among the proximal femora in the population. Based on this distance, closest, farthest and mean femora with a distinction of sex were chosen as alternative atlases to evaluate their influence on bone fabric prediction. Second, we analyzed different tensor mapping schemes for bone fabric prediction: identity, rotation-only, rotation and stretch tensor. Third, we investigated the use of a population average fabric atlas. A leave one out (LOO) evaluation study was performed with a dual QCT and HR-pQCT database of 36 pairs of human femora. The quality of the fabric prediction was assessed with three metrics, the tensor norm (TN) error, the degree of anisotropy (DA) error and the angular deviation of the principal tensor direction (PTD). The closest femur atlas (CTP) with a full rotation (CR) for fabric mapping delivered the best results with a TN error of 7.3 ± 0.9%, a DA error of 6.6 ± 1.3% and a PTD error of 25 ± 2°. The closest to the population mean femur atlas (MTP) using the same mapping scheme yielded only slightly higher errors than CTP for substantially less computing efforts. The population average fabric atlas yielded substantially higher errors than the MTP with the CR mapping scheme. Accounting for sex did not bring any significant improvements. The identified fabric mapping methodology will be exploited in patient-specific QCT-based finite element analysis of the proximal femur to improve the prediction of hip fracture risk. PMID:29176881

Combination of Pre-Treatment DWI-Signal Intensity and S-1 Treatment: A Predictor of Survival in Patients with Locally Advanced Pancreatic Cancer Receiving Stereotactic Body Radiation Therapy and Sequential S-1.

PubMed

Zhang, Yu; Zhu, Xiaofei; Liu, Ri; Wang, Xianglian; Sun, Gaofeng; Song, Jiaqi; Lu, Jianping; Zhang, Huojun

2018-04-01

To identify whether the combination of pre-treatment radiological and clinical factors can predict the overall survival (OS) in patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiation and sequential S-1 (a prodrug of 5-FU combined with two modulators) therapy with improved accuracy compared with that of established clinical and radiologic risk models. Patients admitted with LAPC underwent diffusion weighted imaging (DWI) scan at 3.0-T (b = 600 s/mm 2 ). The mean signal intensity (SI b = 600) of region-of-interest (ROI) was measured. The Log-rank test was done for tumor location, biliary stent, S-1, and other treatments and the Cox regression analysis was done to identify independent prognostic factors for OS. Prediction error curves (PEC) were used to assess potential errors in prediction of survival. The accuracy of prediction was evaluated by Integrated Brier Score (IBS) and C index. 41 patients were included in this study. The median OS was 11.7 months (2.8-23.23 months). The 1-year OS was 46%. Multivariate analysis showed that pre-treatment SI b = 600 value and administration of S-1 were independent predictors for OS. The performance of pre-treatment SI b = 600 and S-1 treatment in combination was better than that of SI b = 600 or S-1 treatment alone. The combination of pre-treatment SI b = 600 and S-1 treatment could predict the OS in patients with LAPC undergoing SBRT and sequential S-1 therapy with improved accuracy compared with that of established clinical and radiologic risk models. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

An automated construction of error models for uncertainty quantification and model calibration

NASA Astrophysics Data System (ADS)

Josset, L.; Lunati, I.

2015-12-01

To reduce the computational cost of stochastic predictions, it is common practice to rely on approximate flow solvers (or «proxy»), which provide an inexact, but computationally inexpensive response [1,2]. Error models can be constructed to correct the proxy response: based on a learning set of realizations for which both exact and proxy simulations are performed, a transformation is sought to map proxy into exact responses. Once the error model is constructed a prediction of the exact response is obtained at the cost of a proxy simulation for any new realization. Despite its effectiveness [2,3], the methodology relies on several user-defined parameters, which impact the accuracy of the predictions. To achieve a fully automated construction, we propose a novel methodology based on an iterative scheme: we first initialize the error model with a small training set of realizations; then, at each iteration, we add a new realization both to improve the model and to evaluate its performance. More specifically, at each iteration we use the responses predicted by the updated model to identify the realizations that need to be considered to compute the quantity of interest. Another user-defined parameter is the number of dimensions of the response spaces between which the mapping is sought. To identify the space dimensions that optimally balance mapping accuracy and risk of overfitting, we follow a Leave-One-Out Cross Validation. Also, the definition of a stopping criterion is central to an automated construction. We use a stability measure based on bootstrap techniques to stop the iterative procedure when the iterative model has converged. The methodology is illustrated with two test cases in which an inverse problem has to be solved and assess the performance of the method. We show that an iterative scheme is crucial to increase the applicability of the approach. [1] Josset, L., and I. Lunati, Local and global error models for improving uncertainty quantification, Math.ematical Geosciences, 2013 [2] Josset, L., D. Ginsbourger, and I. Lunati, Functional Error Modeling for uncertainty quantification in hydrogeology, Water Resources Research, 2015 [3] Josset, L., V. Demyanov, A.H. Elsheikhb, and I. Lunati, Accelerating Monte Carlo Markov chains with proxy and error models, Computer & Geosciences, 2015 (In press)

Risk Factors for Increased Severity of Paediatric Medication Administration Errors

PubMed Central

Sears, Kim; Goodman, William M.

2012-01-01

Patients' risks from medication errors are widely acknowledged. Yet not all errors, if they occur, have the same risks for severe consequences. Facing resource constraints, policy makers could prioritize factors having the greatest severe–outcome risks. This study assists such prioritization by identifying work-related risk factors most clearly associated with more severe consequences. Data from three Canadian paediatric centres were collected, without identifiers, on actual or potential errors that occurred. Three hundred seventy-two errors were reported, with outcome severities ranging from time delays up to fatalities. Four factors correlated significantly with increased risk for more severe outcomes: insufficient training; overtime; precepting a student; and off-service patient. Factors' impacts on severity also vary with error class: for wrong-time errors, the factors precepting a student or working overtime significantly increase severe-outcomes risk. For other types, caring for an off-service patient has greatest severity risk. To expand such research, better standardization is needed for categorizing outcome severities. PMID:23968607

Artificial neural networks as a useful tool to predict the risk level of Betula pollen in the air

NASA Astrophysics Data System (ADS)

Castellano-Méndez, M.; Aira, M. J.; Iglesias, I.; Jato, V.; González-Manteiga, W.

2005-05-01

An increasing percentage of the European population suffers from allergies to pollen. The study of the evolution of air pollen concentration supplies prior knowledge of the levels of pollen in the air, which can be useful for the prevention and treatment of allergic symptoms, and the management of medical resources. The symptoms of Betula pollinosis can be associated with certain levels of pollen in the air. The aim of this study was to predict the risk of the concentration of pollen exceeding a given level, using previous pollen and meteorological information, by applying neural network techniques. Neural networks are a widespread statistical tool useful for the study of problems associated with complex or poorly understood phenomena. The binary response variable associated with each level requires a careful selection of the neural network and the error function associated with the learning algorithm used during the training phase. The performance of the neural network with the validation set showed that the risk of the pollen level exceeding a certain threshold can be successfully forecasted using artificial neural networks. This prediction tool may be implemented to create an automatic system that forecasts the risk of suffering allergic symptoms.

A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder risk.

PubMed

Chen, C L; Kaber, D B; Dempsey, P G

2000-06-01

A new and improved method to feedforward neural network (FNN) development for application to data classification problems, such as the prediction of levels of low-back disorder (LBD) risk associated with industrial jobs, is presented. Background on FNN development for data classification is provided along with discussions of previous research and neighborhood (local) solution search methods for hard combinatorial problems. An analytical study is presented which compared prediction accuracy of a FNN based on an error-back propagation (EBP) algorithm with the accuracy of a FNN developed by considering results of local solution search (simulated annealing) for classifying industrial jobs as posing low or high risk for LBDs. The comparison demonstrated superior performance of the FNN generated using the new method. The architecture of this FNN included fewer input (predictor) variables and hidden neurons than the FNN developed based on the EBP algorithm. Independent variable selection methods and the phenomenon of 'overfitting' in FNN (and statistical model) generation for data classification are discussed. The results are supportive of the use of the new approach to FNN development for applications to musculoskeletal disorders and risk forecasting in other domains.

Estimation of Separation Buffers for Wind-Prediction Error in an Airborne Separation Assistance System

NASA Technical Reports Server (NTRS)

Consiglio, Maria C.; Hoadley, Sherwood T.; Allen, B. Danette

2009-01-01

Wind prediction errors are known to affect the performance of automated air traffic management tools that rely on aircraft trajectory predictions. In particular, automated separation assurance tools, planned as part of the NextGen concept of operations, must be designed to account and compensate for the impact of wind prediction errors and other system uncertainties. In this paper we describe a high fidelity batch simulation study designed to estimate the separation distance required to compensate for the effects of wind-prediction errors throughout increasing traffic density on an airborne separation assistance system. These experimental runs are part of the Safety Performance of Airborne Separation experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assurance systems. In this experiment, wind-prediction errors were varied between zero and forty knots while traffic density was increased several times current traffic levels. In order to accurately measure the full unmitigated impact of wind-prediction errors, no uncertainty buffers were added to the separation minima. The goal of the study was to measure the impact of wind-prediction errors in order to estimate the additional separation buffers necessary to preserve separation and to provide a baseline for future analyses. Buffer estimations from this study will be used and verified in upcoming safety evaluation experiments under similar simulation conditions. Results suggest that the strategic airborne separation functions exercised in this experiment can sustain wind prediction errors up to 40kts at current day air traffic density with no additional separation distance buffer and at eight times the current day with no more than a 60% increase in separation distance buffer.

Artificial neural network implementation of a near-ideal error prediction controller

NASA Technical Reports Server (NTRS)

Mcvey, Eugene S.; Taylor, Lynore Denise

1992-01-01

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.

Sleep quality, but not quantity, is associated with self-perceived minor error rates among emergency department nurses.

PubMed

Weaver, Amy L; Stutzman, Sonja E; Supnet, Charlene; Olson, DaiWai M

2016-03-01

The emergency department (ED) is demanding and high risk. The impact of sleep quantity has been hypothesized to impact patient care. This study investigated the hypothesis that fatigue and impaired mentation, due to sleep disturbance and shortened overall sleeping hours, would lead to increased nursing errors. This is a prospective observational study of 30 ED nurses using self-administered survey and sleep architecture measured by wrist actigraphy as predictors of self-reported error rates. An actigraphy device was worn prior to working a 12-hour shift and nurses completed the Pittsburgh Sleep Quality Index (PSQI). Error rates were reported on a visual analog scale at the end of a 12-hour shift. The PSQI responses indicated that 73.3% of subjects had poor sleep quality. Lower sleep quality measured by actigraphy (hours asleep/hours in bed) was associated with higher self-perceived minor errors. Sleep quantity (total hours slept) was not associated with minor, moderate, nor severe errors. Our study found that ED nurses' sleep quality, immediately prior to a working 12-hour shift, is more predictive of error than sleep quantity. These results present evidence that a "good night's sleep" prior to working a nursing shift in the ED is beneficial for reducing minor errors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-reported and observed punitive parenting prospectively predicts increased error-related brain activity in six-year-old children

PubMed Central

Meyer, Alexandria; Proudfit, Greg Hajcak; Bufferd, Sara J.; Kujawa, Autumn J.; Laptook, Rebecca S.; Torpey, Dana C.; Klein, Daniel N.

2017-01-01

The error-related negativity (ERN) is a negative deflection in the event-related potential (ERP) occurring approximately 50 ms after error commission at fronto-central electrode sites and is thought to reflect the activation of a generic error monitoring system. Several studies have reported an increased ERN in clinically anxious children, and suggest that anxious children are more sensitive to error commission—although the mechanisms underlying this association are not clear. We have previously found that punishing errors results in a larger ERN, an effect that persists after punishment ends. It is possible that learning-related experiences that impact sensitivity to errors may lead to an increased ERN. In particular, punitive parenting might sensitize children to errors and increase their ERN. We tested this possibility in the current study by prospectively examining the relationship between parenting style during early childhood and children’s ERN approximately three years later. Initially, 295 parents and children (approximately 3 years old) participated in a structured observational measure of parenting behavior, and parents completed a self-report measure of parenting style. At a follow-up assessment approximately three years later, the ERN was elicited during a Go/No-Go task, and diagnostic interviews were completed with parents to assess child psychopathology. Results suggested that both observational measures of hostile parenting and self-report measures of authoritarian parenting style uniquely predicted a larger ERN in children 3 years later. We previously reported that children in this sample with anxiety disorders were characterized by an increased ERN. A mediation analysis indicated that ERN magnitude mediated the relationship between harsh parenting and child anxiety disorder. Results suggest that parenting may shape children’s error processing through environmental conditioning and thereby risk for anxiety, although future work is needed to confirm this hypothesis. PMID:25092483

An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting

NASA Astrophysics Data System (ADS)

Faramarzi, F.; Ebrahimi Farsangi, M. A.; Mansouri, H.

2013-07-01

Most blasting operations are associated with various forms of energy loss, emerging as environmental side effects of rock blasting, such as flyrock, vibration, airblast, and backbreak. Backbreak is an adverse phenomenon in rock blasting operations, which imposes risk and increases operation expenses because of safety reduction due to the instability of walls, poor fragmentation, and uneven burden in subsequent blasts. In this paper, based on the basic concepts of a rock engineering systems (RES) approach, a new model for the prediction of backbreak and the risk associated with a blast is presented. The newly suggested model involves 16 effective parameters on backbreak due to blasting, while retaining simplicity as well. The data for 30 blasts, carried out at Sungun copper mine, western Iran, were used to predict backbreak and the level of risk corresponding to each blast by the RES-based model. The results obtained were compared with the backbreak measured for each blast, which showed that the level of risk achieved is in consistence with the backbreak measured. The maximum level of risk [vulnerability index (VI) = 60] was associated with blast No. 2, for which the corresponding average backbreak was the highest achieved (9.25 m). Also, for blasts with levels of risk under 40, the minimum average backbreaks (<4 m) were observed. Furthermore, to evaluate the model performance for backbreak prediction, the coefficient of correlation ( R 2) and root mean square error (RMSE) of the model were calculated ( R 2 = 0.8; RMSE = 1.07), indicating the good performance of the model.

A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

PubMed Central

Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.

PubMed

Schoop, Rotraut; Beyersmann, Jan; Schumacher, Martin; Binder, Harald

2011-02-01

Prognostic models for time-to-event data play a prominent role in therapy assignment, risk stratification and inter-hospital quality assurance. The assessment of their prognostic value is vital not only for responsible resource allocation, but also for their widespread acceptance. The additional presence of competing risks to the event of interest requires proper handling not only on the model building side, but also during assessment. Research into methods for the evaluation of the prognostic potential of models accounting for competing risks is still needed, as most proposed methods measure either their discrimination or calibration, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (Statistics in Medicine 1999, 18, 2529–2545) and Gerds and Schumacher (Biometrical Journal 2006, 48, 1029–1040) to handle models with competing risks, i.e. more than one possible event type, and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows.

Model parameter-related optimal perturbations and their contributions to El Niño prediction errors

NASA Astrophysics Data System (ADS)

Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua

2018-04-01

Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

The prediction of satellite ephemeris errors as they result from surveillance system measurement errors

NASA Astrophysics Data System (ADS)

Simmons, B. E.

1981-08-01

This report derives equations predicting satellite ephemeris error as a function of measurement errors of space-surveillance sensors. These equations lend themselves to rapid computation with modest computer resources. They are applicable over prediction times such that measurement errors, rather than uncertainties of atmospheric drag and of Earth shape, dominate in producing ephemeris error. This report describes the specialization of these equations underlying the ANSER computer program, SEEM (Satellite Ephemeris Error Model). The intent is that this report be of utility to users of SEEM for interpretive purposes, and to computer programmers who may need a mathematical point of departure for limited generalization of SEEM.

Prediction error induced motor contagions in human behaviors.

PubMed

Ikegami, Tsuyoshi; Ganesh, Gowrishankar; Takeuchi, Tatsuya; Nakamoto, Hiroki

2018-05-29

Motor contagions refer to implicit effects on one's actions induced by observed actions. Motor contagions are believed to be induced simply by action observation and cause an observer's action to become similar to the action observed. In contrast, here we report a new motor contagion that is induced only when the observation is accompanied by prediction errors - differences between actions one observes and those he/she predicts or expects. In two experiments, one on whole-body baseball pitching and another on simple arm reaching, we show that the observation of the same action induces distinct motor contagions, depending on whether prediction errors are present or not. In the absence of prediction errors, as in previous reports, participants' actions changed to become similar to the observed action, while in the presence of prediction errors, their actions changed to diverge away from it, suggesting distinct effects of action observation and action prediction on human actions. © 2018, Ikegami et al.

Error sensitivity analysis in 10-30-day extended range forecasting by using a nonlinear cross-prediction error model

NASA Astrophysics Data System (ADS)

Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan

2017-06-01

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

Predictability of CFSv2 in the tropical Indo-Pacific region, at daily and subseasonal time scales

NASA Astrophysics Data System (ADS)

Krishnamurthy, V.

2018-06-01

The predictability of a coupled climate model is evaluated at daily and intraseasonal time scales in the tropical Indo-Pacific region during boreal summer and winter. This study has assessed the daily retrospective forecasts of the Climate Forecast System version 2 from the National Centers of Environmental Prediction for the period 1982-2010. The growth of errors in the forecasts of daily precipitation, monsoon intraseasonal oscillation (MISO) and the Madden-Julian oscillation (MJO) is studied. The seasonal cycle of the daily climatology of precipitation is reasonably well predicted except for the underestimation during the peak of summer. The anomalies follow the typical pattern of error growth in nonlinear systems and show no difference between summer and winter. The initial errors in all the cases are found to be in the nonlinear phase of the error growth. The doubling time of small errors is estimated by applying Lorenz error formula. For summer and winter, the doubling time of the forecast errors is in the range of 4-7 and 5-14 days while the doubling time of the predictability errors is 6-8 and 8-14 days, respectively. The doubling time in MISO during the summer and MJO during the winter is in the range of 12-14 days, indicating higher predictability and providing optimism for long-range prediction. There is no significant difference in the growth of forecasts errors originating from different phases of MISO and MJO, although the prediction of the active phase seems to be slightly better.

SIMulation of Medication Error induced by Clinical Trial drug labeling: the SIMME-CT study.

PubMed

Dollinger, Cecile; Schwiertz, Vérane; Sarfati, Laura; Gourc-Berthod, Chloé; Guédat, Marie-Gabrielle; Alloux, Céline; Vantard, Nicolas; Gauthier, Noémie; He, Sophie; Kiouris, Elena; Caffin, Anne-Gaelle; Bernard, Delphine; Ranchon, Florence; Rioufol, Catherine

2016-06-01

To assess the impact of investigational drug labels on the risk of medication error in drug dispensing. A simulation-based learning program focusing on investigational drug dispensing was conducted. The study was undertaken in an Investigational Drugs Dispensing Unit of a University Hospital of Lyon, France. Sixty-three pharmacy workers (pharmacists, residents, technicians or students) were enrolled. Ten risk factors were selected concerning label information or the risk of confusion with another clinical trial. Each risk factor was scored independently out of 5: the higher the score, the greater the risk of error. From 400 labels analyzed, two groups were selected for the dispensing simulation: 27 labels with high risk (score ≥3) and 27 with low risk (score ≤2). Each question in the learning program was displayed as a simulated clinical trial prescription. Medication error was defined as at least one erroneous answer (i.e. error in drug dispensing). For each question, response times were collected. High-risk investigational drug labels correlated with medication error and slower response time. Error rates were significantly 5.5-fold higher for high-risk series. Error frequency was not significantly affected by occupational category or experience in clinical trials. SIMME-CT is the first simulation-based learning tool to focus on investigational drug labels as a risk factor for medication error. SIMME-CT was also used as a training tool for staff involved in clinical research, to develop medication error risk awareness and to validate competence in continuing medical education. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

Risk-sensitive reinforcement learning.

PubMed

Shen, Yun; Tobia, Michael J; Sommer, Tobias; Obermayer, Klaus

2014-07-01

We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When appropriate utility functions are chosen, the agents' behaviors express key features of human behavior as predicted by prospect theory (Kahneman & Tversky, 1979 ), for example, different risk preferences for gains and losses, as well as the shape of subjective probability curves. We derive a risk-sensitive Q-learning algorithm, which is necessary for modeling human behavior when transition probabilities are unknown, and prove its convergence. As a proof of principle for the applicability of the new framework, we apply it to quantify human behavior in a sequential investment task. We find that the risk-sensitive variant provides a significantly better fit to the behavioral data and that it leads to an interpretation of the subject's responses that is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals shows a significant correlation of the risk-sensitive TD error with BOLD signal change in the ventral striatum. In addition we find a significant correlation of the risk-sensitive Q-values with neural activity in the striatum, cingulate cortex, and insula that is not present if standard Q-values are used.

When is an error not a prediction error? An electrophysiological investigation.

PubMed

Holroyd, Clay B; Krigolson, Olave E; Baker, Robert; Lee, Seung; Gibson, Jessica

2009-03-01

A recent theory holds that the anterior cingulate cortex (ACC) uses reinforcement learning signals conveyed by the midbrain dopamine system to facilitate flexible action selection. According to this position, the impact of reward prediction error signals on ACC modulates the amplitude of a component of the event-related brain potential called the error-related negativity (ERN). The theory predicts that ERN amplitude is monotonically related to the expectedness of the event: It is larger for unexpected outcomes than for expected outcomes. However, a recent failure to confirm this prediction has called the theory into question. In the present article, we investigated this discrepancy in three trial-and-error learning experiments. All three experiments provided support for the theory, but the effect sizes were largest when an optimal response strategy could actually be learned. This observation suggests that ACC utilizes dopamine reward prediction error signals for adaptive decision making when the optimal behavior is, in fact, learnable.

Quantification of LiDAR measurement uncertainty through propagation of errors due to sensor sub-systems and terrain morphology

NASA Astrophysics Data System (ADS)

Goulden, T.; Hopkinson, C.

2013-12-01

The quantification of LiDAR sensor measurement uncertainty is important for evaluating the quality of derived DEM products, compiling risk assessment of management decisions based from LiDAR information, and enhancing LiDAR mission planning capabilities. Current quality assurance estimates of LiDAR measurement uncertainty are limited to post-survey empirical assessments or vendor estimates from commercial literature. Empirical evidence can provide valuable information for the performance of the sensor in validated areas; however, it cannot characterize the spatial distribution of measurement uncertainty throughout the extensive coverage of typical LiDAR surveys. Vendor advertised error estimates are often restricted to strict and optimal survey conditions, resulting in idealized values. Numerical modeling of individual pulse uncertainty provides an alternative method for estimating LiDAR measurement uncertainty. LiDAR measurement uncertainty is theoretically assumed to fall into three distinct categories, 1) sensor sub-system errors, 2) terrain influences, and 3) vegetative influences. This research details the procedures for numerical modeling of measurement uncertainty from the sensor sub-system (GPS, IMU, laser scanner, laser ranger) and terrain influences. Results show that errors tend to increase as the laser scan angle, altitude or laser beam incidence angle increase. An experimental survey over a flat and paved runway site, performed with an Optech ALTM 3100 sensor, showed an increase in modeled vertical errors of 5 cm, at a nadir scan orientation, to 8 cm at scan edges; for an aircraft altitude of 1200 m and half scan angle of 15°. In a survey with the same sensor, at a highly sloped glacial basin site absent of vegetation, modeled vertical errors reached over 2 m. Validation of error models within the glacial environment, over three separate flight lines, respectively showed 100%, 85%, and 75% of elevation residuals fell below error predictions. Future work in LiDAR sensor measurement uncertainty must focus on the development of vegetative error models to create more robust error prediction algorithms. To achieve this objective, comprehensive empirical exploratory analysis is recommended to relate vegetative parameters to observed errors.

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies.

PubMed

Liu, Geng; Niu, Junjie; Zhang, Chao; Guo, Guanlin

2015-12-01

Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

PubMed

Ye, Min; Nagar, Swati; Korzekwa, Ken

2016-04-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500).

PubMed

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun; Seong, Gong Je

2017-03-01

To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R²=0.404). Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors.

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500)

PubMed Central

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun

2017-01-01

Purpose To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. Materials and Methods This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. Results In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R2=0.404). Conclusion Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors. PMID:28120576

Long-term orbit prediction for China's Tiangong-1 spacecraft based on mean atmosphere model

NASA Astrophysics Data System (ADS)

Tang, Jingshi; Liu, Lin; Miao, Manqian

Tiangong-1 is China's test module for future space station. It has gone through three successful rendezvous and dockings with Shenzhou spacecrafts from 2011 to 2013. For the long-term management and maintenance, the orbit sometimes needs to be predicted for a long period of time. As Tiangong-1 works in a low-Earth orbit with an altitude of about 300-400 km, the error in the a priori atmosphere model contributes significantly to the rapid increase of the predicted orbit error. When the orbit is predicted for 10-20 days, the error in the a priori atmosphere model, if not properly corrected, could induce the semi-major axis error and the overall position error up to a few kilometers and several thousand kilometers respectively. In this work, we use a mean atmosphere model averaged from NRLMSIS00. The a priori reference mean density can be corrected during precise orbit determination (POD). For applications in the long-term orbit prediction, the observations are first accumulated. With sufficiently long period of observations, we are able to obtain a series of the diurnal mean densities. This series bears the recent variation of the atmosphere density and can be analyzed for various periods. After being properly fitted, the mean density can be predicted and then applied in the orbit prediction. We show that the densities predicted with this approach can serve to increase the accuracy of the predicted orbit. In several 20-day prediction tests, most predicted orbits show semi-major axis errors better than 700m and overall position errors better than 600km.

Prediction of discretization error using the error transport equation

NASA Astrophysics Data System (ADS)

Celik, Ismail B.; Parsons, Don Roscoe

2017-06-01

This study focuses on an approach to quantify the discretization error associated with numerical solutions of partial differential equations by solving an error transport equation (ETE). The goal is to develop a method that can be used to adequately predict the discretization error using the numerical solution on only one grid/mesh. The primary problem associated with solving the ETE is the formulation of the error source term which is required for accurately predicting the transport of the error. In this study, a novel approach is considered which involves fitting the numerical solution with a series of locally smooth curves and then blending them together with a weighted spline approach. The result is a continuously differentiable analytic expression that can be used to determine the error source term. Once the source term has been developed, the ETE can easily be solved using the same solver that is used to obtain the original numerical solution. The new methodology is applied to the two-dimensional Navier-Stokes equations in the laminar flow regime. A simple unsteady flow case is also considered. The discretization error predictions based on the methodology presented in this study are in good agreement with the 'true error'. While in most cases the error predictions are not quite as accurate as those from Richardson extrapolation, the results are reasonable and only require one numerical grid. The current results indicate that there is much promise going forward with the newly developed error source term evaluation technique and the ETE.

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

A new approach to the characterization of subtle errors in everyday action: implications for mild cognitive impairment.

PubMed

Seligman, Sarah C; Giovannetti, Tania; Sestito, John; Libon, David J

2014-01-01

Mild functional difficulties have been associated with early cognitive decline in older adults and increased risk for conversion to dementia in mild cognitive impairment, but our understanding of this decline has been limited by a dearth of objective methods. This study evaluated the reliability and validity of a new system to code subtle errors on an established performance-based measure of everyday action and described preliminary findings within the context of a theoretical model of action disruption. Here 45 older adults completed the Naturalistic Action Test (NAT) and neuropsychological measures. NAT performance was coded for overt errors, and subtle action difficulties were scored using a novel coding system. An inter-rater reliability coefficient was calculated. Validity of the coding system was assessed using a repeated-measures ANOVA with NAT task (simple versus complex) and error type (overt versus subtle) as within-group factors. Correlation/regression analyses were conducted among overt NAT errors, subtle NAT errors, and neuropsychological variables. The coding of subtle action errors was reliable and valid, and episodic memory breakdown predicted subtle action disruption. Results suggest that the NAT can be useful in objectively assessing subtle functional decline. Treatments targeting episodic memory may be most effective in addressing early functional impairment in older age.

Estimating the impact of grouping misclassification on risk ...

EPA Pesticide Factsheets

Environmental health risk assessments of chemical mixtures that rely on component approaches often begin by grouping the chemicals of concern according to toxicological similarity. Approaches that assume dose addition typically are used for groups of similarly-acting chemicals and those that assume response addition are used for groups of independently acting chemicals. Grouping criteria for similarity can include a common adverse outcome pathway (AOP) and similarly shaped dose-response curves, with the latter used in the relative potency factor (RPF) method for estimating mixture response. Independence of toxic action is generally assumed if there is evidence that the chemicals act by different mechanisms. Several questions arise about the potential for misclassification error in the mixture risk prediction. If a common AOP has been established, how much error could there be if the same dose-response curve shape is assumed for all chemicals, when the shapes truly differ and, conversely, what is the error potential if different shapes are assumed when they are not? In particular, how do those concerns impact the choice of index chemical and uncertainty of the RPF-estimated mixture response? What is the quantitative impact if dose additivity is assumed when complete or partial independence actually holds and vice versa? These concepts and implications will be presented with numerical examples in the context of uncertainty of the RPF-estimated mixture response,

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Application of Exactly Linearized Error Transport Equations to AIAA CFD Prediction Workshops

NASA Technical Reports Server (NTRS)

Derlaga, Joseph M.; Park, Michael A.; Rallabhandi, Sriram

2017-01-01

The computational fluid dynamics (CFD) prediction workshops sponsored by the AIAA have created invaluable opportunities in which to discuss the predictive capabilities of CFD in areas in which it has struggled, e.g., cruise drag, high-lift, and sonic boom pre diction. While there are many factors that contribute to disagreement between simulated and experimental results, such as modeling or discretization error, quantifying the errors contained in a simulation is important for those who make decisions based on the computational results. The linearized error transport equations (ETE) combined with a truncation error estimate is a method to quantify one source of errors. The ETE are implemented with a complex-step method to provide an exact linearization with minimal source code modifications to CFD and multidisciplinary analysis methods. The equivalency of adjoint and linearized ETE functional error correction is demonstrated. Uniformly refined grids from a series of AIAA prediction workshops demonstrate the utility of ETE for multidisciplinary analysis with a connection between estimated discretization error and (resolved or under-resolved) flow features.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

For how long can we predict the weather? - Insights into atmospheric predictability from global convection-allowing simulations

NASA Astrophysics Data System (ADS)

Judt, Falko

2017-04-01

A tremendous increase in computing power has facilitated the advent of global convection-resolving numerical weather prediction (NWP) models. Although this technological breakthrough allows for the seamless prediction of weather from local to global scales, the predictability of multiscale weather phenomena in these models is not very well known. To address this issue, we conducted a global high-resolution (4-km) predictability experiment using the Model for Prediction Across Scales (MPAS), a state-of-the-art global NWP model developed at the National Center for Atmospheric Research. The goals of this experiment are to investigate error growth from convective to planetary scales and to quantify the intrinsic, scale-dependent predictability limits of atmospheric motions. The globally uniform resolution of 4 km allows for the explicit treatment of organized deep moist convection, alleviating grave limitations of previous predictability studies that either used high-resolution limited-area models or global simulations with coarser grids and cumulus parameterization. Error growth is analyzed within the context of an "identical twin" experiment setup: the error is defined as the difference between a 20-day long "nature run" and a simulation that was perturbed with small-amplitude noise, but is otherwise identical. It is found that in convectively active regions, errors grow by several orders of magnitude within the first 24 h ("super-exponential growth"). The errors then spread to larger scales and begin a phase of exponential growth after 2-3 days when contaminating the baroclinic zones. After 16 days, the globally averaged error saturates—suggesting that the intrinsic limit of atmospheric predictability (in a general sense) is about two weeks, which is in line with earlier estimates. However, error growth rates differ between the tropics and mid-latitudes as well as between the troposphere and stratosphere, highlighting that atmospheric predictability is a complex problem. The comparatively slower error growth in the tropics and in the stratosphere indicates that certain weather phenomena could potentially have longer predictability than currently thought.

Probability of criminal acts of violence: a test of jury predictive accuracy.

PubMed

Reidy, Thomas J; Sorensen, Jon R; Cunningham, Mark D

2013-01-01

The ability of capital juries to accurately predict future prison violence at the sentencing phase of aggravated murder trials was examined through retrospective review of the disciplinary records of 115 male inmates sentenced to either life (n = 65) or death (n = 50) in Oregon from 1985 through 2008, with a mean post-conviction time at risk of 15.3 years. Violent prison behavior was completely unrelated to predictions made by capital jurors, with bidirectional accuracy simply reflecting the base rate of assaultive misconduct in the group. Rejection of the special issue predicting future violence enjoyed 90% accuracy. Conversely, predictions that future violence was probable had 90% error rates. More than 90% of the assaultive rule violations committed by these offenders resulted in no harm or only minor injuries. Copyright © 2013 John Wiley & Sons, Ltd.

Artificial neural network classifier predicts neuroblastoma patients' outcome.

PubMed

Cangelosi, Davide; Pelassa, Simone; Morini, Martina; Conte, Massimo; Bosco, Maria Carla; Eva, Alessandra; Sementa, Angela Rita; Varesio, Luigi

2016-11-08

More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients' outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease. Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients' outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with "Poor" or "Good" outcome. We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients' outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors' set. The NB-hypo classifier predicted the patients' outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile demonstrated the hypoxic status of the tumor in patients with poor prognosis. We developed a robust classifier predicting neuroblastoma patients' outcome with a very low error rate and we provided independent evidence that the poor outcome patients had hypoxic tumors, supporting the potential of using hypoxia as target for neuroblastoma treatment.

Preschool speech error patterns predict articulation and phonological awareness outcomes in children with histories of speech sound disorders.

PubMed

Preston, Jonathan L; Hull, Margaret; Edwards, Mary Louise

2013-05-01

To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost 4 years later. Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 (years;months) and were followed up at age 8;3. The frequency of occurrence of preschool distortion errors, typical substitution and syllable structure errors, and atypical substitution and syllable structure errors was used to predict later speech sound production, PA, and literacy outcomes. Group averages revealed below-average school-age articulation scores and low-average PA but age-appropriate reading and spelling. Preschool speech error patterns were related to school-age outcomes. Children for whom >10% of their speech sound errors were atypical had lower PA and literacy scores at school age than children who produced <10% atypical errors. Preschoolers who produced more distortion errors were likely to have lower school-age articulation scores than preschoolers who produced fewer distortion errors. Different preschool speech error patterns predict different school-age clinical outcomes. Many atypical speech sound errors in preschoolers may be indicative of weak phonological representations, leading to long-term PA weaknesses. Preschoolers' distortions may be resistant to change over time, leading to persisting speech sound production problems.

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vedam, S.; Docef, A.; Fix, M.

2005-06-15

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effectsmore » of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.« less

Risk adjustment alternatives in paying for behavioral health care under Medicaid.

PubMed Central

Ettner, S L; Frank, R G; McGuire, T G; Hermann, R C

2001-01-01

OBJECTIVE: To compare the performance of various risk adjustment models in behavioral health applications such as setting mental health and substance abuse (MH/SA) capitation payments or overall capitation payments for populations including MH/SA users. DATA SOURCES/STUDY DESIGN: The 1991-93 administrative data from the Michigan Medicaid program were used. We compared mean absolute prediction error for several risk adjustment models and simulated the profits and losses that behavioral health care carve outs and integrated health plans would experience under risk adjustment if they enrolled beneficiaries with a history of MH/SA problems. Models included basic demographic adjustment, Adjusted Diagnostic Groups, Hierarchical Condition Categories, and specifications designed for behavioral health. PRINCIPAL FINDINGS: Differences in predictive ability among risk adjustment models were small and generally insignificant. Specifications based on relatively few MH/SA diagnostic categories did as well as or better than models controlling for additional variables such as medical diagnoses at predicting MH/SA expenditures among adults. Simulation analyses revealed that among both adults and minors considerable scope remained for behavioral health care carve outs to make profits or losses after risk adjustment based on differential enrollment of severely ill patients. Similarly, integrated health plans have strong financial incentives to avoid MH/SA users even after adjustment. CONCLUSIONS: Current risk adjustment methodologies do not eliminate the financial incentives for integrated health plans and behavioral health care carve-out plans to avoid high-utilizing patients with psychiatric disorders. PMID:11508640

Managing residual refractive error after cataract surgery.

PubMed

Sáles, Christopher S; Manche, Edward E

2015-06-01

We present a review of keratorefractive and intraocular approaches to managing residual astigmatic and spherical refractive error after cataract surgery, including laser in situ keratomileusis (LASIK), photorefractive keratectomy (PRK), arcuate keratotomy, intraocular lens (IOL) exchange, piggyback IOLs, and light-adjustable IOLs. Currently available literature suggests that laser vision correction, whether LASIK or PRK, yields more effective and predictable outcomes than intraocular surgery. Piggyback IOLs with a rounded-edge profile implanted in the sulcus may be superior to IOL exchange, but both options present potential risks that likely outweigh the refractive benefits except in cases with large residual spherical errors. The light-adjustable IOL may provide an ideal treatment to pseudophakic ametropia by obviating the need for secondary invasive procedures after cataract surgery, but it is not widely available nor has it been sufficiently studied. Copyright © 2015 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

An emission-weighted proximity model for air pollution exposure assessment.

PubMed

Zou, Bin; Wilson, J Gaines; Zhan, F Benjamin; Zeng, Yongnian

2009-08-15

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

A novel geometry-dosimetry label fusion method in multi-atlas segmentation for radiotherapy: a proof-of-concept study

NASA Astrophysics Data System (ADS)

Chang, Jina; Tian, Zhen; Lu, Weiguo; Gu, Xuejun; Chen, Mingli; Jiang, Steve B.

2017-05-01

Multi-atlas segmentation (MAS) has been widely used to automate the delineation of organs at risk (OARs) for radiotherapy. Label fusion is a crucial step in MAS to cope with the segmentation variabilities among multiple atlases. However, most existing label fusion methods do not consider the potential dosimetric impact of the segmentation result. In this proof-of-concept study, we propose a novel geometry-dosimetry label fusion method for MAS-based OAR auto-contouring, which evaluates the segmentation performance in terms of both geometric accuracy and the dosimetric impact of the segmentation accuracy on the resulting treatment plan. Differently from the original selective and iterative method for performance level estimation (SIMPLE), we evaluated and rejected the atlases based on both Dice similarity coefficient and the predicted error of the dosimetric endpoints. The dosimetric error was predicted using our previously developed geometry-dosimetry model. We tested our method in MAS-based rectum auto-contouring on 20 prostate cancer patients. The accuracy in the rectum sub-volume close to the planning tumor volume (PTV), which was found to be a dosimetric sensitive region of the rectum, was greatly improved. The mean absolute distance between the obtained contour and the physician-drawn contour in the rectum sub-volume 2 mm away from PTV was reduced from 3.96 mm to 3.36 mm on average for the 20 patients, with the maximum decrease found to be from 9.22 mm to 3.75 mm. We also compared the dosimetric endpoints predicted for the obtained contours with those predicted for the physician-drawn contours. Our method led to smaller dosimetric endpoint errors than the SIMPLE method in 15 patients, comparable errors in 2 patients, and slightly larger errors in 3 patients. These results indicated the efficacy of our method in terms of considering both geometric accuracy and dosimetric impact during label fusion. Our algorithm can be applied to different tumor sites and radiation treatments, given a specifically trained geometry-dosimetry model.

A novel geometry-dosimetry label fusion method in multi-atlas segmentation for radiotherapy: a proof-of-concept study.

PubMed

Chang, Jina; Tian, Zhen; Lu, Weiguo; Gu, Xuejun; Chen, Mingli; Jiang, Steve B

2017-05-07

Multi-atlas segmentation (MAS) has been widely used to automate the delineation of organs at risk (OARs) for radiotherapy. Label fusion is a crucial step in MAS to cope with the segmentation variabilities among multiple atlases. However, most existing label fusion methods do not consider the potential dosimetric impact of the segmentation result. In this proof-of-concept study, we propose a novel geometry-dosimetry label fusion method for MAS-based OAR auto-contouring, which evaluates the segmentation performance in terms of both geometric accuracy and the dosimetric impact of the segmentation accuracy on the resulting treatment plan. Differently from the original selective and iterative method for performance level estimation (SIMPLE), we evaluated and rejected the atlases based on both Dice similarity coefficient and the predicted error of the dosimetric endpoints. The dosimetric error was predicted using our previously developed geometry-dosimetry model. We tested our method in MAS-based rectum auto-contouring on 20 prostate cancer patients. The accuracy in the rectum sub-volume close to the planning tumor volume (PTV), which was found to be a dosimetric sensitive region of the rectum, was greatly improved. The mean absolute distance between the obtained contour and the physician-drawn contour in the rectum sub-volume 2 mm away from PTV was reduced from 3.96 mm to 3.36 mm on average for the 20 patients, with the maximum decrease found to be from 9.22 mm to 3.75 mm. We also compared the dosimetric endpoints predicted for the obtained contours with those predicted for the physician-drawn contours. Our method led to smaller dosimetric endpoint errors than the SIMPLE method in 15 patients, comparable errors in 2 patients, and slightly larger errors in 3 patients. These results indicated the efficacy of our method in terms of considering both geometric accuracy and dosimetric impact during label fusion. Our algorithm can be applied to different tumor sites and radiation treatments, given a specifically trained geometry-dosimetry model.

The effectiveness of risk management program on pediatric nurses' medication error.

PubMed

Dehghan-Nayeri, Nahid; Bayat, Fariba; Salehi, Tahmineh; Faghihzadeh, Soghrat

2013-09-01

Medication therapy is one of the most complex and high-risk clinical processes that nurses deal with. Medication error is the most common type of error that brings about damage and death to patients, especially pediatric ones. However, these errors are preventable. Identifying and preventing undesirable events leading to medication errors are the main risk management activities. The aim of this study was to investigate the effectiveness of a risk management program on the pediatric nurses' medication error rate. This study is a quasi-experimental one with a comparison group. In this study, 200 nurses were recruited from two main pediatric hospitals in Tehran. In the experimental hospital, we applied the risk management program for a period of 6 months. Nurses of the control hospital did the hospital routine schedule. A pre- and post-test was performed to measure the frequency of the medication error events. SPSS software, t-test, and regression analysis were used for data analysis. After the intervention, the medication error rate of nurses at the experimental hospital was significantly lower (P < 0.001) and the error-reporting rate was higher (P < 0.007) compared to before the intervention and also in comparison to the nurses of the control hospital. Based on the results of this study and taking into account the high-risk nature of the medical environment, applying the quality-control programs such as risk management can effectively prevent the occurrence of the hospital undesirable events. Nursing mangers can reduce the medication error rate by applying risk management programs. However, this program cannot succeed without nurses' cooperation.

Modelling the spatial distribution of the nuisance mosquito species Anopheles plumbeus (Diptera: Culicidae) in the Netherlands.

PubMed

Ibañez-Justicia, Adolfo; Cianci, Daniela

2015-05-01

Landscape modifications, urbanization or changes of use of rural-agricultural areas can create more favourable conditions for certain mosquito species and therefore indirectly cause nuisance problems for humans. This could potentially result in mosquito-borne disease outbreaks when the nuisance is caused by mosquito species that can transmit pathogens. Anopheles plumbeus is a nuisance mosquito species and a potential malaria vector. It is one of the most frequently observed species in the Netherlands. Information on the distribution of this species is essential for risk assessments. The purpose of the study was to investigate the potential spatial distribution of An. plumbeus in the Netherlands. Random forest models were used to link the occurrence and the abundance of An. plumbeus with environmental features and to produce distribution maps in the Netherlands. Mosquito data were collected using a cross-sectional study design in the Netherlands, from April to October 2010-2013. The environmental data were obtained from satellite imagery and weather stations. Statistical measures (accuracy for the occurrence model and mean squared error for the abundance model) were used to evaluate the models performance. The models were externally validated. The maps show that forested areas (centre of the Netherlands) and the east of the country were predicted as suitable for An. plumbeus. In particular high suitability and high abundance was predicted in the south-eastern provinces Limburg and North Brabant. Elevation, precipitation, day and night temperature and vegetation indices were important predictors for calculating the probability of occurrence for An. plumbeus. The probability of occurrence, vegetation indices and precipitation were important for predicting its abundance. The AUC value was 0.73 and the error in the validation was 0.29; the mean squared error value was 0.12. The areas identified by the model as suitable and with high abundance of An. plumbeus, are consistent with the areas from which nuisance was reported. Our results can be helpful in the assessment of vector-borne disease risk.

A predictive model of nuclear power plant crew decision-making and performance in a dynamic simulation environment

NASA Astrophysics Data System (ADS)

Coyne, Kevin Anthony

The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional branching events and provide a better representation of the IDAC cognitive model. An operator decision-making engine capable of responding to dynamic changes in situational context was implemented. The IDAC human performance model was fully integrated with a detailed nuclear plant model in order to realistically simulate plant accident scenarios. Finally, the improved ADS-IDAC model was calibrated, validated, and updated using actual nuclear plant crew performance data. This research led to the following general conclusions: (1) A relatively small number of branching rules are capable of efficiently capturing a wide spectrum of crew-to-crew variabilities. (2) Compared to traditional static risk assessment methods, ADS-IDAC can provide a more realistic and integrated assessment of human error events by directly determining the effect of operator behaviors on plant thermal hydraulic parameters. (3) The ADS-IDAC approach provides an efficient framework for capturing actual operator performance data such as timing of operator actions, mental models, and decision-making activities.

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

PubMed

Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De

2016-01-01

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew

Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount ofmore » uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.« less

Mismeasurement and the resonance of strong confounders: correlated errors.

PubMed

Marshall, J R; Hastrup, J L; Ross, J S

1999-07-01

Confounding in epidemiology, and the limits of standard methods of control for an imperfectly measured confounder, have been understood for some time. However, most treatments of this problem are based on the assumption that errors of measurement in confounding and confounded variables are independent. This paper considers the situation in which a strong risk factor (confounder) and an inconsequential but suspected risk factor (confounded) are each measured with errors that are correlated; the situation appears especially likely to occur in the field of nutritional epidemiology. Error correlation appears to add little to measurement error as a source of bias in estimating the impact of a strong risk factor: it can add to, diminish, or reverse the bias induced by measurement error in estimating the impact of the inconsequential risk factor. Correlation of measurement errors can add to the difficulty involved in evaluating structures in which confounding and measurement error are present. In its presence, observed correlations among risk factors can be greater than, less than, or even opposite to the true correlations. Interpretation of multivariate epidemiologic structures in which confounding is likely requires evaluation of measurement error structures, including correlations among measurement errors.

Development of Rock Engineering Systems-Based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting

NASA Astrophysics Data System (ADS)

Faramarzi, Farhad; Mansouri, Hamid; Farsangi, Mohammad Ali Ebrahimi

2014-07-01

The environmental effects of blasting must be controlled in order to comply with regulatory limits. Because of safety concerns and risk of damage to infrastructures, equipment, and property, and also having a good fragmentation, flyrock control is crucial in blasting operations. If measures to decrease flyrock are taken, then the flyrock distance would be limited, and, in return, the risk of damage can be reduced or eliminated. This paper deals with modeling the level of risk associated with flyrock and, also, flyrock distance prediction based on the rock engineering systems (RES) methodology. In the proposed models, 13 effective parameters on flyrock due to blasting are considered as inputs, and the flyrock distance and associated level of risks as outputs. In selecting input data, the simplicity of measuring input data was taken into account as well. The data for 47 blasts, carried out at the Sungun copper mine, western Iran, were used to predict the level of risk and flyrock distance corresponding to each blast. The obtained results showed that, for the 47 blasts carried out at the Sungun copper mine, the level of estimated risks are mostly in accordance with the measured flyrock distances. Furthermore, a comparison was made between the results of the flyrock distance predictive RES-based model, the multivariate regression analysis model (MVRM), and, also, the dimensional analysis model. For the RES-based model, R 2 and root mean square error (RMSE) are equal to 0.86 and 10.01, respectively, whereas for the MVRM and dimensional analysis, R 2 and RMSE are equal to (0.84 and 12.20) and (0.76 and 13.75), respectively. These achievements confirm the better performance of the RES-based model over the other proposed models.

Influences on emergency department length of stay for older people.

PubMed

Street, Maryann; Mohebbi, Mohammadreza; Berry, Debra; Cross, Anthony; Considine, Julie

2018-02-14

The aim of this study was to examine the influences on emergency department (ED) length of stay (LOS) for older people and develop a predictive model for an ED LOS more than 4 h. This retrospective cohort study used organizational data linkage at the patient level from a major Australian health service. The study population was aged 65 years or older, attending an ED during the 2013/2014 financial year. We developed and internally validated a clinical prediction rule. Discriminatory performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. An integer-based risk score was developed using multivariate logistic regression. The risk score was evaluated using ROC analysis. There were 33 926 ED attendances: 57.5% (n=19 517) had an ED LOS more than 4 h. The area under ROC for age, usual accommodation, triage category, arrival by ambulance, arrival overnight, imaging, laboratory investigations, overcrowding, time to be seen by doctor, ED visits with admission and access block relating to ED LOS more than 4 h was 0.796, indicating good performance. In the validation set, area under ROC was 0.80, P-value was 0.36 and prediction mean square error was 0.18, indicating good calibration. The risk score value attributed to each risk factor ranged from 2 to 68 points. The clinical prediction rule stratified patients into five levels of risk on the basis of the total risk score. Objective identification of older people at intermediate and high risk of an ED LOS more than 4 h early in ED care enables targeted approaches to streamline the patient journey, decrease ED LOS and optimize emergency care for older people.

APPLICATION OF BAYESIAN AND GEOSTATISTICAL MODELING TO THE ENVIRONMENTAL MONITORING OF CS-137 AT THE IDAHO NATIONAL LABORATORY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kara G. Eby

2010-08-01

At the Idaho National Laboratory (INL) Cs-137 concentrations above the U.S. Environmental Protection Agency risk-based threshold of 0.23 pCi/g may increase the risk of human mortality due to cancer. As a leader in nuclear research, the INL has been conducting nuclear activities for decades. Elevated anthropogenic radionuclide levels including Cs-137 are a result of atmospheric weapons testing, the Chernobyl accident, and nuclear activities occurring at the INL site. Therefore environmental monitoring and long-term surveillance of Cs-137 is required to evaluate risk. However, due to the large land area involved, frequent and comprehensive monitoring is limited. Developing a spatial model thatmore » predicts Cs-137 concentrations at unsampled locations will enhance the spatial characterization of Cs-137 in surface soils, provide guidance for an efficient monitoring program, and pinpoint areas requiring mitigation strategies. The predictive model presented herein is based on applied geostatistics using a Bayesian analysis of environmental characteristics across the INL site, which provides kriging spatial maps of both Cs-137 estimates and prediction errors. Comparisons are presented of two different kriging methods, showing that the use of secondary information (i.e., environmental characteristics) can provide improved prediction performance in some areas of the INL site.« less

Error disclosure: a new domain for safety culture assessment.

PubMed

Etchegaray, Jason M; Gallagher, Thomas H; Bell, Sigall K; Dunlap, Ben; Thomas, Eric J

2012-07-01

To (1) develop and test survey items that measure error disclosure culture, (2) examine relationships among error disclosure culture, teamwork culture and safety culture and (3) establish predictive validity for survey items measuring error disclosure culture. All clinical faculty from six health institutions (four medical schools, one cancer centre and one health science centre) in The University of Texas System were invited to anonymously complete an electronic survey containing questions about safety culture and error disclosure. The authors found two factors to measure error disclosure culture: one factor is focused on the general culture of error disclosure and the second factor is focused on trust. Both error disclosure culture factors were unique from safety culture and teamwork culture (correlations were less than r=0.85). Also, error disclosure general culture and error disclosure trust culture predicted intent to disclose a hypothetical error to a patient (r=0.25, p<0.001 and r=0.16, p<0.001, respectively) while teamwork and safety culture did not predict such an intent (r=0.09, p=NS and r=0.12, p=NS). Those who received prior error disclosure training reported significantly higher levels of error disclosure general culture (t=3.7, p<0.05) and error disclosure trust culture (t=2.9, p<0.05). The authors created and validated a new measure of error disclosure culture that predicts intent to disclose an error better than other measures of healthcare culture. This measure fills an existing gap in organisational assessments by assessing transparent communication after medical error, an important aspect of culture.

Exposure measurement error in PM2.5 health effects studies: A pooled analysis of eight personal exposure validation studies

PubMed Central

2014-01-01

Background Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures. Methods Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects’ homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models. Results When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5. Conclusions Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies. PMID:24410940

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

NASA Technical Reports Server (NTRS)

Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

2013-01-01

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

Statistical analysis of modeling error in structural dynamic systems

NASA Technical Reports Server (NTRS)

Hasselman, T. K.; Chrostowski, J. D.

1990-01-01

The paper presents a generic statistical model of the (total) modeling error for conventional space structures in their launch configuration. Modeling error is defined as the difference between analytical prediction and experimental measurement. It is represented by the differences between predicted and measured real eigenvalues and eigenvectors. Comparisons are made between pre-test and post-test models. Total modeling error is then subdivided into measurement error, experimental error and 'pure' modeling error, and comparisons made between measurement error and total modeling error. The generic statistical model presented in this paper is based on the first four global (primary structure) modes of four different structures belonging to the generic category of Conventional Space Structures (specifically excluding large truss-type space structures). As such, it may be used to evaluate the uncertainty of predicted mode shapes and frequencies, sinusoidal response, or the transient response of other structures belonging to the same generic category.

Wind Power predictability a risk factor in the design, construction and operation of Wind Generation Turbines

NASA Astrophysics Data System (ADS)

Thiesen, J.; Gulstad, L.; Ristic, I.; Maric, T.

2010-09-01

Summit: The wind power predictability is often a forgotten decision and planning factor for most major wind parks, both onshore and offshore. The results of the predictability are presented after having examined a number of European offshore and offshore parks power predictability by using three(3) mesoscale model IRIE_GFS and IRIE_EC and WRF. Full description: It is well known that the potential wind production is changing with latitude and complexity in terrain, but how big are the changes in the predictability and the economic impacts on a project? The concept of meteorological predictability has hitherto to some degree been neglected as a risk factor in the design, construction and operation of wind power plants. Wind power plants are generally built in places where the wind resources are high, but these are often also sites where the predictability of the wind and other weather parameters is comparatively low. This presentation addresses the question of whether higher predictability can outweigh lower average wind speeds with regard to the overall economy of a wind power project. Low predictability also tends to reduce the value of the energy produced. If it is difficult to forecast the wind on a site, it will also be difficult to predict the power production. This, in turn, leads to increased balance costs and a less reduced carbon emission from the renewable source. By investigating the output from three(3) mesoscale models IRIE and WRF, using ECMWF and GFS as boundary data over a forecasting period of 3 months for 25 offshore and onshore wind parks in Europe, the predictability are mapped. Three operational mesoscale models with two different boundary data have been chosen in order to eliminate the uncertainty with one mesoscale model. All mesoscale models are running in a 10 km horizontal resolution. The model output are converted into "day a head" wind turbine generation forecasts by using a well proven advanced physical wind power model. The power models are using a number of weather parameters like wind speed in different heights, friction velocity and DTHV. The 25 wind sites are scattered around in Europe and contains 4 offshore parks and 21 onshore parks in various terrain complexity. The "day a head" forecasts are compared with production data and predictability for the period February 2010-April 2010 are given in Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE). The power predictability results are mapped for each turbine giving a clear picture of the predictability in Europe. . Finally a economic analysis are shown for each wind parks in different regimes of predictability will be compared with regard to the balance costs that result from errors in the wind power prediction. Analysis shows that it may very well be profitable to place wind parks in regions of lower, but more predictable wind ressource. Authors: Ivan Ristic, CTO Weather2Umberlla D.O.O Tomislav Maric, Meteorologist at Global Flow Solutions Vestas Wind Technology R&D Line Gulstad, Manager Global Flow Solutions Vestas Wind Technology R&D Jesper Thiesen, CEO ConWx ApS

Error management for musicians: an interdisciplinary conceptual framework

PubMed Central

Kruse-Weber, Silke; Parncutt, Richard

2014-01-01

Musicians tend to strive for flawless performance and perfection, avoiding errors at all costs. Dealing with errors while practicing or performing is often frustrating and can lead to anger and despair, which can explain musicians’ generally negative attitude toward errors and the tendency to aim for flawless learning in instrumental music education. But even the best performances are rarely error-free, and research in general pedagogy and psychology has shown that errors provide useful information for the learning process. Research in instrumental pedagogy is still neglecting error issues; the benefits of risk management (before the error) and error management (during and after the error) are still underestimated. It follows that dealing with errors is a key aspect of music practice at home, teaching, and performance in public. And yet, to be innovative, or to make their performance extraordinary, musicians need to risk errors. Currently, most music students only acquire the ability to manage errors implicitly – or not at all. A more constructive, creative, and differentiated culture of errors would balance error tolerance and risk-taking against error prevention in ways that enhance music practice and music performance. The teaching environment should lay the foundation for the development of such an approach. In this contribution, we survey recent research in aviation, medicine, economics, psychology, and interdisciplinary decision theory that has demonstrated that specific error-management training can promote metacognitive skills that lead to better adaptive transfer and better performance skills. We summarize how this research can be applied to music, and survey-relevant research that is specifically tailored to the needs of musicians, including generic guidelines for risk and error management in music teaching and performance. On this basis, we develop a conceptual framework for risk management that can provide orientation for further music education and musicians at all levels. PMID:25120501

Error management for musicians: an interdisciplinary conceptual framework.

PubMed

Kruse-Weber, Silke; Parncutt, Richard

2014-01-01

Musicians tend to strive for flawless performance and perfection, avoiding errors at all costs. Dealing with errors while practicing or performing is often frustrating and can lead to anger and despair, which can explain musicians' generally negative attitude toward errors and the tendency to aim for flawless learning in instrumental music education. But even the best performances are rarely error-free, and research in general pedagogy and psychology has shown that errors provide useful information for the learning process. Research in instrumental pedagogy is still neglecting error issues; the benefits of risk management (before the error) and error management (during and after the error) are still underestimated. It follows that dealing with errors is a key aspect of music practice at home, teaching, and performance in public. And yet, to be innovative, or to make their performance extraordinary, musicians need to risk errors. Currently, most music students only acquire the ability to manage errors implicitly - or not at all. A more constructive, creative, and differentiated culture of errors would balance error tolerance and risk-taking against error prevention in ways that enhance music practice and music performance. The teaching environment should lay the foundation for the development of such an approach. In this contribution, we survey recent research in aviation, medicine, economics, psychology, and interdisciplinary decision theory that has demonstrated that specific error-management training can promote metacognitive skills that lead to better adaptive transfer and better performance skills. We summarize how this research can be applied to music, and survey-relevant research that is specifically tailored to the needs of musicians, including generic guidelines for risk and error management in music teaching and performance. On this basis, we develop a conceptual framework for risk management that can provide orientation for further music education and musicians at all levels.

Preschool speech error patterns predict articulation and phonological awareness outcomes in children with histories of speech sound disorders

PubMed Central

Preston, Jonathan L.; Hull, Margaret; Edwards, Mary Louise

2012-01-01

Purpose To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost four years later. Method Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 and followed up at 8;3. The frequency of occurrence of preschool distortion errors, typical substitution and syllable structure errors, and atypical substitution and syllable structure errors were used to predict later speech sound production, PA, and literacy outcomes. Results Group averages revealed below-average school-age articulation scores and low-average PA, but age-appropriate reading and spelling. Preschool speech error patterns were related to school-age outcomes. Children for whom more than 10% of their speech sound errors were atypical had lower PA and literacy scores at school-age than children who produced fewer than 10% atypical errors. Preschoolers who produced more distortion errors were likely to have lower school-age articulation scores. Conclusions Different preschool speech error patterns predict different school-age clinical outcomes. Many atypical speech sound errors in preschool may be indicative of weak phonological representations, leading to long-term PA weaknesses. Preschool distortions may be resistant to change over time, leading to persisting speech sound production problems. PMID:23184137

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

PubMed Central

Hermes, Helen E.; Teutonico, Donato; Preuss, Thomas G.; Schneckener, Sebastian

2018-01-01

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations. PMID:29561908

Real-time prediction and gating of respiratory motion in 3D space using extended Kalman filters and Gaussian process regression network

NASA Astrophysics Data System (ADS)

Bukhari, W.; Hong, S.-M.

2016-03-01

The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient’s breathing cycle. The algorithm, named EKF-GPRN+ , first employs an extended Kalman filter (EKF) independently along each coordinate to predict the respiratory motion and then uses a Gaussian process regression network (GPRN) to correct the prediction error of the EKF in 3D space. The GPRN is a nonparametric Bayesian algorithm for modeling input-dependent correlations between the output variables in multi-output regression. Inference in GPRN is intractable and we employ variational inference with mean field approximation to compute an approximate predictive mean and predictive covariance matrix. The approximate predictive mean is used to correct the prediction error of the EKF. The trace of the approximate predictive covariance matrix is utilized to capture the uncertainty in EKF-GPRN+ prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification enables us to pause the treatment beam over such instances. EKF-GPRN+ implements a gating function by using simple calculations based on the trace of the predictive covariance matrix. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPRN+ . The experimental results show that the EKF-GPRN+ algorithm reduces the patient-wise prediction error to 38%, 40% and 40% in root-mean-square, compared to no prediction, at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The EKF-GPRN+ algorithm can further reduce the prediction error by employing the gating function, albeit at the cost of reduced duty cycle. The error reduction allows the clinical target volume to planning target volume (CTV-PTV) margin to be reduced, leading to decreased normal-tissue toxicity and possible dose escalation. The CTV-PTV margin is also evaluated to quantify clinical benefits of EKF-GPRN+ prediction.

TOPEX/POSEIDON orbit maintenance maneuver design

NASA Technical Reports Server (NTRS)

Bhat, R. S.; Frauenholz, R. B.; Cannell, Patrick E.

1990-01-01

The Ocean Topography Experiment (TOPEX/POSEIDON) mission orbit requirements are outlined, as well as its control and maneuver spacing requirements including longitude and time targeting. A ground-track prediction model dealing with geopotential, luni-solar gravity, and atmospheric-drag perturbations is considered. Targeting with all modeled perturbations is discussed, and such ground-track prediction errors as initial semimajor axis, orbit-determination, maneuver-execution, and atmospheric-density modeling errors are assessed. A longitude targeting strategy for two extreme situations is investigated employing all modeled perturbations and prediction errors. It is concluded that atmospheric-drag modeling errors are the prevailing ground-track prediction error source early in the mission during high solar flux, and that low solar-flux levels expected late in the experiment stipulate smaller maneuver magnitudes.

The impact of experimental measurement errors on long-term viscoelastic predictions. [of structural materials

NASA Technical Reports Server (NTRS)

Tuttle, M. E.; Brinson, H. F.

1986-01-01

The impact of flight error in measured viscoelastic parameters on subsequent long-term viscoelastic predictions is numerically evaluated using the Schapery nonlinear viscoelastic model. Of the seven Schapery parameters, the results indicated that long-term predictions were most sensitive to errors in the power law parameter n. Although errors in the other parameters were significant as well, errors in n dominated all other factors at long times. The process of selecting an appropriate short-term test cycle so as to insure an accurate long-term prediction was considered, and a short-term test cycle was selected using material properties typical for T300/5208 graphite-epoxy at 149 C. The process of selection is described, and its individual steps are itemized.

Comparison of various error functions in predicting the optimum isotherm by linear and non-linear regression analysis for the sorption of basic red 9 by activated carbon.

PubMed

Kumar, K Vasanth; Porkodi, K; Rocha, F

2008-01-15

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.

How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

PubMed

Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

2014-03-01

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

Performance of statistical models to predict mental health and substance abuse cost.

PubMed

Montez-Rath, Maria; Christiansen, Cindy L; Ettner, Susan L; Loveland, Susan; Rosen, Amy K

2006-10-26

Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS) models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice. Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH) or substance abuse (SA) diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS); Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample. The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples. Models with square-root transformation or link fit the data best. This function (whether used as transformation or as a link) seems to help deal with the high comorbidity of this population by introducing a form of interaction. The Gamma distribution helps with the long tail of the distribution. However, the Normal distribution is suitable if the correct transformation of the outcome is used.

Does the sensorimotor system minimize prediction error or select the most likely prediction during object lifting?

PubMed Central

McGregor, Heather R.; Pun, Henry C. H.; Buckingham, Gavin; Gribble, Paul L.

2016-01-01

The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy. NEW & NOTEWORTHY Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems. PMID:27760821

Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

PubMed

Holmes, John B; Dodds, Ken G; Lee, Michael A

2017-03-02

An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

Medial-Frontal Stimulation Enhances Learning in Schizophrenia by Restoring Prediction Error Signaling.

PubMed

Reinhart, Robert M G; Zhu, Julia; Park, Sohee; Woodman, Geoffrey F

2015-09-02

Posterror learning, associated with medial-frontal cortical recruitment in healthy subjects, is compromised in neuropsychiatric disorders. Here we report novel evidence for the mechanisms underlying learning dysfunctions in schizophrenia. We show that, by noninvasively passing direct current through human medial-frontal cortex, we could enhance the event-related potential related to learning from mistakes (i.e., the error-related negativity), a putative index of prediction error signaling in the brain. Following this causal manipulation of brain activity, the patients learned a new task at a rate that was indistinguishable from healthy individuals. Moreover, the severity of delusions interacted with the efficacy of the stimulation to improve learning. Our results demonstrate a causal link between disrupted prediction error signaling and inefficient learning in schizophrenia. These findings also demonstrate the feasibility of nonpharmacological interventions to address cognitive deficits in neuropsychiatric disorders. When there is a difference between what we expect to happen and what we actually experience, our brains generate a prediction error signal, so that we can map stimuli to responses and predict outcomes accurately. Theories of schizophrenia implicate abnormal prediction error signaling in the cognitive deficits of the disorder. Here, we combine noninvasive brain stimulation with large-scale electrophysiological recordings to establish a causal link between faulty prediction error signaling and learning deficits in schizophrenia. We show that it is possible to improve learning rate, as well as the neural signature of prediction error signaling, in patients to a level quantitatively indistinguishable from that of healthy subjects. The results provide mechanistic insight into schizophrenia pathophysiology and suggest a future therapy for this condition. Copyright © 2015 the authors 0270-6474/15/3512232-09$15.00/0.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa.

PubMed

DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W

2017-06-01

Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa

PubMed Central

DeGuzman, Marisa; Shott, Megan E.; Yang, Tony T.; Riederer, Justin; Frank, Guido K.W.

2017-01-01

Objective Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Method Female adolescents with anorexia nervosa (N=21; mean age, 15.2 years [SD=2.4]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 16.4 years [SD=1.9]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Results Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Conclusions Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs. PMID:28231717

Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps

PubMed Central

Goovaerts, Pierre

2006-01-01

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first used to estimate the risk value and the associated standard error within each polygon, accounting for the population size and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between polygons (i.e. boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes the proportion of type I errors (i.e. edges wrongly declared significant) while the frequency of these errors is predicted well by the p-value of the statistical test. PMID:19023455

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

False consensus and adolescent peer contagion: examining discrepancies between perceptions and actual reported levels of friends' deviant and health risk behaviors.

PubMed

Prinstein, Mitchell J; Wang, Shirley S

2005-06-01

Adolescents' perceptions of their friends' behavior strongly predict adolescents' own behavior, however, these perceptions often are erroneous. This study examined correlates of discrepancies between adolescents' perceptions and friends' reports of behavior. A total of 120 11th-grade adolescents provided data regarding their engagement in deviant and health risk behaviors, as well as their perceptions of the behavior of their best friend, as identified through sociometric assessment. Data from friends' own report were used to calculate discrepancy measures of adolescents' overestimations and estimation errors (absolute value of discrepancies) of friends' behavior. Adolescents also completed a measure of friendship quality, and a sociometric assessment yielding measures of peer acceptance/rejection and aggression. Findings revealed that adolescents' peer rejection and aggression were associated with greater overestimations of friends' behavior. This effect was partially mediated by adolescents' own behavior, consistent with a false consensus effect. Low levels of positive friendship quality were significantly associated with estimation errors, but not overestimations specifically.

Effect of correlated observation error on parameters, predictions, and uncertainty

USGS Publications Warehouse

Tiedeman, Claire; Green, Christopher T.

2013-01-01

Correlations among observation errors are typically omitted when calculating observation weights for model calibration by inverse methods. We explore the effects of omitting these correlations on estimates of parameters, predictions, and uncertainties. First, we develop a new analytical expression for the difference in parameter variance estimated with and without error correlations for a simple one-parameter two-observation inverse model. Results indicate that omitting error correlations from both the weight matrix and the variance calculation can either increase or decrease the parameter variance, depending on the values of error correlation (ρ) and the ratio of dimensionless scaled sensitivities (rdss). For small ρ, the difference in variance is always small, but for large ρ, the difference varies widely depending on the sign and magnitude of rdss. Next, we consider a groundwater reactive transport model of denitrification with four parameters and correlated geochemical observation errors that are computed by an error-propagation approach that is new for hydrogeologic studies. We compare parameter estimates, predictions, and uncertainties obtained with and without the error correlations. Omitting the correlations modestly to substantially changes parameter estimates, and causes both increases and decreases of parameter variances, consistent with the analytical expression. Differences in predictions for the models calibrated with and without error correlations can be greater than parameter differences when both are considered relative to their respective confidence intervals. These results indicate that including observation error correlations in weighting for nonlinear regression can have important effects on parameter estimates, predictions, and their respective uncertainties.

Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

PubMed

García-García, Isabel; Zeighami, Yashar; Dagher, Alain

2017-06-01

Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

Using the weighted area under the net benefit curve for decision curve analysis.

PubMed

Talluri, Rajesh; Shete, Sanjay

2016-07-18

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.

Management of high-risk perioperative systems.

PubMed

Dain, Steven

2006-06-01

The perioperative system is a complex system that requires people, materials, and processes to come together in a highly ordered and timely manner. However, when working in this high-risk system, even well-organized, knowledgeable, vigilant, and well-intentioned individuals will eventually make errors. All systems need to be evaluated on a continual basis to reduce the risk of errors, make errors more easily recognizable, and provide methods for error mitigation. A simple approach to risk management that may be applied in clinical medicine is discussed.

Emotion recognition ability in mothers at high and low risk for child physical abuse.

PubMed

Balge, K A; Milner, J S

2000-10-01

The study sought to determine if high-risk, compared to low-risk, mothers make more emotion recognition errors when they attempt to recognize emotions in children and adults. Thirty-two demographically matched high-risk (n = 16) and low-risk (n = 16) mothers were asked to identify different emotions expressed by children and adults. Sets of high- and low-intensity, visual and auditory emotions were presented. Mothers also completed measures of stress, depression, and ego-strength. High-risk, compared to low-risk, mothers showed a tendency to make more errors on the visual and auditory emotion recognition tasks, with a trend toward more errors on the low-intensity, visual stimuli. However, the observed trends were not significant. Only a post-hoc test of error rates across all stimuli indicated that high-risk, compared to low-risk, mothers made significantly more emotion recognition errors. Although situational stress differences were not found, high-risk mothers reported significantly higher levels of general parenting stress and depression and lower levels of ego-strength. Since only trends and a significant post hoc finding of more overall emotion recognition errors in high-risk mothers were observed, additional research is needed to determine if high-risk mothers have emotion recognition deficits that may impact parent-child interactions. As in prior research, the study found that high-risk mothers reported more parenting stress and depression and less ego-strength.

Punishing an error improves learning: the influence of punishment magnitude on error-related neural activity and subsequent learning.

PubMed

Hester, Robert; Murphy, Kevin; Brown, Felicity L; Skilleter, Ashley J

2010-11-17

Punishing an error to shape subsequent performance is a major tenet of individual and societal level behavioral interventions. Recent work examining error-related neural activity has identified that the magnitude of activity in the posterior medial frontal cortex (pMFC) is predictive of learning from an error, whereby greater activity in this region predicts adaptive changes in future cognitive performance. It remains unclear how punishment influences error-related neural mechanisms to effect behavior change, particularly in key regions such as pMFC, which previous work has demonstrated to be insensitive to punishment. Using an associative learning task that provided monetary reward and punishment for recall performance, we observed that when recall errors were categorized by subsequent performance--whether the failure to accurately recall a number-location association was corrected at the next presentation of the same trial--the magnitude of error-related pMFC activity predicted future correction. However, the pMFC region was insensitive to the magnitude of punishment an error received and it was the left insula cortex that predicted learning from the most aversive outcomes. These findings add further evidence to the hypothesis that error-related pMFC activity may reflect more than a prediction error in representing the value of an outcome. The novel role identified here for the insular cortex in learning from punishment appears particularly compelling for our understanding of psychiatric and neurologic conditions that feature both insular cortex dysfunction and a diminished capacity for learning from negative feedback or punishment.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

NASA Astrophysics Data System (ADS)

Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo

2017-05-01

Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

A predictability study of Lorenz's 28-variable model as a dynamical system

NASA Technical Reports Server (NTRS)

Krishnamurthy, V.

1993-01-01

The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.

Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

NASA Astrophysics Data System (ADS)

Qian, Xiaoshan

2018-01-01

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.

Heat transfer models for predicting Salmonella enteritidis in shell eggs through supply chain distribution.

PubMed

Almonacid, S; Simpson, R; Teixeira, A

2007-11-01

Egg and egg preparations are important vehicles for Salmonella enteritidis infections. The influence of time-temperature becomes important when the presence of this organism is found in commercial shell eggs. A computer-aided mathematical model was validated to estimate surface and interior temperature of shell eggs under variable ambient and refrigerated storage temperature. A risk assessment of S. enteritidis based on the use of this model, coupled with S. enteritidis kinetics, has already been reported in a companion paper published earlier in JFS. The model considered the actual geometry and composition of shell eggs and was solved by numerical techniques (finite differences and finite elements). Parameters of interest such as local (h) and global (U) heat transfer coefficient, thermal conductivity, and apparent volumetric specific heat were estimated by an inverse procedure from experimental temperature measurement. In order to assess the error in predicting microbial population growth, theoretical and experimental temperatures were applied to a S. enteritidis growth model taken from the literature. Errors between values of microbial population growth calculated from model predicted compared with experimentally measured temperatures were satisfactorily low: 1.1% and 0.8% for the finite difference and finite element model, respectively.

Profit through predictability: The MRF difference at optimax

NASA Astrophysics Data System (ADS)

Light, Brandon

2007-05-01

In the manufacturing business, there is one product that matters, money. Whether making shoelaces or aircraft carriers a business that doesn't also make a profit doesn't stay around long. Being able to predict operational expenses is critical to determining a product's sale price. Priced too high a product won't sell, too low profit goes away. In the business of precision optics manufacturing, predictability has been often impossible or had large error bars. Manufacturing unpredictability made setting price a challenge. What if predictability could improve by changing the polishing process? Would a predictable, deterministic process lead to profit? Optimax Systems has experienced exactly that. Incorporating Magnetorheological Finishing (MRF) into its finishing process, Optimax saw parts categorized financially as "high risk" become a routine product of higher quality, delivered on time and within budget. Using actual production figures, this presentation will show how much incorporating MRF reduced costs, improved output and increased quality all at the same time.

Effects of Moist Convection on Hurricane Predictability

NASA Technical Reports Server (NTRS)

Zhang, Fuqing; Sippel, Jason A.

2008-01-01

This study exemplifies inherent uncertainties in deterministic prediction of hurricane formation and intensity. Such uncertainties could ultimately limit the predictability of hurricanes at all time scales. In particular, this study highlights the predictability limit due to the effects on moist convection of initial-condition errors with amplitudes far smaller than those of any observation or analysis system. Not only can small and arguably unobservable differences in the initial conditions result in different routes to tropical cyclogenesis, but they can also determine whether or not a tropical disturbance will significantly develop. The details of how the initial vortex is built can depend on chaotic interactions of mesoscale features, such as cold pools from moist convection, whose timing and placement may significantly vary with minute initial differences. Inherent uncertainties in hurricane forecasts illustrate the need for developing advanced ensemble prediction systems to provide event-dependent probabilistic forecasts and risk assessment.

Prediction-error variance in Bayesian model updating: a comparative study

NASA Astrophysics Data System (ADS)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.

An intravenous medication safety system: preventing high-risk medication errors at the point of care.

PubMed

Hatcher, Irene; Sullivan, Mark; Hutchinson, James; Thurman, Susan; Gaffney, F Andrew

2004-10-01

Improving medication safety at the point of care--particularly for high-risk drugs--is a major concern of nursing administrators. The medication errors most likely to cause harm are administration errors related to infusion of high-risk medications. An intravenous medication safety system is designed to prevent high-risk infusion medication errors and to capture continuous quality improvement data for best practice improvement. Initial testing with 50 systems in 2 units at Vanderbilt University Medical Center revealed that, even in the presence of a fully mature computerized prescriber order-entry system, the new safety system averted 99 potential infusion errors in 8 months.

Seasonal to interannual Arctic sea ice predictability in current global climate models

NASA Astrophysics Data System (ADS)

Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.

2014-02-01

We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

Attention in the predictive mind.

PubMed

Ransom, Madeleine; Fazelpour, Sina; Mole, Christopher

2017-01-01

It has recently become popular to suggest that cognition can be explained as a process of Bayesian prediction error minimization. Some advocates of this view propose that attention should be understood as the optimization of expected precisions in the prediction-error signal (Clark, 2013, 2016; Feldman & Friston, 2010; Hohwy, 2012, 2013). This proposal successfully accounts for several attention-related phenomena. We claim that it cannot account for all of them, since there are certain forms of voluntary attention that it cannot accommodate. We therefore suggest that, although the theory of Bayesian prediction error minimization introduces some powerful tools for the explanation of mental phenomena, its advocates have been wrong to claim that Bayesian prediction error minimization is 'all the brain ever does'. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model

NASA Astrophysics Data System (ADS)

Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.

2015-12-01

Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently provides the most reliable forecasts in the Atlantic basin.

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

Predictive and Feedback Performance Errors are Signaled in the Simple Spike Discharge of Individual Purkinje Cells

PubMed Central

Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.

2012-01-01

The cerebellum has been implicated in processing motor errors required for online control of movement and motor learning. The dominant view is that Purkinje cell complex spike discharge signals motor errors. This study investigated whether errors are encoded in the simple spike discharge of Purkinje cells in monkeys trained to manually track a pseudo-randomly moving target. Four task error signals were evaluated based on cursor movement relative to target movement. Linear regression analyses based on firing residuals ensured that the modulation with a specific error parameter was independent of the other error parameters and kinematics. The results demonstrate that simple spike firing in lobules IV–VI is significantly correlated with position, distance and directional errors. Independent of the error signals, the same Purkinje cells encode kinematics. The strongest error modulation occurs at feedback timing. However, in 72% of cells at least one of the R2 temporal profiles resulting from regressing firing with individual errors exhibit two peak R2 values. For these bimodal profiles, the first peak is at a negative τ (lead) and a second peak at a positive τ (lag), implying that Purkinje cells encode both prediction and feedback about an error. For the majority of the bimodal profiles, the signs of the regression coefficients or preferred directions reverse at the times of the peaks. The sign reversal results in opposing simple spike modulation for the predictive and feedback components. Dual error representations may provide the signals needed to generate sensory prediction errors used to update a forward internal model. PMID:23115173

The Dopamine Prediction Error: Contributions to Associative Models of Reward Learning

PubMed Central

Nasser, Helen M.; Calu, Donna J.; Schoenbaum, Geoffrey; Sharpe, Melissa J.

2017-01-01

Phasic activity of midbrain dopamine neurons is currently thought to encapsulate the prediction-error signal described in Sutton and Barto’s (1981) model-free reinforcement learning algorithm. This phasic signal is thought to contain information about the quantitative value of reward, which transfers to the reward-predictive cue after learning. This is argued to endow the reward-predictive cue with the value inherent in the reward, motivating behavior toward cues signaling the presence of reward. Yet theoretical and empirical research has implicated prediction-error signaling in learning that extends far beyond a transfer of quantitative value to a reward-predictive cue. Here, we review the research which demonstrates the complexity of how dopaminergic prediction errors facilitate learning. After briefly discussing the literature demonstrating that phasic dopaminergic signals can act in the manner described by Sutton and Barto (1981), we consider how these signals may also influence attentional processing across multiple attentional systems in distinct brain circuits. Then, we discuss how prediction errors encode and promote the development of context-specific associations between cues and rewards. Finally, we consider recent evidence that shows dopaminergic activity contains information about causal relationships between cues and rewards that reflect information garnered from rich associative models of the world that can be adapted in the absence of direct experience. In discussing this research we hope to support the expansion of how dopaminergic prediction errors are thought to contribute to the learning process beyond the traditional concept of transferring quantitative value. PMID:28275359

An MEG signature corresponding to an axiomatic model of reward prediction error.

PubMed

Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.

A Case-Series Test of the Interactive Two-step Model of Lexical Access: Predicting Word Repetition from Picture Naming

PubMed Central

Dell, Gary S.; Martin, Nadine; Schwartz, Myrna F.

2010-01-01

Lexical access in language production, and particularly pathologies of lexical access, are often investigated by examining errors in picture naming and word repetition. In this article, we test a computational approach to lexical access, the two-step interactive model, by examining whether the model can quantitatively predict the repetition-error patterns of 65 aphasic subjects from their naming errors. The model’s characterizations of the subjects’ naming errors were taken from the companion paper to this one (Schwartz, Dell, N. Martin, Gahl & Sobel, 2006), and their repetition was predicted from the model on the assumption that naming involves two error prone steps, word and phonological retrieval, whereas repetition only creates errors in the second of these steps. A version of the model in which lexical-semantic and lexical-phonological connections could be independently lesioned was generally successful in predicting repetition for the aphasics. An analysis of the few cases in which model predictions were inaccurate revealed the role of input phonology in the repetition task. PMID:21085621

Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models.

PubMed

Stam, Pieter J A; van Vliet, René C J A; van de Ven, Wynand P M M

2010-05-01

Current research on the added value of self-reported health measures for risk equalization modeling does not include all types of self-reported health measures; and/or is compared with a limited set of medically diagnosed or pharmacy-based diseases; and/or is limited to specific populations of high-risk individuals. The objective of our study is to determine the predictive power of all types of self-reported health measures for prospective modeling of health care expenditures in a general population of adult Dutch sickness fund enrollees, given that pharmacy and diagnostic data from administrative records are already included in the risk equalization formula. We used 4 models of 2002 total, inpatient and outpatient expenditures to evaluate the separate and combined predictive ability of 2 kinds of data: (1) Pharmacy-based (PCGs) and Diagnosis-based (DCGs) Cost Groups and (2) summarized self-reported health information. Model performance is measured at the total population level using R2 and mean absolute prediction error; also, by examining mean discrepancies between model-predicted and actual expenditures (ie, expected over- or undercompensation) for members of potentially "mispriced" subgroups. These subgroups are identified by self-reports from prior-year health surveys and utilization and expenditure data from 5 preceding years. Subjects were 18,617 respondents to a health survey, held among a stratified sample of adult members of the largest Dutch sickness fund in 2002, with an overrepresentation of people in poor health. The data were extracted from a claims database and a health survey. The claims-based data are the outcomes of total, inpatient, and outpatient annualized expenditures in 2002; age, gender, PCGs, DCGs in 2001; and health care expenditures and hospitalizations during the years 1997 to 2001. The SF-36, Organization for Economic Cooperation and Development items, and long-term diseases and conditions were collected by a special purpose health survey conducted in the last quarter of 2001. Out-of-sample R2 equals 17.2%, 2.6%, and 32.4% for the models of total, inpatient and outpatient expenditures including PCGs, DCGs, and self-reported health measures. Self-reported health measures contribute less to predictive power than PCGs and DCGs. PCGs and DCGs also predict better than self-reported health measures for people with top 25% total expenditures or hospitalizations in each year during a 5-year period. On the other hand, self-reported health measures are better predictors than PCGs and DCGs for people without any top 25% expenditures during the 5-year period, for switchers, and for most subgroups of relatively unhealthy people defined by self-reported health measures. Among the set of self-reported health measures, the SF-36 adds most to predictive power in terms of R2, mean absolute prediction error, and for almost all studied subgroups. It is concluded that the self-reported health measures make an independent contribution to forecasting health care expenditures, even if the prediction model already includes diagnostic and pharmacy-based information currently used in Dutch risk equalization models.

Assumption-free estimation of the genetic contribution to refractive error across childhood.

PubMed

Guggenheim, Jeremy A; St Pourcain, Beate; McMahon, George; Timpson, Nicholas J; Evans, David M; Williams, Cathy

2015-01-01

Studies in relatives have generally yielded high heritability estimates for refractive error: twins 75-90%, families 15-70%. However, because related individuals often share a common environment, these estimates are inflated (via misallocation of unique/common environment variance). We calculated a lower-bound heritability estimate for refractive error free from such bias. Between the ages 7 and 15 years, participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) underwent non-cycloplegic autorefraction at regular research clinics. At each age, an estimate of the variance in refractive error explained by single nucleotide polymorphism (SNP) genetic variants was calculated using genome-wide complex trait analysis (GCTA) using high-density genome-wide SNP genotype information (minimum N at each age=3,404). The variance in refractive error explained by the SNPs ("SNP heritability") was stable over childhood: Across age 7-15 years, SNP heritability averaged 0.28 (SE=0.08, p<0.001). The genetic correlation for refractive error between visits varied from 0.77 to 1.00 (all p<0.001) demonstrating that a common set of SNPs was responsible for the genetic contribution to refractive error across this period of childhood. Simulations suggested lack of cycloplegia during autorefraction led to a small underestimation of SNP heritability (adjusted SNP heritability=0.35; SE=0.09). To put these results in context, the variance in refractive error explained (or predicted) by the time participants spent outdoors was <0.005 and by the time spent reading was <0.01, based on a parental questionnaire completed when the child was aged 8-9 years old. Genetic variation captured by common SNPs explained approximately 35% of the variation in refractive error between unrelated subjects. This value sets an upper limit for predicting refractive error using existing SNP genotyping arrays, although higher-density genotyping in larger samples and inclusion of interaction effects is expected to raise this figure toward twin- and family-based heritability estimates. The same SNPs influenced refractive error across much of childhood. Notwithstanding the strong evidence of association between time outdoors and myopia, and time reading and myopia, less than 1% of the variance in myopia at age 15 was explained by crude measures of these two risk factors, indicating that their effects may be limited, at least when averaged over the whole population.

Differences among Job Positions Related to Communication Errors at Construction Sites

NASA Astrophysics Data System (ADS)

Takahashi, Akiko; Ishida, Toshiro

In a previous study, we classified the communicatio n errors at construction sites as faulty intention and message pattern, inadequate channel pattern, and faulty comprehension pattern. This study seeks to evaluate the degree of risk of communication errors and to investigate differences among people in various job positions in perception of communication error risk . Questionnaires based on the previous study were a dministered to construction workers (n=811; 149 adminis trators, 208 foremen and 454 workers). Administrators evaluated all patterns of communication error risk equally. However, foremen and workers evaluated communication error risk differently in each pattern. The common contributing factors to all patterns wer e inadequate arrangements before work and inadequate confirmation. Some factors were common among patterns but other factors were particular to a specific pattern. To help prevent future accidents at construction sites, administrators should understand how people in various job positions perceive communication errors and propose human factors measures to prevent such errors.

Suppressing my memories by listening to yours: The effect of socially triggered context-based prediction error on memory.

PubMed

Vlasceanu, Madalina; Drach, Rae; Coman, Alin

2018-05-03

The mind is a prediction machine. In most situations, it has expectations as to what might happen. But when predictions are invalidated by experience (i.e., prediction errors), the memories that generate these predictions are suppressed. Here, we explore the effect of prediction error on listeners' memories following social interaction. We find that listening to a speaker recounting experiences similar to one's own triggers prediction errors on the part of the listener that lead to the suppression of her memories. This effect, we show, is sensitive to a perspective-taking manipulation, such that individuals who are instructed to take the perspective of the speaker experience memory suppression, whereas individuals who undergo a low-perspective-taking manipulation fail to show a mnemonic suppression effect. We discuss the relevance of these findings for our understanding of the bidirectional influences between cognition and social contexts, as well as for the real-world situations that involve memory-based predictions.

EEG oscillatory patterns are associated with error prediction during music performance and are altered in musician's dystonia.

PubMed

Ruiz, María Herrojo; Strübing, Felix; Jabusch, Hans-Christian; Altenmüller, Eckart

2011-04-15

Skilled performance requires the ability to monitor ongoing behavior, detect errors in advance and modify the performance accordingly. The acquisition of fast predictive mechanisms might be possible due to the extensive training characterizing expertise performance. Recent EEG studies on piano performance reported a negative event-related potential (ERP) triggered in the ACC 70 ms before performance errors (pitch errors due to incorrect keypress). This ERP component, termed pre-error related negativity (pre-ERN), was assumed to reflect processes of error detection in advance. However, some questions remained to be addressed: (i) Does the electrophysiological marker prior to errors reflect an error signal itself or is it related instead to the implementation of control mechanisms? (ii) Does the posterior frontomedial cortex (pFMC, including ACC) interact with other brain regions to implement control adjustments following motor prediction of an upcoming error? (iii) Can we gain insight into the electrophysiological correlates of error prediction and control by assessing the local neuronal synchronization and phase interaction among neuronal populations? (iv) Finally, are error detection and control mechanisms defective in pianists with musician's dystonia (MD), a focal task-specific dystonia resulting from dysfunction of the basal ganglia-thalamic-frontal circuits? Consequently, we investigated the EEG oscillatory and phase synchronization correlates of error detection and control during piano performances in healthy pianists and in a group of pianists with MD. In healthy pianists, the main outcomes were increased pre-error theta and beta band oscillations over the pFMC and 13-15 Hz phase synchronization, between the pFMC and the right lateral prefrontal cortex, which predicted corrective mechanisms. In MD patients, the pattern of phase synchronization appeared in a different frequency band (6-8 Hz) and correlated with the severity of the disorder. The present findings shed new light on the neural mechanisms, which might implement motor prediction by means of forward control processes, as they function in healthy pianists and in their altered form in patients with MD. Copyright © 2010 Elsevier Inc. All rights reserved.

Towards a Framework for Managing Risk Associated with Technology-Induced Error.

PubMed

Borycki, Elizabeth M; Kushniruk, Andre W

2017-01-01

Health information technologies (HIT) promised to streamline and modernize healthcare processes. However, a growing body of research has indicated that if such technologies are not designed, implemented or maintained properly this may lead to an increased incidence of new types of errors which the authors have referred to as "technology-induced errors". In this paper, framework is presented that can be used to manage HIT risk. The framework considers the reduction of technology-induced errors at different stages by managing risks associated with the implementation of HIT. Frameworks that allow health information technology managers to employ proactive and preventative approaches that can be used to manage the risks associated with technology-induced errors are critical to improving HIT safety and managing risk associated with implementing new technologies.

Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

NASA Astrophysics Data System (ADS)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with ARMA(1,2) errors were fit to the observations. Preliminary model validation exercises at a 30-day forecast horizon show that the ARMA error models generally improve the predictive skill of the linear regression rating curves. Skill seems to vary based on the ambient hydrologic conditions at the onset of the forecast. For example, ARMA error model forecasts issued before a high flow/turbidity event do not show significant improvements over the rating curve approach. However, ARMA error model forecasts issued during the "falling limb" of the hydrograph are significantly more accurate than rating curves for both single day and accumulated event predictions. In order to assist in reservoir operations decisions associated with turbidity events and general water supply reliability, DEP has initiated design of an Operations Support Tool (OST). OST integrates a reservoir operations model with 2D hydrodynamic water quality models and a database compiling near-real-time data sources and hydrologic forecasts. Currently, OST uses conventional flow-turbidity rating curves and hydrologic forecasts for predictive turbidity inputs. Given the improvements in predictive skill over traditional rating curves, the ARMA error models are currently being evaluated as an addition to DEP's Operations Support Tool.

Using cognitive status to predict crash risk: blazing new trails?

PubMed

Staplin, Loren; Gish, Kenneth W; Sifrit, Kathy J

2014-02-01

A computer-based version of an established neuropsychological paper-and-pencil assessment tool, the Trail-Making Test, was applied with approximately 700 drivers aged 70 years and older in offices of the Maryland Motor Vehicle Administration. This was a volunteer sample that received a small compensation for study participation, with an assurance that their license status would not be affected by the results. Analyses revealed that the study sample was representative of Maryland older drivers with respect to age and indices of prior driving safety. The relationship between drivers' scores on the Trail-Making Test and prospective crash experience was analyzed using a new outcome measure that explicitly takes into account error responses as well as correct responses, the error-compensated completion time. For the only reliable predictor of crash risk, Trail-Making Test Part B, this measure demonstrated a modest gain in specificity and was a more significant predictor of future safety risk than the simple time-to-completion measure. Improved specificity and the potential for autonomous test administration are particular advantages of this measure for use with large populations, in settings such as health care or driver licensing. © 2013.

Distributions in the error space: goal-directed movements described in time and state-space representations.

PubMed

Fisher, Moria E; Huang, Felix C; Wright, Zachary A; Patton, James L

2014-01-01

Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.

Poster - 49: Assessment of Synchrony respiratory compensation error for CyberKnife liver treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Ming; Cygler,

The goal of this work is to quantify respiratory motion compensation errors for liver tumor patients treated by the CyberKnife system with Synchrony tracking, to identify patients with the smallest tracking errors and to eventually help coach patient’s breathing patterns to minimize dose delivery errors. The accuracy of CyberKnife Synchrony respiratory motion compensation was assessed for 37 patients treated for liver lesions by analyzing data from system logfiles. A predictive model is used to modulate the direction of individual beams during dose delivery based on the positions of internally implanted fiducials determined using an orthogonal x-ray imaging system and themore » current location of LED external markers. For each x-ray pair acquired, system logfiles report the prediction error, the difference between the measured and predicted fiducial positions, and the delivery error, which is an estimate of the statistical error in the model overcoming the latency between x-ray acquisition and robotic repositioning. The total error was calculated at the time of each x-ray pair, for the number of treatment fractions and the number of patients, giving the average respiratory motion compensation error in three dimensions. The 99{sup th} percentile for the total radial error is 3.85 mm, with the highest contribution of 2.79 mm in superior/inferior (S/I) direction. The absolute mean compensation error is 1.78 mm radially with a 1.27 mm contribution in the S/I direction. Regions of high total error may provide insight into features predicting groups of patients with larger or smaller total errors.« less

Assessment of uncertainties in radiation-induced cancer risk predictions at clinically relevant doses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, J.; Moteabbed, M.; Paganetti, H., E-mail: hpaganetti@mgh.harvard.edu

2015-01-15

Purpose: Theoretical dose–response models offer the possibility to assess second cancer induction risks after external beam therapy. The parameters used in these models are determined with limited data from epidemiological studies. Risk estimations are thus associated with considerable uncertainties. This study aims at illustrating uncertainties when predicting the risk for organ-specific second cancers in the primary radiation field illustrated by choosing selected treatment plans for brain cancer patients. Methods: A widely used risk model was considered in this study. The uncertainties of the model parameters were estimated with reported data of second cancer incidences for various organs. Standard error propagationmore » was then subsequently applied to assess the uncertainty in the risk model. Next, second cancer risks of five pediatric patients treated for cancer in the head and neck regions were calculated. For each case, treatment plans for proton and photon therapy were designed to estimate the uncertainties (a) in the lifetime attributable risk (LAR) for a given treatment modality and (b) when comparing risks of two different treatment modalities. Results: Uncertainties in excess of 100% of the risk were found for almost all organs considered. When applied to treatment plans, the calculated LAR values have uncertainties of the same magnitude. A comparison between cancer risks of different treatment modalities, however, does allow statistically significant conclusions. In the studied cases, the patient averaged LAR ratio of proton and photon treatments was 0.35, 0.56, and 0.59 for brain carcinoma, brain sarcoma, and bone sarcoma, respectively. Their corresponding uncertainties were estimated to be potentially below 5%, depending on uncertainties in dosimetry. Conclusions: The uncertainty in the dose–response curve in cancer risk models makes it currently impractical to predict the risk for an individual external beam treatment. On the other hand, the ratio of absolute risks between two modalities is less sensitive to the uncertainties in the risk model and can provide statistically significant estimates.« less

The Surveillance Error Grid

PubMed Central

Lias, Courtney; Vigersky, Robert; Clarke, William; Parkes, Joan Lee; Sacks, David B.; Kirkman, M. Sue; Kovatchev, Boris

2014-01-01

Introduction: Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. Methods: A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG. Results: SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. Discussion: The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs. PMID:25562886

The surveillance error grid.

PubMed

Klonoff, David C; Lias, Courtney; Vigersky, Robert; Clarke, William; Parkes, Joan Lee; Sacks, David B; Kirkman, M Sue; Kovatchev, Boris

2014-07-01

Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG. SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs. © 2014 Diabetes Technology Society.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

PubMed

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects. A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.

Complementary roles for amygdala and periaqueductal gray in temporal-difference fear learning.

PubMed

Cole, Sindy; McNally, Gavan P

2009-01-01

Pavlovian fear conditioning is not a unitary process. At the neurobiological level multiple brain regions and neurotransmitters contribute to fear learning. At the behavioral level many variables contribute to fear learning including the physical salience of the events being learned about, the direction and magnitude of predictive error, and the rate at which these are learned about. These experiments used a serial compound conditioning design to determine the roles of basolateral amygdala (BLA) NMDA receptors and ventrolateral midbrain periaqueductal gray (vlPAG) mu-opioid receptors (MOR) in predictive fear learning. Rats received a three-stage design, which arranged for both positive and negative prediction errors producing bidirectional changes in fear learning within the same subjects during the test stage. Intra-BLA infusion of the NR2B receptor antagonist Ifenprodil prevented all learning. In contrast, intra-vlPAG infusion of the MOR antagonist CTAP enhanced learning in response to positive predictive error but impaired learning in response to negative predictive error--a pattern similar to Hebbian learning and an indication that fear learning had been divorced from predictive error. These findings identify complementary but dissociable roles for amygdala NMDA receptors and vlPAG MOR in temporal-difference predictive fear learning.

When bad things happen: adverse event reporting and disclosure as patient safety and risk management tools in the neonatal intensive care unit.

PubMed

Donn, Steven M; McDonnell, William M

2012-01-01

The Institute of Medicine has recommended a change in culture from "name and blame" to patient safety. This will require system redesign to identify and address errors, establish performance standards, and set safety expectations. This approach, however, is at odds with the present medical malpractice (tort) system. The current system is outcomes-based, meaning that health care providers and institutions are often sued despite providing appropriate care. Nevertheless, the focus should remain to provide the safest patient care. Effective peer review may be hindered by the present tort system. Reporting of medical errors is a key piece of peer review and education, and both anonymous reporting and confidential reporting of errors have potential disadvantages. Diagnostic and treatment errors continue to be the leading sources of allegations of malpractice in pediatrics, and the neonatal intensive care unit is uniquely vulnerable. Most errors result from systems failures rather than human error. Risk management can be an effective process to identify, evaluate, and address problems that may injure patients, lead to malpractice claims, and result in financial losses. Risk management identifies risk or potential risk, calculates the probability of an adverse event arising from a risk, estimates the impact of the adverse event, and attempts to control the risk. Implementation of a successful risk management program requires a positive attitude, sufficient knowledge base, and a commitment to improvement. Transparency in the disclosure of medical errors and a strategy of prospective risk management in dealing with medical errors may result in a substantial reduction in medical malpractice lawsuits, lower litigation costs, and a more safety-conscious environment. Thieme Medical Publishers, Inc.

Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection

USGS Publications Warehouse

Green, Christopher T.; Zhang, Yong; Jurgens, Bryant C.; Starn, J. Jeffrey; Landon, Matthew K.

2014-01-01

Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic datasets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for one public-supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (NO3−) concentrations. Analytical models included a new, scale-dependent dispersivity model (SDM) for two-dimensional transport from the watertable to a well, and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.

Similarities in error processing establish a link between saccade prediction at baseline and adaptation performance.

PubMed

Wong, Aaron L; Shelhamer, Mark

2014-05-01

Adaptive processes are crucial in maintaining the accuracy of body movements and rely on error storage and processing mechanisms. Although classically studied with adaptation paradigms, evidence of these ongoing error-correction mechanisms should also be detectable in other movements. Despite this connection, current adaptation models are challenged when forecasting adaptation ability with measures of baseline behavior. On the other hand, we have previously identified an error-correction process present in a particular form of baseline behavior, the generation of predictive saccades. This process exhibits long-term intertrial correlations that decay gradually (as a power law) and are best characterized with the tools of fractal time series analysis. Since this baseline task and adaptation both involve error storage and processing, we sought to find a link between the intertrial correlations of the error-correction process in predictive saccades and the ability of subjects to alter their saccade amplitudes during an adaptation task. Here we find just such a relationship: the stronger the intertrial correlations during prediction, the more rapid the acquisition of adaptation. This reinforces the links found previously between prediction and adaptation in motor control and suggests that current adaptation models are inadequate to capture the complete dynamics of these error-correction processes. A better understanding of the similarities in error processing between prediction and adaptation might provide the means to forecast adaptation ability with a baseline task. This would have many potential uses in physical therapy and the general design of paradigms of motor adaptation. Copyright © 2014 the American Physiological Society.

Disambiguating ventral striatum fMRI-related bold signal during reward prediction in schizophrenia

PubMed Central

Morris, R W; Vercammen, A; Lenroot, R; Moore, L; Langton, J M; Short, B; Kulkarni, J; Curtis, J; O'Donnell, M; Weickert, C S; Weickert, T W

2012-01-01

Reward detection, surprise detection and prediction-error signaling have all been proposed as roles for the ventral striatum (vStr). Previous neuroimaging studies of striatal function in schizophrenia have found attenuated neural responses to reward-related prediction errors; however, as prediction errors represent a discrepancy in mesolimbic neural activity between expected and actual events, it is critical to examine responses to both expected and unexpected rewards (URs) in conjunction with expected and UR omissions in order to clarify the nature of ventral striatal dysfunction in schizophrenia. In the present study, healthy adults and people with schizophrenia were tested with a reward-related prediction-error task during functional magnetic resonance imaging to determine whether schizophrenia is associated with altered neural responses in the vStr to rewards, surprise prediction errors or all three factors. In healthy adults, we found neural responses in the vStr were correlated more specifically with prediction errors than to surprising events or reward stimuli alone. People with schizophrenia did not display the normal differential activation between expected and URs, which was partially due to exaggerated ventral striatal responses to expected rewards (right vStr) but also included blunted responses to unexpected outcomes (left vStr). This finding shows that neural responses, which typically are elicited by surprise, can also occur to well-predicted events in schizophrenia and identifies aberrant activity in the vStr as a key node of dysfunction in the neural circuitry used to differentiate expected and unexpected feedback in schizophrenia. PMID:21709684

Evaluation of drug administration errors in a teaching hospital

PubMed Central

2012-01-01

Background Medication errors can occur at any of the three steps of the medication use process: prescribing, dispensing and administration. We aimed to determine the incidence, type and clinical importance of drug administration errors and to identify risk factors. Methods Prospective study based on disguised observation technique in four wards in a teaching hospital in Paris, France (800 beds). A pharmacist accompanied nurses and witnessed the preparation and administration of drugs to all patients during the three drug rounds on each of six days per ward. Main outcomes were number, type and clinical importance of errors and associated risk factors. Drug administration error rate was calculated with and without wrong time errors. Relationship between the occurrence of errors and potential risk factors were investigated using logistic regression models with random effects. Results Twenty-eight nurses caring for 108 patients were observed. Among 1501 opportunities for error, 415 administrations (430 errors) with one or more errors were detected (27.6%). There were 312 wrong time errors, ten simultaneously with another type of error, resulting in an error rate without wrong time error of 7.5% (113/1501). The most frequently administered drugs were the cardiovascular drugs (425/1501, 28.3%). The highest risks of error in a drug administration were for dermatological drugs. No potentially life-threatening errors were witnessed and 6% of errors were classified as having a serious or significant impact on patients (mainly omission). In multivariate analysis, the occurrence of errors was associated with drug administration route, drug classification (ATC) and the number of patient under the nurse's care. Conclusion Medication administration errors are frequent. The identification of its determinants helps to undertake designed interventions. PMID:22409837

Evaluation of drug administration errors in a teaching hospital.

PubMed

Berdot, Sarah; Sabatier, Brigitte; Gillaizeau, Florence; Caruba, Thibaut; Prognon, Patrice; Durieux, Pierre

2012-03-12

Medication errors can occur at any of the three steps of the medication use process: prescribing, dispensing and administration. We aimed to determine the incidence, type and clinical importance of drug administration errors and to identify risk factors. Prospective study based on disguised observation technique in four wards in a teaching hospital in Paris, France (800 beds). A pharmacist accompanied nurses and witnessed the preparation and administration of drugs to all patients during the three drug rounds on each of six days per ward. Main outcomes were number, type and clinical importance of errors and associated risk factors. Drug administration error rate was calculated with and without wrong time errors. Relationship between the occurrence of errors and potential risk factors were investigated using logistic regression models with random effects. Twenty-eight nurses caring for 108 patients were observed. Among 1501 opportunities for error, 415 administrations (430 errors) with one or more errors were detected (27.6%). There were 312 wrong time errors, ten simultaneously with another type of error, resulting in an error rate without wrong time error of 7.5% (113/1501). The most frequently administered drugs were the cardiovascular drugs (425/1501, 28.3%). The highest risks of error in a drug administration were for dermatological drugs. No potentially life-threatening errors were witnessed and 6% of errors were classified as having a serious or significant impact on patients (mainly omission). In multivariate analysis, the occurrence of errors was associated with drug administration route, drug classification (ATC) and the number of patient under the nurse's care. Medication administration errors are frequent. The identification of its determinants helps to undertake designed interventions.

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling.

PubMed

Bohil, Corey J; Higgins, Nicholas A; Keebler, Joseph R

2014-01-01

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification. Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.

Impact of SST Anomaly Events over the Kuroshio-Oyashio Extension on the "Summer Prediction Barrier"

NASA Astrophysics Data System (ADS)

Wu, Yujie; Duan, Wansuo

2018-04-01

The "summer prediction barrier" (SPB) of SST anomalies (SSTA) over the Kuroshio-Oyashio Extension (KOE) refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October (ASO) season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transition rates (Category-1) favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates (Category-2). The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling (downwelling) in the ASO season also causes SSTA cooling (warming), accelerating the transition rates of warm (cold) KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.

Suppression of Striatal Prediction Errors by the Prefrontal Cortex in Placebo Hypoalgesia.

PubMed

Schenk, Lieven A; Sprenger, Christian; Onat, Selim; Colloca, Luana; Büchel, Christian

2017-10-04

Classical learning theories predict extinction after the discontinuation of reinforcement through prediction errors. However, placebo hypoalgesia, although mediated by associative learning, has been shown to be resistant to extinction. We tested the hypothesis that this is mediated by the suppression of prediction error processing through the prefrontal cortex (PFC). We compared pain modulation through treatment cues (placebo hypoalgesia, treatment context) with pain modulation through stimulus intensity cues (stimulus context) during functional magnetic resonance imaging in 48 male and female healthy volunteers. During acquisition, our data show that expectations are correctly learned and that this is associated with prediction error signals in the ventral striatum (VS) in both contexts. However, in the nonreinforced test phase, pain modulation and expectations of pain relief persisted to a larger degree in the treatment context, indicating that the expectations were not correctly updated in the treatment context. Consistently, we observed significantly stronger neural prediction error signals in the VS in the stimulus context compared with the treatment context. A connectivity analysis revealed negative coupling between the anterior PFC and the VS in the treatment context, suggesting that the PFC can suppress the expression of prediction errors in the VS. Consistent with this, a participant's conceptual views and beliefs about treatments influenced the pain modulation only in the treatment context. Our results indicate that in placebo hypoalgesia contextual treatment information engages prefrontal conceptual processes, which can suppress prediction error processing in the VS and lead to reduced updating of treatment expectancies, resulting in less extinction of placebo hypoalgesia. SIGNIFICANCE STATEMENT In aversive and appetitive reinforcement learning, learned effects show extinction when reinforcement is discontinued. This is thought to be mediated by prediction errors (i.e., the difference between expectations and outcome). Although reinforcement learning has been central in explaining placebo hypoalgesia, placebo hypoalgesic effects show little extinction and persist after the discontinuation of reinforcement. Our results support the idea that conceptual treatment beliefs bias the neural processing of expectations in a treatment context compared with a more stimulus-driven processing of expectations with stimulus intensity cues. We provide evidence that this is associated with the suppression of prediction error processing in the ventral striatum by the prefrontal cortex. This provides a neural basis for persisting effects in reinforcement learning and placebo hypoalgesia. Copyright © 2017 the authors 0270-6474/17/379715-09$15.00/0.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Identifying Children at Risk of High Myopia Using Population Centile Curves of Refraction.

PubMed

Chen, Yanxian; Zhang, Jian; Morgan, Ian G; He, Mingguang

2016-01-01

To construct reference centile curves of refraction based on population-based data as an age-specific severity scale to evaluate their efficacy as a tool for identifying children at risk of developing high myopia in a longitudinal study. Data of 4218 children aged 5-15 years from the Guangzhou Refractive Error Study in Children (RESC) study, and 354 first-born twins from the Guangzhou Twin Eye Study (GTES) with annual visit were included in the analysis. Reference centile curves for refraction were constructed using a quantile regression model based on the cycloplegic refraction data from the RESC. The risk of developing high myopia (spherical equivalent ≤ -6 diopters [D]) was evaluated as a diagnostic test using the twin follow-up data. The centile curves suggested that the 3rd, 5th, and 10th percentile decreased from -0.25 D, 0.00 D and 0.25 D in 5 year-olds to -6.00 D, -5.65D and -4.63 D in 15 year-olds in the population-based data from RESC. In the GTES cohort, the 5th centile showed the most effective diagnostic value with a sensitivity of 92.9%, a specificity of 97.9% and a positive predictive value (PPV) of 65.0% in predicting high myopia onset (≤-6.00D) before the age of 15 years. The PPV was highest (87.5%) in 3rd centile but with only 50.0% sensitivity. The Mathew's correlation coefficient of 5th centile in predicting myopia of -6.0D/-5.0D/-4.0D by age of 15 was 0.77/0.51/0.30 respectively. Reference centile curves provide an age-specific estimation on a severity scale of refractive error in school-aged children. Children located under lower percentiles at young age were more likely to have high myopia at 15 years or probably in adulthood.

"An integrative formal model of motivation and decision making: The MGPM*": Correction to Ballard et al. (2016).

PubMed

2017-02-01

Reports an error in "An integrative formal model of motivation and decision making: The MGPM*" by Timothy Ballard, Gillian Yeo, Shayne Loft, Jeffrey B. Vancouver and Andrew Neal ( Journal of Applied Psychology , 2016[Sep], Vol 101[9], 1240-1265). Equation A3 contained an error. This correct equation is provided in the erratum. (The following abstract of the original article appeared in record 2016-28692-001.) We develop and test an integrative formal model of motivation and decision making. The model, referred to as the extended multiple-goal pursuit model (MGPM*), is an integration of the multiple-goal pursuit model (Vancouver, Weinhardt, & Schmidt, 2010) and decision field theory (Busemeyer & Townsend, 1993). Simulations of the model generated predictions regarding the effects of goal type (approach vs. avoidance), risk, and time sensitivity on prioritization. We tested these predictions in an experiment in which participants pursued different combinations of approach and avoidance goals under different levels of risk. The empirical results were consistent with the predictions of the MGPM*. Specifically, participants pursuing 1 approach and 1 avoidance goal shifted priority from the approach to the avoidance goal over time. Among participants pursuing 2 approach goals, those with low time sensitivity prioritized the goal with the larger discrepancy, whereas those with high time sensitivity prioritized the goal with the smaller discrepancy. Participants pursuing 2 avoidance goals generally prioritized the goal with the smaller discrepancy. Finally, all of these effects became weaker as the level of risk increased. We used quantitative model comparison to show that the MGPM* explained the data better than the original multiple-goal pursuit model, and that the major extensions from the original model were justified. The MGPM* represents a step forward in the development of a general theory of decision making during multiple-goal pursuit. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Evaluation of Acoustic Doppler Current Profiler measurements of river discharge

USGS Publications Warehouse

Morlock, S.E.

1996-01-01

The standard deviations of the ADCP measurements ranged from approximately 1 to 6 percent and were generally higher than the measurement errors predicted by error-propagation analysis of ADCP instrument performance. These error-prediction methods assume that the largest component of ADCP discharge measurement error is instrument related. The larger standard deviations indicate that substantial portions of measurement error may be attributable to sources unrelated to ADCP electronics or signal processing and are functions of the field environment.

[The error, source of learning].

PubMed

Joyeux, Stéphanie; Bohic, Valérie

2016-05-01

The error itself is not recognised as a fault. It is the intentionality which differentiates between an error and a fault. An error is unintentional while a fault is a failure to respect known rules. The risk of error is omnipresent in health institutions. Public authorities have therefore set out a series of measures to reduce this risk. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Embedded Model Error Representation and Propagation in Climate Models

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Thornton, P. E.

2017-12-01

Over the last decade, parametric uncertainty quantification (UQ) methods have reached a level of maturity, while the same can not be said about representation and quantification of structural or model errors. Lack of characterization of model errors, induced by physical assumptions, phenomenological parameterizations or constitutive laws, is a major handicap in predictive science. In particular, e.g. in climate models, significant computational resources are dedicated to model calibration without gaining improvement in predictive skill. Neglecting model errors during calibration/tuning will lead to overconfident and biased model parameters. At the same time, the most advanced methods accounting for model error merely correct output biases, augmenting model outputs with statistical error terms that can potentially violate physical laws, or make the calibrated model ineffective for extrapolative scenarios. This work will overview a principled path for representing and quantifying model errors, as well as propagating them together with the rest of the predictive uncertainty budget, including data noise, parametric uncertainties and surrogate-related errors. Namely, the model error terms will be embedded in select model components rather than as external corrections. Such embedding ensures consistency with physical constraints on model predictions, and renders calibrated model predictions meaningful and robust with respect to model errors. Besides, in the presence of observational data, the approach can effectively differentiate model structural deficiencies from those of data acquisition. The methodology is implemented in UQ Toolkit (www.sandia.gov/uqtoolkit), relying on a host of available forward and inverse UQ tools. We will demonstrate the application of the technique on few application of interest, including ACME Land Model calibration via a wide range of measurements obtained at select sites.

Knowledge acquisition is governed by striatal prediction errors.

PubMed

Pine, Alex; Sadeh, Noa; Ben-Yakov, Aya; Dudai, Yadin; Mendelsohn, Avi

2018-04-26

Discrepancies between expectations and outcomes, or prediction errors, are central to trial-and-error learning based on reward and punishment, and their neurobiological basis is well characterized. It is not known, however, whether the same principles apply to declarative memory systems, such as those supporting semantic learning. Here, we demonstrate with fMRI that the brain parametrically encodes the degree to which new factual information violates expectations based on prior knowledge and beliefs-most prominently in the ventral striatum, and cortical regions supporting declarative memory encoding. These semantic prediction errors determine the extent to which information is incorporated into long-term memory, such that learning is superior when incoming information counters strong incorrect recollections, thereby eliciting large prediction errors. Paradoxically, by the same account, strong accurate recollections are more amenable to being supplanted by misinformation, engendering false memories. These findings highlight a commonality in brain mechanisms and computational rules that govern declarative and nondeclarative learning, traditionally deemed dissociable.

Flight Evaluation of Center-TRACON Automation System Trajectory Prediction Process

NASA Technical Reports Server (NTRS)

Williams, David H.; Green, Steven M.

1998-01-01

Two flight experiments (Phase 1 in October 1992 and Phase 2 in September 1994) were conducted to evaluate the accuracy of the Center-TRACON Automation System (CTAS) trajectory prediction process. The Transport Systems Research Vehicle (TSRV) Boeing 737 based at Langley Research Center flew 57 arrival trajectories that included cruise and descent segments; at the same time, descent clearance advisories from CTAS were followed. Actual trajectories of the airplane were compared with the trajectories predicted by the CTAS trajectory synthesis algorithms and airplane Flight Management System (FMS). Trajectory prediction accuracy was evaluated over several levels of cockpit automation that ranged from a conventional cockpit to performance-based FMS vertical navigation (VNAV). Error sources and their magnitudes were identified and measured from the flight data. The major source of error during these tests was found to be the predicted winds aloft used by CTAS. The most significant effect related to flight guidance was the cross-track and turn-overshoot errors associated with conventional VOR guidance. FMS lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and airplane performance model errors.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Surprise beyond prediction error

PubMed Central

Chumbley, Justin R; Burke, Christopher J; Stephan, Klaas E; Friston, Karl J; Tobler, Philippe N; Fehr, Ernst

2014-01-01

Surprise drives learning. Various neural “prediction error” signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and accounted for a host of potential SPE confounds. This new surprise signal was evident in ventral striatum, primary sensory cortex, frontal poles, and amygdala. We interpret these findings via a normative model of surprise. PMID:24700400

Homeostatic Regulation of Memory Systems and Adaptive Decisions

PubMed Central

Mizumori, Sheri JY; Jo, Yong Sang

2013-01-01

While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The “multiple memory systems of the brain” have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in rigid and suboptimal decision making and memory as seen in addiction and neurological disease. © 2013 The Authors. Hippocampus Published by Wiley Periodicals, Inc. PMID:23929788

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Homeostatic regulation of memory systems and adaptive decisions.

PubMed

Mizumori, Sheri J Y; Jo, Yong Sang

2013-11-01

While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The "multiple memory systems of the brain" have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in rigid and suboptimal decision making and memory as seen in addiction and neurological disease. Copyright © 2013 Wiley Periodicals, Inc.

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

DOE PAGES

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

2017-07-14

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making.

PubMed

Lindahl, Jonas; Danell, Rickard

The aim of this study was to provide a framework to evaluate bibliometric indicators as decision support tools from a decision making perspective and to examine the information value of early career publication rate as a predictor of future productivity. We used ROC analysis to evaluate a bibliometric indicator as a tool for binary decision making. The dataset consisted of 451 early career researchers in the mathematical sub-field of number theory. We investigated the effect of three different definitions of top performance groups-top 10, top 25, and top 50 %; the consequences of using different thresholds in the prediction models; and the added prediction value of information on early career research collaboration and publications in prestige journals. We conclude that early career performance productivity has an information value in all tested decision scenarios, but future performance is more predictable if the definition of a high performance group is more exclusive. Estimated optimal decision thresholds using the Youden index indicated that the top 10 % decision scenario should use 7 articles, the top 25 % scenario should use 7 articles, and the top 50 % should use 5 articles to minimize prediction errors. A comparative analysis between the decision thresholds provided by the Youden index which take consequences into consideration and a method commonly used in evaluative bibliometrics which do not take consequences into consideration when determining decision thresholds, indicated that differences are trivial for the top 25 and the 50 % groups. However, a statistically significant difference between the methods was found for the top 10 % group. Information on early career collaboration and publication strategies did not add any prediction value to the bibliometric indicator publication rate in any of the models. The key contributions of this research is the focus on consequences in terms of prediction errors and the notion of transforming uncertainty into risk when we are choosing decision thresholds in bibliometricly informed decision making. The significance of our results are discussed from the point of view of a science policy and management.

Ramping and Uncertainty Prediction Tool - Analysis and Visualization of Wind Generation Impact on Electrical Grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel; Makarov, PNNL Yuri; Subbarao, PNNL Kris

RUT software is designed for use by the Balancing Authorities to predict and display additional requirements caused by the variability and uncertainty in load and generation. The prediction is made for the next operating hours as well as for the next day. The tool predicts possible deficiencies in generation capability and ramping capability. This deficiency of balancing resources can cause serious risks to power system stability and also impact real-time market energy prices. The tool dynamically and adaptively correlates changing system conditions with the additional balancing needs triggered by the interplay between forecasted and actual load and output of variablemore » resources. The assessment is performed using a specially developed probabilistic algorithm incorporating multiple sources of uncertainty including wind, solar and load forecast errors. The tool evaluates required generation for a worst case scenario, with a user-specified confidence level.« less

Five-equation and robust three-equation methods for solution verification of large eddy simulation

NASA Astrophysics Data System (ADS)

Dutta, Rabijit; Xing, Tao

2018-02-01

This study evaluates the recently developed general framework for solution verification methods for large eddy simulation (LES) using implicitly filtered LES of periodic channel flows at friction Reynolds number of 395 on eight systematically refined grids. The seven-equation method shows that the coupling error based on Hypothesis I is much smaller as compared with the numerical and modeling errors and therefore can be neglected. The authors recommend five-equation method based on Hypothesis II, which shows a monotonic convergence behavior of the predicted numerical benchmark ( S C ), and provides realistic error estimates without the need of fixing the orders of accuracy for either numerical or modeling errors. Based on the results from seven-equation and five-equation methods, less expensive three and four-equation methods for practical LES applications were derived. It was found that the new three-equation method is robust as it can be applied to any convergence types and reasonably predict the error trends. It was also observed that the numerical and modeling errors usually have opposite signs, which suggests error cancellation play an essential role in LES. When Reynolds averaged Navier-Stokes (RANS) based error estimation method is applied, it shows significant error in the prediction of S C on coarse meshes. However, it predicts reasonable S C when the grids resolve at least 80% of the total turbulent kinetic energy.

Lock-in amplifier error prediction and correction in frequency sweep measurements.

PubMed

Sonnaillon, Maximiliano Osvaldo; Bonetto, Fabian Jose

2007-01-01

This article proposes an analytical algorithm for predicting errors in lock-in amplifiers (LIAs) working with time-varying reference frequency. Furthermore, a simple method for correcting such errors is presented. The reference frequency can be swept in order to measure the frequency response of a system within a given spectrum. The continuous variation of the reference frequency produces a measurement error that depends on three factors: the sweep speed, the LIA low-pass filters, and the frequency response of the measured system. The proposed error prediction algorithm is based on the final value theorem of the Laplace transform. The correction method uses a double-sweep measurement. A mathematical analysis is presented and validated with computational simulations and experimental measurements.

Experiences with Lean Six Sigma as improvement strategy to reduce parenteral medication administration errors and associated potential risk of harm.

PubMed

van de Plas, Afke; Slikkerveer, Mariëlle; Hoen, Saskia; Schrijnemakers, Rick; Driessen, Johanna; de Vries, Frank; van den Bemt, Patricia

2017-01-01

In this controlled before-after study the effect of improvements, derived from Lean Six Sigma strategy, on parenteral medication administration errors and the potential risk of harm was determined. During baseline measurement, on control versus intervention ward, at least one administration error occurred in 14 (74%) and 6 (46%) administrations with potential risk of harm in 6 (32%) and 1 (8%) administrations. Most administration errors with high potential risk of harm occurred in bolus injections: 8 (57%) versus 2 (67%) bolus injections were injected too fast with a potential risk of harm in 6 (43%) and 1 (33%) bolus injections on control and intervention ward. Implemented improvement strategies, based on major causes of too fast administration of bolus injections, were: Substitution of bolus injections by infusions, education, availability of administration information and drug round tabards. Post intervention, on the control ward in 76 (76%) administrations at least one error was made (RR 1.03; CI95:0.77-1.38), with a potential risk of harm in 14 (14%) administrations (RR 0.45; CI95:0.20-1.02). In 40 (68%) administrations on the intervention ward at least one error occurred (RR 1.47; CI95:0.80-2.71) but no administrations were associated with a potential risk of harm. A shift in wrong duration administration errors from bolus injections to infusions, with a reduction of potential risk of harm, seems to have occurred on the intervention ward. Although data are insufficient to prove an effect, Lean Six Sigma was experienced as a suitable strategy to select tailored improvements. Further studies are required to prove the effect of the strategy on parenteral medication administration errors.

Experiences with Lean Six Sigma as improvement strategy to reduce parenteral medication administration errors and associated potential risk of harm

PubMed Central

van de Plas, Afke; Slikkerveer, Mariëlle; Hoen, Saskia; Schrijnemakers, Rick; Driessen, Johanna; de Vries, Frank; van den Bemt, Patricia

2017-01-01

In this controlled before-after study the effect of improvements, derived from Lean Six Sigma strategy, on parenteral medication administration errors and the potential risk of harm was determined. During baseline measurement, on control versus intervention ward, at least one administration error occurred in 14 (74%) and 6 (46%) administrations with potential risk of harm in 6 (32%) and 1 (8%) administrations. Most administration errors with high potential risk of harm occurred in bolus injections: 8 (57%) versus 2 (67%) bolus injections were injected too fast with a potential risk of harm in 6 (43%) and 1 (33%) bolus injections on control and intervention ward. Implemented improvement strategies, based on major causes of too fast administration of bolus injections, were: Substitution of bolus injections by infusions, education, availability of administration information and drug round tabards. Post intervention, on the control ward in 76 (76%) administrations at least one error was made (RR 1.03; CI95:0.77-1.38), with a potential risk of harm in 14 (14%) administrations (RR 0.45; CI95:0.20-1.02). In 40 (68%) administrations on the intervention ward at least one error occurred (RR 1.47; CI95:0.80-2.71) but no administrations were associated with a potential risk of harm. A shift in wrong duration administration errors from bolus injections to infusions, with a reduction of potential risk of harm, seems to have occurred on the intervention ward. Although data are insufficient to prove an effect, Lean Six Sigma was experienced as a suitable strategy to select tailored improvements. Further studies are required to prove the effect of the strategy on parenteral medication administration errors. PMID:28674608

Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.

PubMed

Wu, J; Awate, S P; Licht, D J; Clouchoux, C; du Plessis, A J; Avants, B B; Vossough, A; Gee, J C; Limperopoulos, C

2015-07-01

Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates. © 2015 by American Journal of Neuroradiology.

Granular Materials and the Risks They Pose for Success on the Moon and Mars

NASA Technical Reports Server (NTRS)

Wilkinson, R. Allen; Behringer, Robert P.; Jenkins, James T.; Louge, Michel Y.

2004-01-01

Working with soil, sand, powders, ores, cement and sintered bricks, excavating, grading construction sites, driving off-road, transporting granules in chutes and pipes, sifting gravel, separating solids from gases, and using hoppers are so routine that it seems straightforward to do it on the Moon and Mars as we do it on Earth. This paper brings to the fore how little these processes are understood and the millennia-long trial-and-error practices that lead to today's massive over-design, high failure rate, and extensive incremental scaling up of industrial processes because of the inadequate predictive tools for design. We present a number of pragmatic scenarios where granular materials play a role, the risks involved, and what understanding is needed to greatly reduce the risks.

Granular Materials and the Risks They Pose for Success on the Moon and Mars

NASA Astrophysics Data System (ADS)

Wilkinson, R. Allen; Behringer, Robert P.; Jenkins, James T.; Louge, Michel Y.

2005-02-01

Working with soil, sand, powders, ores, cement and sintered bricks, excavating, grading construction sites, driving off-road, transporting granules in chutes and pipes, sifting gravel, separating solids from gases, and using hoppers are so routine that it seems straightforward to do it on the Moon and Mars as we do it on Earth. This paper brings to the fore how little these processes are understood and the millennia-long trial-and-error practices that lead to today's massive over-design, high failure rate, and extensive incremental scaling up of industrial processes because of the inadequate predictive tools for design. We present a number of pragmatic scenarios where granular materials play a role, the risks involved, and what understanding is needed to greatly reduce the risks.

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Sample Model

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Kummerow, Christian D.; Einaudi, Franco (Technical Monitor)

2000-01-01

Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.

Prediction of transmission distortion for wireless video communication: analysis.

PubMed

Chen, Zhifeng; Wu, Dapeng

2012-03-01

Transmitting video over wireless is a challenging problem since video may be seriously distorted due to packet errors caused by wireless channels. The capability of predicting transmission distortion (i.e., video distortion caused by packet errors) can assist in designing video encoding and transmission schemes that achieve maximum video quality or minimum end-to-end video distortion. This paper is aimed at deriving formulas for predicting transmission distortion. The contribution of this paper is twofold. First, we identify the governing law that describes how the transmission distortion process evolves over time and analytically derive the transmission distortion formula as a closed-form function of video frame statistics, channel error statistics, and system parameters. Second, we identify, for the first time, two important properties of transmission distortion. The first property is that the clipping noise, which is produced by nonlinear clipping, causes decay of propagated error. The second property is that the correlation between motion-vector concealment error and propagated error is negative and has dominant impact on transmission distortion, compared with other correlations. Due to these two properties and elegant error/distortion decomposition, our formula provides not only more accurate prediction but also lower complexity than the existing methods.

Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building

NASA Astrophysics Data System (ADS)

Behmanesh, Iman; Yousefianmoghadam, Seyedsina; Nozari, Amin; Moaveni, Babak; Stavridis, Andreas

2018-07-01

This paper investigates the application of Hierarchical Bayesian model updating for uncertainty quantification and response prediction of civil structures. In this updating framework, structural parameters of an initial finite element (FE) model (e.g., stiffness or mass) are calibrated by minimizing error functions between the identified modal parameters and the corresponding parameters of the model. These error functions are assumed to have Gaussian probability distributions with unknown parameters to be determined. The estimated parameters of error functions represent the uncertainty of the calibrated model in predicting building's response (modal parameters here). The focus of this paper is to answer whether the quantified model uncertainties using dynamic measurement at building's reference/calibration state can be used to improve the model prediction accuracies at a different structural state, e.g., damaged structure. Also, the effects of prediction error bias on the uncertainty of the predicted values is studied. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state are identified from ambient vibration data and used to calibrate parameters of the initial FE model as well as the error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after the wall removal. These data are not used to calibrate the model; they are only used to assess the predicted results. The model updating framework proposed in this paper is applied to estimate the modal parameters of the building at its reference state as well as two damaged states: moderate damage (removal of four walls) and severe damage (removal of six walls). Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests. Moreover, it is shown that including prediction error bias in the updating process instead of commonly-used zero-mean error function can significantly reduce the prediction uncertainties.

Pharmacogenetic excitation of dorsomedial prefrontal cortex restores fear prediction error.

PubMed

Yau, Joanna Oi-Yue; McNally, Gavan P

2015-01-07

Pavlovian conditioning involves encoding the predictive relationship between a conditioned stimulus (CS) and an unconditioned stimulus, so that synaptic plasticity and learning is instructed by prediction error. Here we used pharmacogenetic techniques to show a causal relation between activity of rat dorsomedial prefrontal cortex (dmPFC) neurons and fear prediction error. We expressed the excitatory hM3Dq designer receptor exclusively activated by a designer drug (DREADD) in dmPFC and isolated actions of prediction error by using an associative blocking design. Rats were trained to fear the visual CS (CSA) in stage I via pairings with footshock. Then in stage II, rats received compound presentations of visual CSA and auditory CS (CSB) with footshock. This prior fear conditioning of CSA reduced the prediction error during stage II to block fear learning to CSB. The group of rats that received AAV-hSYN-eYFP vector that was treated with clozapine-N-oxide (CNO; 3 mg/kg, i.p.) before stage II showed blocking when tested in the absence of CNO the next day. In contrast, the groups that received AAV-hSYN-hM3Dq and AAV-CaMKIIα-hM3Dq that were treated with CNO before stage II training did not show blocking; learning toward CSB was restored. This restoration of prediction error and fear learning was specific to the injection of CNO because groups that received AAV-hSYN-hM3Dq and AAV-CaMKIIα-hM3Dq that were injected with vehicle before stage II training did show blocking. These effects were not attributable to the DREADD manipulation enhancing learning or arousal, increasing fear memory strength or asymptotic levels of fear learning, or altering fear memory retrieval. Together, these results identify a causal role for dmPFC in a signature of adaptive behavior: using the past to predict future danger and learning from errors in these predictions. Copyright © 2015 the authors 0270-6474/15/350074-10$15.00/0.

Reinforcement Learning Models and Their Neural Correlates: An Activation Likelihood Estimation Meta-Analysis

PubMed Central

Kumar, Poornima; Eickhoff, Simon B.; Dombrovski, Alexandre Y.

2015-01-01

Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments – prediction error – is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies suggest that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that employed algorithmic reinforcement learning models, across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, while instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually-estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies. PMID:25665667

Reward positivity: Reward prediction error or salience prediction error?

PubMed

Heydari, Sepideh; Holroyd, Clay B

2016-08-01

The reward positivity is a component of the human ERP elicited by feedback stimuli in trial-and-error learning and guessing tasks. A prominent theory holds that the reward positivity reflects a reward prediction error signal that is sensitive to outcome valence, being larger for unexpected positive events relative to unexpected negative events (Holroyd & Coles, 2002). Although the theory has found substantial empirical support, most of these studies have utilized either monetary or performance feedback to test the hypothesis. However, in apparent contradiction to the theory, a recent study found that unexpected physical punishments also elicit the reward positivity (Talmi, Atkinson, & El-Deredy, 2013). The authors of this report argued that the reward positivity reflects a salience prediction error rather than a reward prediction error. To investigate this finding further, in the present study participants navigated a virtual T maze and received feedback on each trial under two conditions. In a reward condition, the feedback indicated that they would either receive a monetary reward or not and in a punishment condition the feedback indicated that they would receive a small shock or not. We found that the feedback stimuli elicited a typical reward positivity in the reward condition and an apparently delayed reward positivity in the punishment condition. Importantly, this signal was more positive to the stimuli that predicted the omission of a possible punishment relative to stimuli that predicted a forthcoming punishment, which is inconsistent with the salience hypothesis. © 2016 Society for Psychophysiological Research.

A molecular topology approach to predicting pesticide pollution of groundwater

USGS Publications Warehouse

Worrall , Fred

2001-01-01

Various models have proposed methods for the discrimination of polluting and nonpolluting compounds on the basis of simple parameters, typically adsorption and degradation constants. However, such attempts are prone to site variability and measurement error to the extent that compounds cannot be reliably classified nor the chemistry of pollution extrapolated from them. Using observations of pesticide occurrence in U.S. groundwater it is possible to show that polluting from nonpolluting compounds can be distinguished purely on the basis of molecular topology. Topological parameters can be derived without measurement error or site-specific variability. A logistic regression model has been developed which explains 97% of the variation in the data, with 86% of the variation being explained by the rule that a compound will be found in groundwater if 6 < 0.55. Where 6χp is the sixth-order molecular path connectivity. One group of compounds cannot be classified by this rule and prediction requires reference to higher order connectivity parameters. The use of molecular approaches for understanding pollution at the molecular level and their application to agrochemical development and risk assessment is discussed.

Accuracy of Robotic Radiosurgical Liver Treatment Throughout the Respiratory Cycle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winter, Jeff D.; Wong, Raimond; Swaminath, Anand

Purpose: To quantify random uncertainties in robotic radiosurgical treatment of liver lesions with real-time respiratory motion management. Methods and Materials: We conducted a retrospective analysis of 27 liver cancer patients treated with robotic radiosurgery over 118 fractions. The robotic radiosurgical system uses orthogonal x-ray images to determine internal target position and correlates this position with an external surrogate to provide robotic corrections of linear accelerator positioning. Verification and update of this internal–external correlation model was achieved using periodic x-ray images collected throughout treatment. To quantify random uncertainties in targeting, we analyzed logged tracking information and isolated x-ray images collected immediately beforemore » beam delivery. For translational correlation errors, we quantified the difference between correlation model–estimated target position and actual position determined by periodic x-ray imaging. To quantify prediction errors, we computed the mean absolute difference between the predicted coordinates and actual modeled position calculated 115 milliseconds later. We estimated overall random uncertainty by quadratically summing correlation, prediction, and end-to-end targeting errors. We also investigated relationships between tracking errors and motion amplitude using linear regression. Results: The 95th percentile absolute correlation errors in each direction were 2.1 mm left–right, 1.8 mm anterior–posterior, 3.3 mm cranio–caudal, and 3.9 mm 3-dimensional radial, whereas 95th percentile absolute radial prediction errors were 0.5 mm. Overall 95th percentile random uncertainty was 4 mm in the radial direction. Prediction errors were strongly correlated with modeled target amplitude (r=0.53-0.66, P<.001), whereas only weak correlations existed for correlation errors. Conclusions: Study results demonstrate that model correlation errors are the primary random source of uncertainty in Cyberknife liver treatment and, unlike prediction errors, are not strongly correlated with target motion amplitude. Aggregate 3-dimensional radial position errors presented here suggest the target will be within 4 mm of the target volume for 95% of the beam delivery.« less

Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery.

PubMed

Taft, L M; Evans, R S; Shyu, C R; Egger, M J; Chawla, N; Mitchell, J A; Thornton, S N; Bray, B; Varner, M

2009-04-01

The IOM report, Preventing Medication Errors, emphasizes the overall lack of knowledge of the incidence of adverse drug events (ADE). Operating rooms, emergency departments and intensive care units are known to have a higher incidence of ADE. Labor and delivery (L&D) is an emergency care unit that could have an increased risk of ADE, where reported rates remain low and under-reporting is suspected. Risk factor identification with electronic pattern recognition techniques could improve ADE detection rates. The objective of the present study is to apply Synthetic Minority Over Sampling Technique (SMOTE) as an enhanced sampling method in a sparse dataset to generate prediction models to identify ADE in women admitted for labor and delivery based on patient risk factors and comorbidities. By creating synthetic cases with the SMOTE algorithm and using a 10-fold cross-validation technique, we demonstrated improved performance of the Naïve Bayes and the decision tree algorithms. The true positive rate (TPR) of 0.32 in the raw dataset increased to 0.67 in the 800% over-sampled dataset. Enhanced performance from classification algorithms can be attained with the use of synthetic minority class oversampling techniques in sparse clinical datasets. Predictive models created in this manner can be used to develop evidence based ADE monitoring systems.

Predictability of Solar Radiation for Photovoltaics systems over Europe: from short-term to seasonal time-scales

NASA Astrophysics Data System (ADS)

De Felice, Matteo; Petitta, Marcello; Ruti, Paolo

2014-05-01

Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."

Assessing explicit error reporting in the narrative electronic medical record using keyword searching.

PubMed

Cao, Hui; Stetson, Peter; Hripcsak, George

2003-01-01

Many types of medical errors occur in and outside of hospitals, some of which have very serious consequences and increase cost. Identifying errors is a critical step for managing and preventing them. In this study, we assessed the explicit reporting of medical errors in the electronic record. We used five search terms "mistake," "error," "incorrect," "inadvertent," and "iatrogenic" to survey several sets of narrative reports including discharge summaries, sign-out notes, and outpatient notes from 1991 to 2000. We manually reviewed all the positive cases and identified them based on the reporting of physicians. We identified 222 explicitly reported medical errors. The positive predictive value varied with different keywords. In general, the positive predictive value for each keyword was low, ranging from 3.4 to 24.4%. Therapeutic-related errors were the most common reported errors and these reported therapeutic-related errors were mainly medication errors. Keyword searches combined with manual review indicated some medical errors that were reported in medical records. It had a low sensitivity and a moderate positive predictive value, which varied by search term. Physicians were most likely to record errors in the Hospital Course and History of Present Illness sections of discharge summaries. The reported errors in medical records covered a broad range and were related to several types of care providers as well as non-health care professionals.

Debiasing affective forecasting errors with targeted, but not representative, experience narratives.

PubMed

Shaffer, Victoria A; Focella, Elizabeth S; Scherer, Laura D; Zikmund-Fisher, Brian J

2016-10-01

To determine whether representative experience narratives (describing a range of possible experiences) or targeted experience narratives (targeting the direction of forecasting bias) can reduce affective forecasting errors, or errors in predictions of experiences. In Study 1, participants (N=366) were surveyed about their experiences with 10 common medical events. Those who had never experienced the event provided ratings of predicted discomfort and those who had experienced the event provided ratings of actual discomfort. Participants making predictions were randomly assigned to either the representative experience narrative condition or the control condition in which they made predictions without reading narratives. In Study 2, participants (N=196) were again surveyed about their experiences with these 10 medical events, but participants making predictions were randomly assigned to either the targeted experience narrative condition or the control condition. Affective forecasting errors were observed in both studies. These forecasting errors were reduced with the use of targeted experience narratives (Study 2) but not representative experience narratives (Study 1). Targeted, but not representative, narratives improved the accuracy of predicted discomfort. Public collections of patient experiences should favor stories that target affective forecasting biases over stories representing the range of possible experiences. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Adjustment of regional regression models of urban-runoff quality using data for Chattanooga, Knoxville, and Nashville, Tennessee

USGS Publications Warehouse

Hoos, Anne B.; Patel, Anant R.

1996-01-01

Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.

Predictive accuracy of a ground-water model--Lessons from a postaudit

USGS Publications Warehouse

Konikow, Leonard F.

1986-01-01

Hydrogeologic studies commonly include the development, calibration, and application of a deterministic simulation model. To help assess the value of using such models to make predictions, a postaudit was conducted on a previously studied area in the Salt River and lower Santa Cruz River basins in central Arizona. A deterministic, distributed-parameter model of the ground-water system in these alluvial basins was calibrated by Anderson (1968) using about 40 years of data (1923–64). The calibrated model was then used to predict future water-level changes during the next 10 years (1965–74). Examination of actual water-level changes in 77 wells from 1965–74 indicates a poor correlation between observed and predicted water-level changes. The differences have a mean of 73 ft that is, predicted declines consistently exceeded those observed and a standard deviation of 47 ft. The bias in the predicted water-level change can be accounted for by the large error in the assumed total pumpage during the prediction period. However, the spatial distribution of errors in predicted water-level change does not correlate with the spatial distribution of errors in pumpage. Consequently, the lack of precision probably is not related only to errors in assumed pumpage, but may indicate the presence of other sources of error in the model, such as the two-dimensional representation of a three-dimensional problem or the lack of consideration of land-subsidence processes. This type of postaudit is a valuable method of verifying a model, and an evaluation of predictive errors can provide an increased understanding of the system and aid in assessing the value of undertaking development of a revised model.

Data Analysis and Its Impact on Predicting Schedule & Cost Risk

DTIC Science & Technology

2006-03-01

variance of the error term by performing a Breusch - Pagan test for constant variance (Neter et al., 1996:239). In order to test the normality of...is constant variance. Using Microsoft Excel®, we calculate a p- 68 value of 0.225678 for the Breusch - Pagan test . We again compare this p-value to...calculate a p-value of 0.121211092 Breusch - Pagan test . We again compare this p-value to an alpha of 0.05 indicating our assumption of constant variance

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yannan; Hou, Zhangshuan; Meng, Da

2016-07-17

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

Human errors and measurement uncertainty

NASA Astrophysics Data System (ADS)

Kuselman, Ilya; Pennecchi, Francesca

2015-04-01

Evaluating the residual risk of human errors in a measurement and testing laboratory, remaining after the error reduction by the laboratory quality system, and quantifying the consequences of this risk for the quality of the measurement/test results are discussed based on expert judgments and Monte Carlo simulations. A procedure for evaluation of the contribution of the residual risk to the measurement uncertainty budget is proposed. Examples are provided using earlier published sets of expert judgments on human errors in pH measurement of groundwater, elemental analysis of geological samples by inductively coupled plasma mass spectrometry, and multi-residue analysis of pesticides in fruits and vegetables. The human error contribution to the measurement uncertainty budget in the examples was not negligible, yet also not dominant. This was assessed as a good risk management result.

Moderator's view: Predictive models: a prelude to precision nephrology.

PubMed

Zoccali, Carmine

2017-05-01

Appropriate diagnosis is fundamental in medicine because it sets the basis for the prediction of disease outcome at the single patient level (prognosis) and decisions regarding the most appropriate therapy. However, given the large series of social, clinical and biological factors that determine the likelihood of an individual's future outcome, prognosis only partly depends on diagnosis and aetiology and treatment is not decided solely on the basis of the underlying diagnosis. This issue is crucial in multifactorial diseases like atherosclerosis, where the use of statins has now shifted from 'treating hypercholesterolaemia' to 'treating the risk of adverse cardiovascular events'. Approaches that take due account of prognosis limit the lingering risk of over-diagnosis and maximize the value of prognostic information in the clinical decision process. In the nephrology realm, the application of a well-validated risk equation for kidney failure in Canada led to a 35% reduction in new referrals. Prognostic models based on simple clinical data extractable from clinical files have recently been developed to predict all-cause and cardiovascular mortality in end-stage kidney disease patients. However, research on predictive models in renal diseases remains suboptimal and non-accounting for competing events and measurement errors, and a lack of calibration analyses and external validation are common fallacies in currently available studies. More focus on this blossoming research area is desirable. The nephrology community may now start to apply the best validated risk scores and further test their potential usefulness in chronic kidney disease patients in diverse clinical situations and geographical areas. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

PubMed

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

PubMed

Limongi, Roberto; Silva, Angélica M

2016-11-01

The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Tropical forecasting - Predictability perspective

NASA Technical Reports Server (NTRS)

Shukla, J.

1989-01-01

Results are presented of classical predictability studies and forecast experiments with observed initial conditions to show the nature of initial error growth and final error equilibration for the tropics and midlatitudes, separately. It is found that the theoretical upper limit of tropical circulation predictability is far less than for midlatitudes. The error growth for a complete general circulation model is compared to a dry version of the same model in which there is no prognostic equation for moisture, and diabatic heat sources are prescribed. It is found that the growth rate of synoptic-scale errors for the dry model is significantly smaller than for the moist model, suggesting that the interactions between dynamics and moist processes are among the important causes of atmospheric flow predictability degradation. Results are then presented of numerical experiments showing that correct specification of the slowly varying boundary condition of SST produces significant improvement in the prediction of time-averaged circulation and rainfall over the tropics.

Generalized Variance Function Applications in Forestry

Treesearch

James Alegria; Charles T. Scott; Charles T. Scott

1991-01-01

Adequately predicting the sampling errors of tabular data can reduce printing costs by eliminating the need to publish separate sampling error tables. Two generalized variance functions (GVFs) found in the literature and three GVFs derived for this study were evaluated for their ability to predict the sampling error of tabular forestry estimates. The recommended GVFs...

Problems in evaluating radiation dose via terrestrial and aquatic pathways.

PubMed Central

Vaughan, B E; Soldat, J K; Schreckhise, R G; Watson, E C; McKenzie, D H

1981-01-01

This review is concerned with exposure risk and the environmental pathways models used for predictive assessment of radiation dose. Exposure factors, the adequacy of available data, and the model subcomponents are critically reviewed from the standpoint of absolute error propagation. Although the models are inherently capable of better absolute accuracy, a calculated dose is usually overestimated by from two to six orders of magnitude, in practice. The principal reason for so large an error lies in using "generic" concentration ratios in situations where site specific data are needed. Major opinion of the model makers suggests a number midway between these extremes, with only a small likelihood of ever underestimating the radiation dose. Detailed evaluations are made of source considerations influencing dose (i.e., physical and chemical status of released material); dispersal mechanisms (atmospheric, hydrologic and biotic vector transport); mobilization and uptake mechanisms (i.e., chemical and other factors affecting the biological availability of radioelements); and critical pathways. Examples are shown of confounding in food-chain pathways, due to uncritical application of concentration ratios. Current thoughts of replacing the critical pathways approach to calculating dose with comprehensive model calculations are also shown to be ill-advised, given present limitations in the comprehensive data base. The pathways models may also require improved parametrization, as they are not at present structured adequately to lend themselves to validation. The extremely wide errors associated with predicting exposure stand in striking contrast to the error range associated with the extrapolation of animal effects data to the human being. PMID:7037381

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Brain negativity as an indicator of predictive error processing: the contribution of visual action effect monitoring.

PubMed

Joch, Michael; Hegele, Mathias; Maurer, Heiko; Müller, Hermann; Maurer, Lisa Katharina

2017-07-01

The error (related) negativity (Ne/ERN) is an event-related potential in the electroencephalogram (EEG) correlating with error processing. Its conditions of appearance before terminal external error information suggest that the Ne/ERN is indicative of predictive processes in the evaluation of errors. The aim of the present study was to specifically examine the Ne/ERN in a complex motor task and to particularly rule out other explaining sources of the Ne/ERN aside from error prediction processes. To this end, we focused on the dependency of the Ne/ERN on visual monitoring about the action outcome after movement termination but before result feedback (action effect monitoring). Participants performed a semi-virtual throwing task by using a manipulandum to throw a virtual ball displayed on a computer screen to hit a target object. Visual feedback about the ball flying to the target was masked to prevent action effect monitoring. Participants received a static feedback about the action outcome (850 ms) after each trial. We found a significant negative deflection in the average EEG curves of the error trials peaking at ~250 ms after ball release, i.e., before error feedback. Furthermore, this Ne/ERN signal did not depend on visual ball-flight monitoring after release. We conclude that the Ne/ERN has the potential to indicate error prediction in motor tasks and that it exists even in the absence of action effect monitoring. NEW & NOTEWORTHY In this study, we are separating different kinds of possible contributors to an electroencephalogram (EEG) error correlate (Ne/ERN) in a throwing task. We tested the influence of action effect monitoring on the Ne/ERN amplitude in the EEG. We used a task that allows us to restrict movement correction and action effect monitoring and to control the onset of result feedback. We ascribe the Ne/ERN to predictive error processing where a conscious feeling of failure is not a prerequisite. Copyright © 2017 the American Physiological Society.

Preseason screening of shoulder range of motion and humeral retrotorsion does not predict injury in high school baseball players.

PubMed

Oyama, Sakiko; Hibberd, Elizabeth E; Myers, Joseph B

2017-07-01

Shoulder and elbow injuries are commonplace in high school baseball. Although altered shoulder range of motion (ROM) and humeral retrotorsion angles have been associated with injuries, the efficacy of preseason screening of these characteristics remains controversial. We conducted preseason screenings for shoulder internal and external rotation ROM and humeral retrotorsion on 832 high school baseball players and tracked their exposure and incidence on throwing-related shoulder and elbow injuries during a subsequent season. Poisson regression with robust error variance was used to determine whether preseason screening could identify injury risk in baseball players and whether the injury risk was higher for pitchers compared with players who do not pitch. Shoulder rotation ROM or humeral retrotorsion at preseason did not predict the risk of throwing-related upper extremity injury (P = .15-.89). Injury risk was 3.84 higher for baseball players who pitched compared with those who did not (95% confidence interval, 1.72-8.56; P = .001). Preseason measures of shoulder ROM and humeral retrotorsion may not be effective in identifying players who are at increased injury risk. Because shoulder ROM is a measure that fluctuates under a variety of influences, future study should investigate whether taking multiple measurements during a season can identify at-risk players. The usefulness of preseason screening may also depend on rigor of participation in sports. Future studies should investigate how preseason shoulder characteristics and participation factors (ie, pitch count and frequency, competitive level, pitching in multiple leagues) interact to predict injury risk in baseball players. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

SEC proton prediction model: verification and analysis.

PubMed

Balch, C C

1999-06-01

This paper describes a model that has been used at the NOAA Space Environment Center since the early 1970s as a guide for the prediction of solar energetic particle events. The algorithms for proton event probability, peak flux, and rise time are described. The predictions are compared with observations. The current model shows some ability to distinguish between proton event associated flares and flares that are not associated with proton events. The comparisons of predicted and observed peak flux show considerable scatter, with an rms error of almost an order of magnitude. Rise time comparisons also show scatter, with an rms error of approximately 28 h. The model algorithms are analyzed using historical data and improvements are suggested. Implementation of the algorithm modifications reduces the rms error in the log10 of the flux prediction by 21%, and the rise time rms error by 31%. Improvements are also realized in the probability prediction by deriving the conditional climatology for proton event occurrence given flare characteristics.

Predictability of the Arctic sea ice edge

NASA Astrophysics Data System (ADS)

Goessling, H. F.; Tietsche, S.; Day, J. J.; Hawkins, E.; Jung, T.

2016-02-01

Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the "truth" disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.

The prediction of speech intelligibility in classrooms using computer models

NASA Astrophysics Data System (ADS)

Dance, Stephen; Dentoni, Roger

2005-04-01

Two classrooms were measured and modeled using the industry standard CATT model and the Web model CISM. Sound levels, reverberation times and speech intelligibility were predicted in these rooms using data for 7 octave bands. It was found that overall sound levels could be predicted to within 2 dB by both models. However, overall reverberation time was found to be accurately predicted by CATT 14% prediction error, but not by CISM, 41% prediction error. This compared to a 30% prediction error using classical theory. As for STI: CATT predicted within 11%, CISM to within 3% and Sabine to within 28% of the measured value. It should be noted that CISM took approximately 15 seconds to calculate, while CATT took 15 minutes. CISM is freely available on-line at www.whyverne.co.uk/acoustics/Pages/cism/cism.html

Implementation and evaluation of a community-based medication reconciliation (CMR) system at the hospital-community interface of care.

PubMed

Bailey, Allan L; Moe, Grace; Moe, Jessica; Oland, Ryan

2009-01-01

The WestView community-based medication reconciliation (CMR) aims to decrease medication error risk. A clinical pharmacist visits patients' homes within 72 hours of hospital discharge and compares medications in discharge orders, family physicians' charts, community pharmacy profiles and in the home. Discrepancies are discussed and reconciled with the dispenser, hospital prescriber and follow-up care provider. The CMR demonstrates successful integration that is patient-centred and standardized, bridging the hospital-community interface and improving information flow and communication channels across a family-physician-led multi-disciplinary team. A concurrent research study will evaluate the impact of CMR on health services utilization and to develop a risk prediction model.

Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy.

PubMed

Wang, Jiazhou; Jin, Xiance; Zhao, Kuaike; Peng, Jiayuan; Xie, Jiang; Chen, Junchao; Zhang, Zhen; Studenski, Matthew; Hu, Weigang

2015-02-01

To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient's anatomic and dosimetric parameters for esophageal cancer patients. Eighty esophagus patients in the authors' institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman's rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis. It is feasible to use patients' anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.

Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiazhou; Zhao, Kuaike; Peng, Jiayuan

2015-02-15

Purpose: To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient’s anatomic and dosimetric parameters for esophageal cancer patients. Methods: Eighty esophagus patients in the authors’ institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlapmore » volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman’s rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. Results: With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis. Conclusions: It is feasible to use patients’ anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.« less

Automated body weight prediction of dairy cows using 3-dimensional vision.

PubMed

Song, X; Bokkers, E A M; van der Tol, P P J; Groot Koerkamp, P W G; van Mourik, S

2018-05-01

The objectives of this study were to quantify the error of body weight prediction using automatically measured morphological traits in a 3-dimensional (3-D) vision system and to assess the influence of various sources of uncertainty on body weight prediction. In this case study, an image acquisition setup was created in a cow selection box equipped with a top-view 3-D camera. Morphological traits of hip height, hip width, and rump length were automatically extracted from the raw 3-D images taken of the rump area of dairy cows (n = 30). These traits combined with days in milk, age, and parity were used in multiple linear regression models to predict body weight. To find the best prediction model, an exhaustive feature selection algorithm was used to build intermediate models (n = 63). Each model was validated by leave-one-out cross-validation, giving the root mean square error and mean absolute percentage error. The model consisting of hip width (measurement variability of 0.006 m), days in milk, and parity was the best model, with the lowest errors of 41.2 kg of root mean square error and 5.2% mean absolute percentage error. Our integrated system, including the image acquisition setup, image analysis, and the best prediction model, predicted the body weights with a performance similar to that achieved using semi-automated or manual methods. Moreover, the variability of our simplified morphological trait measurement showed a negligible contribution to the uncertainty of body weight prediction. We suggest that dairy cow body weight prediction can be improved by incorporating more predictive morphological traits and by improving the prediction model structure. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Decision making in child protective services: a risky business?

PubMed

Camasso, Michael J; Jagannathan, Radha

2013-09-01

Child Protective Services (CPS) in the United States has received a torrent of criticism from politicians, the media, child advocate groups, and the general public for a perceived propensity to make decisions that are detrimental to children and families. This perception has resulted in numerous lawsuits and court takeovers of CPS in 35 states, and calls for profound restructuring in other states. A widely prescribed remedy for decision errors and faulty judgments is an improvement of risk assessment strategies that enhance hazard evaluation through an improved understanding of threat potentials and exposure likelihoods. We examine the reliability and validity problems that continue to plague current CPS risk assessment and discuss actions that can be taken in the field, including the use of receiver operating characteristic (ROC) curve technology to improve the predictive validity of risk assessment strategies. © 2012 Society for Risk Analysis.

Flight Test Results: CTAS Cruise/Descent Trajectory Prediction Accuracy for En route ATC Advisories

NASA Technical Reports Server (NTRS)

Green, S.; Grace, M.; Williams, D.

1999-01-01

The Center/TRACON Automation System (CTAS), under development at NASA Ames Research Center, is designed to assist controllers with the management and control of air traffic transitioning to/from congested airspace. This paper focuses on the transition from the en route environment, to high-density terminal airspace, under a time-based arrival-metering constraint. Two flight tests were conducted at the Denver Air Route Traffic Control Center (ARTCC) to study trajectory-prediction accuracy, the key to accurate Decision Support Tool advisories such as conflict detection/resolution and fuel-efficient metering conformance. In collaboration with NASA Langley Research Center, these test were part of an overall effort to research systems and procedures for the integration of CTAS and flight management systems (FMS). The Langley Transport Systems Research Vehicle Boeing 737 airplane flew a combined total of 58 cruise-arrival trajectory runs while following CTAS clearance advisories. Actual trajectories of the airplane were compared to CTAS and FMS predictions to measure trajectory-prediction accuracy and identify the primary sources of error for both. The research airplane was used to evaluate several levels of cockpit automation ranging from conventional avionics to a performance-based vertical navigation (VNAV) FMS. Trajectory prediction accuracy was analyzed with respect to both ARTCC radar tracking and GPS-based aircraft measurements. This paper presents detailed results describing the trajectory accuracy and error sources. Although differences were found in both accuracy and error sources, CTAS accuracy was comparable to the FMS in terms of both meter-fix arrival-time performance (in support of metering) and 4D-trajectory prediction (key to conflict prediction). Overall arrival time errors (mean plus standard deviation) were measured to be approximately 24 seconds during the first flight test (23 runs) and 15 seconds during the second flight test (25 runs). The major source of error during these tests was found to be the predicted winds aloft used by CTAS. Position and velocity estimates of the airplane provided to CTAS by the ATC Host radar tracker were found to be a relatively insignificant error source for the trajectory conditions evaluated. Airplane performance modeling errors within CTAS were found to not significantly affect arrival time errors when the constrained descent procedures were used. The most significant effect related to the flight guidance was observed to be the cross-track and turn-overshoot errors associated with conventional VOR guidance. Lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and aircraft performance model errors.

Impacts of Earth rotation parameters on GNSS ultra-rapid orbit prediction: Derivation and real-time correction

NASA Astrophysics Data System (ADS)

Wang, Qianxin; Hu, Chao; Xu, Tianhe; Chang, Guobin; Hernández Moraleda, Alberto

2017-12-01

Analysis centers (ACs) for global navigation satellite systems (GNSSs) cannot accurately obtain real-time Earth rotation parameters (ERPs). Thus, the prediction of ultra-rapid orbits in the international terrestrial reference system (ITRS) has to utilize the predicted ERPs issued by the International Earth Rotation and Reference Systems Service (IERS) or the International GNSS Service (IGS). In this study, the accuracy of ERPs predicted by IERS and IGS is analyzed. The error of the ERPs predicted for one day can reach 0.15 mas and 0.053 ms in polar motion and UT1-UTC direction, respectively. Then, the impact of ERP errors on ultra-rapid orbit prediction by GNSS is studied. The methods for orbit integration and frame transformation in orbit prediction with introduced ERP errors dominate the accuracy of the predicted orbit. Experimental results show that the transformation from the geocentric celestial references system (GCRS) to ITRS exerts the strongest effect on the accuracy of the predicted ultra-rapid orbit. To obtain the most accurate predicted ultra-rapid orbit, a corresponding real-time orbit correction method is developed. First, orbits without ERP-related errors are predicted on the basis of ITRS observed part of ultra-rapid orbit for use as reference. Then, the corresponding predicted orbit is transformed from GCRS to ITRS to adjust for the predicted ERPs. Finally, the corrected ERPs with error slopes are re-introduced to correct the predicted orbit in ITRS. To validate the proposed method, three experimental schemes are designed: function extrapolation, simulation experiments, and experiments with predicted ultra-rapid orbits and international GNSS Monitoring and Assessment System (iGMAS) products. Experimental results show that using the proposed correction method with IERS products considerably improved the accuracy of ultra-rapid orbit prediction (except the geosynchronous BeiDou orbits). The accuracy of orbit prediction is enhanced by at least 50% (error related to ERP) when a highly accurate observed orbit is used with the correction method. For iGMAS-predicted orbits, the accuracy improvement ranges from 8.5% for the inclined BeiDou orbits to 17.99% for the GPS orbits. This demonstrates that the correction method proposed by this study can optimize the ultra-rapid orbit prediction.

Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy

NASA Astrophysics Data System (ADS)

Valdes, Gilmer; Solberg, Timothy D.; Heskel, Marina; Ungar, Lyle; Simone, Charles B., II

2016-08-01

To develop a patient-specific ‘big data’ clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). 61 features were recorded for 201 consecutive patients with stage I NSCLC treated with SBRT, in whom 8 (4.0%) developed radiation pneumonitis. Pneumonitis thresholds were found for each feature individually using decision stumps. The performance of three different algorithms (Decision Trees, Random Forests, RUSBoost) was evaluated. Learning curves were developed and the training error analyzed and compared to the testing error in order to evaluate the factors needed to obtain a cross-validated error smaller than 0.1. These included the addition of new features, increasing the complexity of the algorithm and enlarging the sample size and number of events. In the univariate analysis, the most important feature selected was the diffusion capacity of the lung for carbon monoxide (DLCO adj%). On multivariate analysis, the three most important features selected were the dose to 15 cc of the heart, dose to 4 cc of the trachea or bronchus, and race. Higher accuracy could be achieved if the RUSBoost algorithm was used with regularization. To predict radiation pneumonitis within an error smaller than 10%, we estimate that a sample size of 800 patients is required. Clinically relevant thresholds that put patients at risk of developing radiation pneumonitis were determined in a cohort of 201 stage I NSCLC patients treated with SBRT. The consistency of these thresholds can provide radiation oncologists with an estimate of their reliability and may inform treatment planning and patient counseling. The accuracy of the classification is limited by the number of patients in the study and not by the features gathered or the complexity of the algorithm.

Error-related negativities elicited by monetary loss and cues that predict loss.

PubMed

Dunning, Jonathan P; Hajcak, Greg

2007-11-19

Event-related potential studies have reported error-related negativity following both error commission and feedback indicating errors or monetary loss. The present study examined whether error-related negativities could be elicited by a predictive cue presented prior to both the decision and subsequent feedback in a gambling task. Participants were presented with a cue that indicated the probability of reward on the upcoming trial (0, 50, and 100%). Results showed a negative deflection in the event-related potential in response to loss cues compared with win cues; this waveform shared a similar latency and morphology with the traditional feedback error-related negativity.

Engineering Test Report Paint Waste Reduction Fluidized Bed Process Demonstration at Letterkenny Army Depot Chambersburg, Pennsylvania

DTIC Science & Technology

1991-07-01

predicted by equation using actual chart response obtained from each calibration gas response. (Concentration of cal. gas,l Calibration error, % span • ppm...Analyzer predicted by cali- Col. gas Chart divisions equation* bration Cylinder conc., error,** Drift,***INo. ppm or % Pretest Posttest Pretest Posttest...2m ~J * Correlation coef. * qgq’jq **Analyzer ca.error, % spn (Cal. gas conc. conc. predicted ) x 1003 cal spanSpan value Acceptable limit x ɚ% of

Dopamine reward prediction-error signalling: a two-component response

PubMed Central

Schultz, Wolfram

2017-01-01

Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy. PMID:26865020

Chain pooling to minimize prediction error in subset regression. [Monte Carlo studies using population models

NASA Technical Reports Server (NTRS)

Holms, A. G.

1974-01-01

Monte Carlo studies using population models intended to represent response surface applications are reported. Simulated experiments were generated by adding pseudo random normally distributed errors to population values to generate observations. Model equations were fitted to the observations and the decision procedure was used to delete terms. Comparison of values predicted by the reduced models with the true population values enabled the identification of deletion strategies that are approximately optimal for minimizing prediction errors.

Prediction of the Possibility a Right-Turn Driving Behavior at Intersection Leads to an Accident by Detecting Deviation of the Situation from Usual when the Behavior is Observed

NASA Astrophysics Data System (ADS)

Hayashi, Toshinori; Yamada, Keiichi

Deviation of driving behavior from usual could be a sign of human error that increases the risk of traffic accidents. This paper proposes a novel method for predicting the possibility a driving behavior leads to an accident from the information on the driving behavior and the situation. In a previous work, a method of predicting the possibility by detecting the deviation of driving behavior from usual one in that situation has been proposed. In contrast, the method proposed in this paper predicts the possibility by detecting the deviation of the situation from usual one when the behavior is observed. An advantage of the proposed method is the number of the required models is independent of the variety of the situations. The method was applied to a problem of predicting accidents by right-turn driving behavior at an intersection, and the performance of the method was evaluated by experiments on a driving simulator.

Neural correlates of sensory prediction errors in monkeys: evidence for internal models of voluntary self-motion in the cerebellum.

PubMed

Cullen, Kathleen E; Brooks, Jessica X

2015-02-01

During self-motion, the vestibular system makes essential contributions to postural stability and self-motion perception. To ensure accurate perception and motor control, it is critical to distinguish between vestibular sensory inputs that are the result of externally applied motion (exafference) and that are the result of our own actions (reafference). Indeed, although the vestibular sensors encode vestibular afference and reafference with equal fidelity, neurons at the first central stage of sensory processing selectively encode vestibular exafference. The mechanism underlying this reafferent suppression compares the brain's motor-based expectation of sensory feedback with the actual sensory consequences of voluntary self-motion, effectively computing the sensory prediction error (i.e., exafference). It is generally thought that sensory prediction errors are computed in the cerebellum, yet it has been challenging to explicitly demonstrate this. We have recently addressed this question and found that deep cerebellar nuclei neurons explicitly encode sensory prediction errors during self-motion. Importantly, in everyday life, sensory prediction errors occur in response to changes in the effector or world (muscle strength, load, etc.), as well as in response to externally applied sensory stimulation. Accordingly, we hypothesize that altering the relationship between motor commands and the actual movement parameters will result in the updating in the cerebellum-based computation of exafference. If our hypothesis is correct, under these conditions, neuronal responses should initially be increased--consistent with a sudden increase in the sensory prediction error. Then, over time, as the internal model is updated, response modulation should decrease in parallel with a reduction in sensory prediction error, until vestibular reafference is again suppressed. The finding that the internal model predicting the sensory consequences of motor commands adapts for new relationships would have important implications for understanding how responses to passive stimulation endure despite the cerebellum's ability to learn new relationships between motor commands and sensory feedback.

NASA Model of "Threat and Error" in Pediatric Cardiac Surgery: Patterns of Error Chains.

PubMed

Hickey, Edward; Pham-Hung, Eric; Nosikova, Yaroslavna; Halvorsen, Fredrik; Gritti, Michael; Schwartz, Steven; Caldarone, Christopher A; Van Arsdell, Glen

2017-04-01

We introduced the National Aeronautics and Space Association threat-and-error model to our surgical unit. All admissions are considered flights, which should pass through stepwise deescalations in risk during surgical recovery. We hypothesized that errors significantly influence risk deescalation and contribute to poor outcomes. Patient flights (524) were tracked in real time for threats, errors, and unintended states by full-time performance personnel. Expected risk deescalation was wean from mechanical support, sternal closure, extubation, intensive care unit (ICU) discharge, and discharge home. Data were accrued from clinical charts, bedside data, reporting mechanisms, and staff interviews. Infographics of flights were openly discussed weekly for consensus. In 12% (64 of 524) of flights, the child failed to deescalate sequentially through expected risk levels; unintended increments instead occurred. Failed deescalations were highly associated with errors (426; 257 flights; p < 0.0001). Consequential errors (263; 173 flights) were associated with a 29% rate of failed deescalation versus 4% in flights with no consequential error (p < 0.0001). The most dangerous errors were apical errors typically (84%) occurring in the operating room, which caused chains of propagating unintended states (n = 110): these had a 43% (47 of 110) rate of failed deescalation (versus 4%; p < 0.0001). Chains of unintended state were often (46%) amplified by additional (up to 7) errors in the ICU that would worsen clinical deviation. Overall, failed deescalations in risk were extremely closely linked to brain injury (n = 13; p < 0.0001) or death (n = 7; p < 0.0001). Deaths and brain injury after pediatric cardiac surgery almost always occur from propagating error chains that originate in the operating room and are often amplified by additional ICU errors. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Converting international ¼ inch tree volume to Doyle

Treesearch

Aaron Holley; John R. Brooks; Stuart A. Moss

2014-01-01

An equation for converting Mesavage and Girard's International ¼ inch tree volumes to the Doyle log rule is presented as a function of tree diameter. Volume error for trees having less than four logs exhibited volume prediction errors within a range of ±10 board feet. In addition, volume prediction error as a percent of actual Doyle tree volume...

Long Term Mean Local Time of the Ascending Node Prediction

NASA Technical Reports Server (NTRS)

McKinley, David P.

2007-01-01

Significant error has been observed in the long term prediction of the Mean Local Time of the Ascending Node on the Aqua spacecraft. This error of approximately 90 seconds over a two year prediction is a complication in planning and timing of maneuvers for all members of the Earth Observing System Afternoon Constellation, which use Aqua's MLTAN as the reference for their inclination maneuvers. It was determined that the source of the prediction error was the lack of a solid Earth tide model in the operational force models. The Love Model of the solid Earth tide potential was used to derive analytic corrections to the inclination and right ascension of the ascending node of Aqua's Sun-synchronous orbit. Additionally, it was determined that the resonance between the Sun and orbit plane of the Sun-synchronous orbit is the primary driver of this error. The analytic corrections have been added to the operational force models for the Aqua spacecraft reducing the two-year 90-second error to less than 7 seconds.

Efficient Reduction and Analysis of Model Predictive Error

NASA Astrophysics Data System (ADS)

Doherty, J.

2006-12-01

Most groundwater models are calibrated against historical measurements of head and other system states before being used to make predictions in a real-world context. Through the calibration process, parameter values are estimated or refined such that the model is able to reproduce historical behaviour of the system at pertinent observation points reasonably well. Predictions made by the model are deemed to have greater integrity because of this. Unfortunately, predictive integrity is not as easy to achieve as many groundwater practitioners would like to think. The level of parameterisation detail estimable through the calibration process (especially where estimation takes place on the basis of heads alone) is strictly limited, even where full use is made of modern mathematical regularisation techniques such as those encapsulated in the PEST calibration package. (Use of these mechanisms allows more information to be extracted from a calibration dataset than is possible using simpler regularisation devices such as zones of piecewise constancy.) Where a prediction depends on aspects of parameterisation detail that are simply not inferable through the calibration process (which is often the case for predictions related to contaminant movement, and/or many aspects of groundwater/surface water interaction), then that prediction may be just as much in error as it would have been if the model had not been calibrated at all. Model predictive error arises from two sources. These are (a) the presence of measurement noise within the calibration dataset through which linear combinations of parameters spanning the "calibration solution space" are inferred, and (b) the sensitivity of the prediction to members of the "calibration null space" spanned by linear combinations of parameters which are not inferable through the calibration process. The magnitude of the former contribution depends on the level of measurement noise. The magnitude of the latter contribution (which often dominates the former) depends on the "innate variability" of hydraulic properties within the model domain. Knowledge of both of these is a prerequisite for characterisation of the magnitude of possible model predictive error. Unfortunately, in most cases, such knowledge is incomplete and subjective. Nevertheless, useful analysis of model predictive error can still take place. The present paper briefly discusses the means by which mathematical regularisation can be employed in the model calibration process in order to extract as much information as possible on hydraulic property heterogeneity prevailing within the model domain, thereby reducing predictive error to the lowest that can be achieved on the basis of that dataset. It then demonstrates the means by which predictive error variance can be quantified based on information supplied by the regularised inversion process. Both linear and nonlinear predictive error variance analysis is demonstrated using a number of real-world and synthetic examples.

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

NASA Technical Reports Server (NTRS)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Effect of ephemeris errors on the accuracy of the computation of the tangent point altitude of a solar scanning ray as measured by the SAGE 1 and 2 instruments

NASA Technical Reports Server (NTRS)

Buglia, James J.

1989-01-01

An analysis was made of the error in the minimum altitude of a geometric ray from an orbiting spacecraft to the Sun. The sunrise and sunset errors are highly correlated and are opposite in sign. With the ephemeris generated for the SAGE 1 instrument data reduction, these errors can be as large as 200 to 350 meters (1 sigma) after 7 days of orbit propagation. The bulk of this error results from errors in the position of the orbiting spacecraft rather than errors in computing the position of the Sun. These errors, in turn, result from the discontinuities in the ephemeris tapes resulting from the orbital determination process. Data taken from the end of the definitive ephemeris tape are used to generate the predict data for the time interval covered by the next arc of the orbit determination process. The predicted data are then updated by using the tracking data. The growth of these errors is very nearly linear, with a slight nonlinearity caused by the beta angle. An approximate analytic method is given, which predicts the magnitude of the errors and their growth in time with reasonable fidelity.

Sonographic estimation of fetal weight: comparison of bias, precision and consistency using 12 different formulae.

PubMed

Anderson, N G; Jolley, I J; Wells, J E

2007-08-01

To determine the major sources of error in ultrasonographic assessment of fetal weight and whether they have changed over the last decade. We performed a prospective observational study in 1991 and again in 2000 of a mixed-risk pregnancy population, estimating fetal weight within 7 days of delivery. In 1991, the Rose and McCallum formula was used for 72 deliveries. Inter- and intraobserver agreement was assessed within this group. Bland-Altman measures of agreement from log data were calculated as ratios. We repeated the study in 2000 in 208 consecutive deliveries, comparing predicted and actual weights for 12 published equations using Bland-Altman and percentage error methods. We compared bias (mean percentage error), precision (SD percentage error), and their consistency across the weight ranges. 95% limits of agreement ranged from - 4.4% to + 3.3% for inter- and intraobserver estimates, but were - 18.0% to 24.0% for estimated and actual birth weight. There was no improvement in accuracy between 1991 and 2000. In 2000 only six of the 12 published formulae had overall bias within 7% and precision within 15%. There was greater bias and poorer precision in nearly all equations if the birth weight was < 1,000 g. Observer error is a relatively minor component of the error in estimating fetal weight; error due to the equation is a larger source of error. Improvements in ultrasound technology have not improved the accuracy of estimating fetal weight. Comparison of methods of estimating fetal weight requires statistical methods that can separate out bias, precision and consistency. Estimating fetal weight in the very low birth weight infant is subject to much greater error than it is in larger babies. Copyright (c) 2007 ISUOG. Published by John Wiley & Sons, Ltd.

A Neurobehavioral Mechanism Linking Behaviorally Inhibited Temperament and Later Adolescent Social Anxiety.

PubMed

Buzzell, George A; Troller-Renfree, Sonya V; Barker, Tyson V; Bowman, Lindsay C; Chronis-Tuscano, Andrea; Henderson, Heather A; Kagan, Jerome; Pine, Daniel S; Fox, Nathan A

2017-12-01

Behavioral inhibition (BI) is a temperament identified in early childhood that is a risk factor for later social anxiety. However, mechanisms underlying the development of social anxiety remain unclear. To better understand the emergence of social anxiety, longitudinal studies investigating changes at behavioral neural levels are needed. BI was assessed in the laboratory at 2 and 3 years of age (N = 268). Children returned at 12 years, and an electroencephalogram was recorded while children performed a flanker task under 2 conditions: once while believing they were being observed by peers and once while not being observed. This methodology isolated changes in error monitoring (error-related negativity) and behavior (post-error reaction time slowing) as a function of social context. At 12 years, current social anxiety symptoms and lifetime diagnoses of social anxiety were obtained. Childhood BI prospectively predicted social-specific error-related negativity increases and social anxiety symptoms in adolescence; these symptoms directly related to clinical diagnoses. Serial mediation analysis showed that social error-related negativity changes explained relations between BI and social anxiety symptoms (n = 107) and diagnosis (n = 92), but only insofar as social context also led to increased post-error reaction time slowing (a measure of error preoccupation); this model was not significantly related to generalized anxiety. Results extend prior work on socially induced changes in error monitoring and error preoccupation. These measures could index a neurobehavioral mechanism linking BI to adolescent social anxiety symptoms and diagnosis. This mechanism could relate more strongly to social than to generalized anxiety in the peri-adolescent period. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. All rights reserved.

Classification based upon gene expression data: bias and precision of error rates.

PubMed

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers.

PubMed

Darvishi, Ebrahim; Khotanlou, Hassan; Khoubi, Jamshid; Giahi, Omid; Mahdavi, Neda

2017-09-01

This study aimed to provide an empirical model of predicting low back pain (LBP) by considering the occupational, personal, and psychological risk factor interactions in workers population employed in industrial units using an artificial neural networks approach. A total of 92 workers with LBP as the case group and 68 healthy workers as a control group were selected in various industrial units with similar occupational conditions. The demographic information and personal, occupational, and psychosocial factors of the participants were collected via interview, related questionnaires, consultation with occupational medicine, and also the Rapid Entire Body Assessment worksheet and National Aeronautics and Space Administration Task Load Index software. Then, 16 risk factors for LBP were used as input variables to develop the prediction model. Networks with various multilayered structures were developed using MATLAB. The developed neural networks with 1 hidden layer and 26 neurons had the least error of classification in both training and testing phases. The mean of classification accuracy of the developed neural networks for the testing and training phase data were about 88% and 96%, respectively. In addition, the mean of classification accuracy of both training and testing data was 92%, indicating much better results compared with other methods. It appears that the prediction model using the neural network approach is more accurate compared with other applied methods. Because occupational LBP is usually untreatable, the results of prediction may be suitable for developing preventive strategies and corrective interventions. Copyright © 2017. Published by Elsevier Inc.

Interspecies scaling and prediction of human clearance: comparison of small- and macro-molecule drugs

PubMed Central

Huh, Yeamin; Smith, David E.; Feng, Meihau Rose

2014-01-01

Human clearance prediction for small- and macro-molecule drugs was evaluated and compared using various scaling methods and statistical analysis.Human clearance is generally well predicted using single or multiple species simple allometry for macro- and small-molecule drugs excreted renally.The prediction error is higher for hepatically eliminated small-molecules using single or multiple species simple allometry scaling, and it appears that the prediction error is mainly associated with drugs with low hepatic extraction ratio (Eh). The error in human clearance prediction for hepatically eliminated small-molecules was reduced using scaling methods with a correction of maximum life span (MLP) or brain weight (BRW).Human clearance of both small- and macro-molecule drugs is well predicted using the monkey liver blood flow method. Predictions using liver blood flow from other species did not work as well, especially for the small-molecule drugs. PMID:21892879

Elastic scattering spectroscopy for detection of cancer risk in Barrett's esophagus: experimental and clinical validation of error removal by orthogonal subtraction for increasing accuracy

NASA Astrophysics Data System (ADS)

Zhu, Ying; Fearn, Tom; MacKenzie, Gary; Clark, Ben; Dunn, Jason M.; Bigio, Irving J.; Bown, Stephen G.; Lovat, Laurence B.

2009-07-01

Elastic scattering spectroscopy (ESS) may be used to detect high-grade dysplasia (HGD) or cancer in Barrett's esophagus (BE). When spectra are measured in vivo by a hand-held optical probe, variability among replicated spectra from the same site can hinder the development of a diagnostic model for cancer risk. An experiment was carried out on excised tissue to investigate how two potential sources of this variability, pressure and angle, influence spectral variability, and the results were compared with the variations observed in spectra collected in vivo from patients with Barrett's esophagus. A statistical method called error removal by orthogonal subtraction (EROS) was applied to model and remove this measurement variability, which accounted for 96.6% of the variation in the spectra, from the in vivo data. Its removal allowed the construction of a diagnostic model with specificity improved from 67% to 82% (with sensitivity fixed at 90%). The improvement was maintained in predictions on an independent in vivo data set. EROS works well as an effective pretreatment for Barrett's in vivo data by identifying measurement variability and ameliorating its effect. The procedure reduces the complexity and increases the accuracy and interpretability of the model for classification and detection of cancer risk in Barrett's esophagus.

Use of machine learning methods to reduce predictive error of groundwater models.

PubMed

Xu, Tianfang; Valocchi, Albert J; Choi, Jaesik; Amir, Eyal

2014-01-01

Quantitative analyses of groundwater flow and transport typically rely on a physically-based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data-driven models (DDMs) to reduce the predictive error of physically-based groundwater models. Two machine learning techniques, the instance-based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real-world case studies of the Republican River Compact Administration model and the Spokane Valley-Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root-mean-square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically-based model. © 2013, National GroundWater Association.

Methods for estimating flood frequency in Montana based on data through water year 1998

USGS Publications Warehouse

Parrett, Charles; Johnson, Dave R.

2004-01-01

Annual peak discharges having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years (T-year floods) were determined for 660 gaged sites in Montana and in adjacent areas of Idaho, Wyoming, and Canada, based on data through water year 1998. The updated flood-frequency information was subsequently used in regression analyses, either ordinary or generalized least squares, to develop equations relating T-year floods to various basin and climatic characteristics, equations relating T-year floods to active-channel width, and equations relating T-year floods to bankfull width. The equations can be used to estimate flood frequency at ungaged sites. Montana was divided into eight regions, within which flood characteristics were considered to be reasonably homogeneous, and the three sets of regression equations were developed for each region. A measure of the overall reliability of the regression equations is the average standard error of prediction. The average standard errors of prediction for the equations based on basin and climatic characteristics ranged from 37.4 percent to 134.1 percent. Average standard errors of prediction for the equations based on active-channel width ranged from 57.2 percent to 141.3 percent. Average standard errors of prediction for the equations based on bankfull width ranged from 63.1 percent to 155.5 percent. In most regions, the equations based on basin and climatic characteristics generally had smaller average standard errors of prediction than equations based on active-channel or bankfull width. An exception was the Southeast Plains Region, where all equations based on active-channel width had smaller average standard errors of prediction than equations based on basin and climatic characteristics or bankfull width. Methods for weighting estimates derived from the basin- and climatic-characteristic equations and the channel-width equations also were developed. The weights were based on the cross correlation of residuals from the different methods and the average standard errors of prediction. When all three methods were combined, the average standard errors of prediction ranged from 37.4 percent to 120.2 percent. Weighting of estimates reduced the standard errors of prediction for all T-year flood estimates in four regions, reduced the standard errors of prediction for some T-year flood estimates in two regions, and provided no reduction in average standard error of prediction in two regions. A computer program for solving the regression equations, weighting estimates, and determining reliability of individual estimates was developed and placed on the USGS Montana District World Wide Web page. A new regression method, termed Region of Influence regression, also was tested. Test results indicated that the Region of Influence method was not as reliable as the regional equations based on generalized least squares regression. Two additional methods for estimating flood frequency at ungaged sites located on the same streams as gaged sites also are described. The first method, based on a drainage-area-ratio adjustment, is intended for use on streams where the ungaged site of interest is located near a gaged site. The second method, based on interpolation between gaged sites, is intended for use on streams that have two or more streamflow-gaging stations.

Medication Errors: New EU Good Practice Guide on Risk Minimisation and Error Prevention.

PubMed

Goedecke, Thomas; Ord, Kathryn; Newbould, Victoria; Brosch, Sabine; Arlett, Peter

2016-06-01

A medication error is an unintended failure in the drug treatment process that leads to, or has the potential to lead to, harm to the patient. Reducing the risk of medication errors is a shared responsibility between patients, healthcare professionals, regulators and the pharmaceutical industry at all levels of healthcare delivery. In 2015, the EU regulatory network released a two-part good practice guide on medication errors to support both the pharmaceutical industry and regulators in the implementation of the changes introduced with the EU pharmacovigilance legislation. These changes included a modification of the 'adverse reaction' definition to include events associated with medication errors, and the requirement for national competent authorities responsible for pharmacovigilance in EU Member States to collaborate and exchange information on medication errors resulting in harm with national patient safety organisations. To facilitate reporting and learning from medication errors, a clear distinction has been made in the guidance between medication errors resulting in adverse reactions, medication errors without harm, intercepted medication errors and potential errors. This distinction is supported by an enhanced MedDRA(®) terminology that allows for coding all stages of the medication use process where the error occurred in addition to any clinical consequences. To better understand the causes and contributing factors, individual case safety reports involving an error should be followed-up with the primary reporter to gather information relevant for the conduct of root cause analysis where this may be appropriate. Such reports should also be summarised in periodic safety update reports and addressed in risk management plans. Any risk minimisation and prevention strategy for medication errors should consider all stages of a medicinal product's life-cycle, particularly the main sources and types of medication errors during product development. This article describes the key concepts of the EU good practice guidance for defining, classifying, coding, reporting, evaluating and preventing medication errors. This guidance should contribute to the safe and effective use of medicines for the benefit of patients and public health.

Childhood Psychosocial Cumulative Risks and Carotid Intima-Media Thickness in Adulthood: The Cardiovascular Risk in Young Finns Study.

PubMed

Hakulinen, Christian; Pulkki-Råback, Laura; Elovainio, Marko; Kubzansky, Laura D; Jokela, Markus; Hintsanen, Mirka; Juonala, Markus; Kivimäki, Mika; Josefsson, Kim; Hutri-Kähönen, Nina; Kähönen, Mika; Viikari, Jorma; Keltikangas-Järvinen, Liisa; Raitakari, Olli T

2016-01-01

Adverse experiences in childhood may influence cardiovascular risk in adulthood. We examined the prospective associations between types of psychosocial adversity and having multiple adversities (e.g., cumulative risk) with carotid intima-media thickness (IMT) and its progression among young adults. Higher cumulative risk score in childhood was expected to be associated with higher IMT and its progression. Participants were 2265 men and women (age range, 24-39 years in 2001) from the ongoing Cardiovascular Risk in Young Finns study whose carotid IMTs were measured in 2001 and 2007. A cumulative psychosocial risk score, assessed at the study baseline in 1980, was derived from four separate aspects of the childhood environment that may impose risk (childhood stressful life events, parental health behavior family, socioeconomic status, and childhood emotional environment). The cumulative risk score was associated with higher IMT in 2007 (b = 0.004, standard error [SE] = 0.001, p < .001) and increased IMT progression from 2001 to 2007 (b = 0.003, SE = 0.001, p = .001). The associations were robust to adjustment for conventional cardiovascular risk factors in childhood and adulthood, including adulthood health behavior, adulthood socioeconomic status, and depressive symptoms. Among the individual childhood psychosocial risk categories, having more stressful life events was associated with higher IMT in 2001 (b = 0.007, SE = 0.003, p = .016) and poorer parental health behavior predicted higher IMT in 2007 (b = 0.004, SE = 0.002, p = .031) after adjustment for age, sex, and childhood cardiovascular risk factors. Early life psychosocial environment influences cardiovascular risk later in life, and considering cumulative childhood risk factors may be more informative than individual factors in predicting progression of preclinical atherosclerosis in adulthood.

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

PubMed

León Blanco, José M; González-R, Pedro L; Arroyo García, Carmen Martina; Cózar-Bernal, María José; Calle Suárez, Marcos; Canca Ortiz, David; Rabasco Álvarez, Antonio María; González Rodríguez, María Luisa

2018-01-01

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains

PubMed Central

Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

2017-01-01

Background Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. Objectives We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Methods Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Results Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Conclusions Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. PMID:27193033

Using Search Engine Data as a Tool to Predict Syphilis.

PubMed

Young, Sean D; Torrone, Elizabeth A; Urata, John; Aral, Sevgi O

2018-07-01

Researchers have suggested that social media and online search data might be used to monitor and predict syphilis and other sexually transmitted diseases. Because people at risk for syphilis might seek sexual health and risk-related information on the internet, we investigated associations between internet state-level search query data (e.g., Google Trends) and reported weekly syphilis cases. We obtained weekly counts of reported primary and secondary syphilis for 50 states from 2012 to 2014 from the US Centers for Disease Control and Prevention. We collected weekly internet search query data regarding 25 risk-related keywords from 2012 to 2014 for 50 states using Google Trends. We joined 155 weeks of Google Trends data with 1-week lag to weekly syphilis data for a total of 7750 data points. Using the least absolute shrinkage and selection operator, we trained three linear mixed models on the first 10 weeks of each year. We validated models for 2012 and 2014 for the following 52 weeks and the 2014 model for the following 42 weeks. The models, consisting of different sets of keyword predictors for each year, accurately predicted 144 weeks of primary and secondary syphilis counts for each state, with an overall average R of 0.9 and overall average root mean squared error of 4.9. We used Google Trends search data from the prior week to predict cases of syphilis in the following weeks for each state. Further research could explore how search data could be integrated into public health monitoring systems.

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

PubMed Central

Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

2012-01-01

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling. PMID:23483015

Improved accuracy of intraocular lens power calculation with the Zeiss IOLMaster.

PubMed

Olsen, Thomas

2007-02-01

This study aimed to demonstrate how the level of accuracy in intraocular lens (IOL) power calculation can be improved with optical biometry using partial optical coherence interferometry (PCI) (Zeiss IOLMaster) and current anterior chamber depth (ACD) prediction algorithms. Intraocular lens power in 461 consecutive cataract operations was calculated using both PCI and ultrasound and the accuracy of the results of each technique were compared. To illustrate the importance of ACD prediction per se, predictions were calculated using both a recently published 5-variable method and the Haigis 2-variable method and the results compared. All calculations were optimized in retrospect to account for systematic errors, including IOL constants and other off-set errors. The average absolute IOL prediction error (observed minus expected refraction) was 0.65 dioptres with ultrasound and 0.43 D with PCI using the 5-variable ACD prediction method (p < 0.00001). The number of predictions within +/- 0.5 D, +/- 1.0 D and +/- 2.0 D of the expected outcome was 62.5%, 92.4% and 99.9% with PCI, compared with 45.5%, 77.3% and 98.4% with ultrasound, respectively (p < 0.00001). The 2-variable ACD method resulted in an average error in PCI predictions of 0.46 D, which was significantly higher than the error in the 5-variable method (p < 0.001). The accuracy of IOL power calculation can be significantly improved using calibrated axial length readings obtained with PCI and modern IOL power calculation formulas incorporating the latest generation ACD prediction algorithms.

RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.

PubMed

Gao, Yujuan; Wang, Sheng; Deng, Minghua; Xu, Jinbo

2018-05-08

Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging. In this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds. Our study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.

Development of Predictive Energy Management Strategies for Hybrid Electric Vehicles

NASA Astrophysics Data System (ADS)

Baker, David

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods.

Design and analysis of multihypothesis motion-compensated prediction (MHMCP) codec for error-resilient visual communications

NASA Astrophysics Data System (ADS)

Kung, Wei-Ying; Kim, Chang-Su; Kuo, C.-C. Jay

2004-10-01

A multi-hypothesis motion compensated prediction (MHMCP) scheme, which predicts a block from a weighted superposition of more than one reference blocks in the frame buffer, is proposed and analyzed for error resilient visual communication in this research. By combining these reference blocks effectively, MHMCP can enhance the error resilient capability of compressed video as well as achieve a coding gain. In particular, we investigate the error propagation effect in the MHMCP coder and analyze the rate-distortion performance in terms of the hypothesis number and hypothesis coefficients. It is shown that MHMCP suppresses the short-term effect of error propagation more effectively than the intra refreshing scheme. Simulation results are given to confirm the analysis. Finally, several design principles for the MHMCP coder are derived based on the analytical and experimental results.

Development and Validation of a qRT-PCR Classifier for Lung Cancer Prognosis

PubMed Central

Chen, Guoan; Kim, Sinae; Taylor, Jeremy MG; Wang, Zhuwen; Lee, Oliver; Ramnath, Nithya; Reddy, Rishindra M; Lin, Jules; Chang, Andrew C; Orringer, Mark B; Beer, David G

2011-01-01

Purpose This prospective study aimed to develop a robust and clinically-applicable method to identify high-risk early stage lung cancer patients and then to validate this method for use in future translational studies. Patients and Methods Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model and random survival forest (RSF) analysis. A final set of 91 genes was selected and tested as a predictor of survival using a qRT-PCR-based assay utilizing an independent cohort of 101 lung adenocarcinomas. Results The RSF model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index (MRI) was significantly related to survival (Cox model p < 0.00001) and separated all patients into low, medium, and high-risk groups (HR = 1.00, 2.82, 4.42). The MRI was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low, medium, and high-risk groups (HR = 1.00, 3.29, 3.77). Conclusions The development and validation of this robust qRT-PCR platform allows prediction of patient survival with early stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival. PMID:21792073

The statistical properties and possible causes of polar motion prediction errors

NASA Astrophysics Data System (ADS)

Kosek, Wieslaw; Kalarus, Maciej; Wnek, Agnieszka; Zbylut-Gorska, Maria

2015-08-01

The pole coordinate data predictions from different prediction contributors of the Earth Orientation Parameters Combination of Prediction Pilot Project (EOPCPPP) were studied to determine the statistical properties of polar motion forecasts by looking at the time series of differences between them and the future IERS pole coordinates data. The mean absolute errors, standard deviations as well as the skewness and kurtosis of these differences were computed together with their error bars as a function of prediction length. The ensemble predictions show a little smaller mean absolute errors or standard deviations however their skewness and kurtosis values are similar as the for predictions from different contributors. The skewness and kurtosis enable to check whether these prediction differences satisfy normal distribution. The kurtosis values diminish with the prediction length which means that the probability distribution of these prediction differences is becoming more platykurtic than letptokurtic. Non zero skewness values result from oscillating character of these differences for particular prediction lengths which can be due to the irregular change of the annual oscillation phase in the joint fluid (atmospheric + ocean + land hydrology) excitation functions. The variations of the annual oscillation phase computed by the combination of the Fourier transform band pass filter and the Hilbert transform from pole coordinates data as well as from pole coordinates model data obtained from fluid excitations are in a good agreement.

ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective

NASA Astrophysics Data System (ADS)

Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua

2018-07-01

Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (socalled "sensitive areas"). And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Niño prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni˜no prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year, increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

The effects of recall errors and of selection bias in epidemiologic studies of mobile phone use and cancer risk.

PubMed

Vrijheid, Martine; Deltour, Isabelle; Krewski, Daniel; Sanchez, Marie; Cardis, Elisabeth

2006-07-01

This paper examines the effects of systematic and random errors in recall and of selection bias in case-control studies of mobile phone use and cancer. These sensitivity analyses are based on Monte-Carlo computer simulations and were carried out within the INTERPHONE Study, an international collaborative case-control study in 13 countries. Recall error scenarios simulated plausible values of random and systematic, non-differential and differential recall errors in amount of mobile phone use reported by study subjects. Plausible values for the recall error were obtained from validation studies. Selection bias scenarios assumed varying selection probabilities for cases and controls, mobile phone users, and non-users. Where possible these selection probabilities were based on existing information from non-respondents in INTERPHONE. Simulations used exposure distributions based on existing INTERPHONE data and assumed varying levels of the true risk of brain cancer related to mobile phone use. Results suggest that random recall errors of plausible levels can lead to a large underestimation in the risk of brain cancer associated with mobile phone use. Random errors were found to have larger impact than plausible systematic errors. Differential errors in recall had very little additional impact in the presence of large random errors. Selection bias resulting from underselection of unexposed controls led to J-shaped exposure-response patterns, with risk apparently decreasing at low to moderate exposure levels. The present results, in conjunction with those of the validation studies conducted within the INTERPHONE study, will play an important role in the interpretation of existing and future case-control studies of mobile phone use and cancer risk, including the INTERPHONE study.

Driving Errors in Parkinson’s Disease: Moving Closer to Predicting On-Road Outcomes

PubMed Central

Brumback, Babette; Monahan, Miriam; Malaty, Irene I.; Rodriguez, Ramon L.; Okun, Michael S.; McFarland, Nikolaus R.

2014-01-01

Age-related medical conditions such as Parkinson’s disease (PD) compromise driver fitness. Results from studies are unclear on the specific driving errors that underlie passing or failing an on-road assessment. In this study, we determined the between-group differences and quantified the on-road driving errors that predicted pass or fail on-road outcomes in 101 drivers with PD (mean age = 69.38 ± 7.43) and 138 healthy control (HC) drivers (mean age = 71.76 ± 5.08). Participants with PD had minor differences in demographics and driving habits and history but made more and different driving errors than HC participants. Drivers with PD failed the on-road test to a greater extent than HC drivers (41% vs. 9%), χ2(1) = 35.54, HC N = 138, PD N = 99, p < .001. The driving errors predicting on-road pass or fail outcomes (95% confidence interval, Nagelkerke R2 =.771) were made in visual scanning, signaling, vehicle positioning, speeding (mainly underspeeding, t(61) = 7.004, p < .001, and total errors. Although it is difficult to predict on-road outcomes, this study provides a foundation for doing so. PMID:24367958

Prediction of human errors by maladaptive changes in event-related brain networks.

PubMed

Eichele, Tom; Debener, Stefan; Calhoun, Vince D; Specht, Karsten; Engel, Andreas K; Hugdahl, Kenneth; von Cramon, D Yves; Ullsperger, Markus

2008-04-22

Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve approximately 30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations.

Prediction of human errors by maladaptive changes in event-related brain networks

PubMed Central

Eichele, Tom; Debener, Stefan; Calhoun, Vince D.; Specht, Karsten; Engel, Andreas K.; Hugdahl, Kenneth; von Cramon, D. Yves; Ullsperger, Markus

2008-01-01

Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve ≈30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations. PMID:18427123

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

PubMed

Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

2014-02-01

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors.

PubMed

Thipphavong, David P

2016-09-01

The inaccuracies of the aircraft performance models utilized by trajectory predictors with regard to takeoff weight, thrust, climb profile, and other parameters result in altitude errors during the climb phase that often exceed the vertical separation standard of 1000 feet. This study investigates the potential reduction in altitude trajectory prediction errors that could be achieved for climbing flights if just one additional parameter is made available: top-of-climb (TOC) time. The TOC-matching method developed and evaluated in this paper is straightforward: a set of candidate trajectory predictions is generated using different aircraft weight parameters, and the one that most closely matches TOC in terms of time is selected. This algorithm was tested using more than 1000 climbing flights in Fort Worth Center. Compared to the baseline trajectory predictions of a real-time research prototype (Center/TRACON Automation System), the TOC-matching method reduced the altitude root mean square error (RMSE) for a 5-minute prediction time by 38%. It also decreased the percentage of flights with absolute altitude error greater than the vertical separation standard of 1000 ft for the same look-ahead time from 55% to 30%.

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors

PubMed Central

Thipphavong, David P.

2017-01-01

The inaccuracies of the aircraft performance models utilized by trajectory predictors with regard to takeoff weight, thrust, climb profile, and other parameters result in altitude errors during the climb phase that often exceed the vertical separation standard of 1000 feet. This study investigates the potential reduction in altitude trajectory prediction errors that could be achieved for climbing flights if just one additional parameter is made available: top-of-climb (TOC) time. The TOC-matching method developed and evaluated in this paper is straightforward: a set of candidate trajectory predictions is generated using different aircraft weight parameters, and the one that most closely matches TOC in terms of time is selected. This algorithm was tested using more than 1000 climbing flights in Fort Worth Center. Compared to the baseline trajectory predictions of a real-time research prototype (Center/TRACON Automation System), the TOC-matching method reduced the altitude root mean square error (RMSE) for a 5-minute prediction time by 38%. It also decreased the percentage of flights with absolute altitude error greater than the vertical separation standard of 1000 ft for the same look-ahead time from 55% to 30%. PMID:28684883

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors

NASA Technical Reports Server (NTRS)

Thipphavong, David P.

2016-01-01

The inaccuracies of the aircraft performance models utilized by trajectory predictors with regard to takeoff weight, thrust, climb profile, and other parameters result in altitude errors during the climb phase that often exceed the vertical separation standard of 1000 feet. This study investigates the potential reduction in altitude trajectory prediction errors that could be achieved for climbing flights if just one additional parameter is made available: top-of-climb (TOC) time. The TOC-matching method developed and evaluated in this paper is straightforward: a set of candidate trajectory predictions is generated using different aircraft weight parameters, and the one that most closely matches TOC in terms of time is selected. This algorithm was tested using more than 1000 climbing flights in Fort Worth Center. Compared to the baseline trajectory predictions of a real-time research prototype (Center/TRACON Automation System), the TOC-matching method reduced the altitude root mean square error (RMSE) for a 5-minute prediction time by 38%. It also decreased the percentage of flights with absolute altitude error greater than the vertical separation standard of 1000 ft for the same look-ahead time from 55% to 30%.

Error-rate prediction for programmable circuits: methodology, tools and studied cases

NASA Astrophysics Data System (ADS)

Velazco, Raoul

2013-05-01

This work presents an approach to predict the error rates due to Single Event Upsets (SEU) occurring in programmable circuits as a consequence of the impact or energetic particles present in the environment the circuits operate. For a chosen application, the error-rate is predicted by combining the results obtained from radiation ground testing and the results of fault injection campaigns performed off-beam during which huge numbers of SEUs are injected during the execution of the studied application. The goal of this strategy is to obtain accurate results about different applications' error rates, without using particle accelerator facilities, thus significantly reducing the cost of the sensitivity evaluation. As a case study, this methodology was applied a complex processor, the Power PC 7448 executing a program issued from a real space application and a crypto-processor application implemented in an SRAM-based FPGA and accepted to be embedded in the payload of a scientific satellite of NASA. The accuracy of predicted error rates was confirmed by comparing, for the same circuit and application, predictions with measures issued from radiation ground testing performed at the cyclotron Cyclone cyclotron of HIF (Heavy Ion Facility) of Louvain-la-Neuve (Belgium).

Plasmodium vivax Malaria Endemicity in Indonesia in 2010

PubMed Central

Elyazar, Iqbal R. F.; Gething, Peter W.; Patil, Anand P.; Rogayah, Hanifah; Sariwati, Elvieda; Palupi, Niken W.; Tarmizi, Siti N.; Kusriastuti, Rita; Baird, J. Kevin; Hay, Simon I.

2012-01-01

Background Plasmodium vivax imposes substantial morbidity and mortality burdens in endemic zones. Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. We used model based geostatistics (MBG) techniques to generate a contemporary map of risk of Plasmodium vivax malaria in Indonesia in 2010. Methods Plasmodium vivax Annual Parasite Incidence data (2006–2008) and temperature masks were used to map P. vivax transmission limits. A total of 4,658 community surveys of P. vivax parasite rate (PvPR) were identified (1985–2010) for mapping quantitative estimates of contemporary endemicity within those limits. After error-checking a total of 4,457 points were included into a national database of age-standardized 1–99 year old PvPR data. A Bayesian MBG procedure created a predicted PvPR1–99 endemicity surface with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population surface. Results We estimated 129.6 million people in Indonesia lived at risk of P. vivax transmission in 2010. Among these, 79.3% inhabited unstable transmission areas and 20.7% resided in stable transmission areas. In western Indonesia, the predicted P. vivax prevalence was uniformly low. Over 70% of the population at risk in this region lived on Java and Bali islands, where little malaria transmission occurs. High predicted prevalence areas were observed in the Lesser Sundas, Maluku and Papua. In general, prediction uncertainty was relatively low in the west and high in the east. Conclusion Most Indonesians living with endemic P. vivax experience relatively low risk of infection. However, blood surveys for this parasite are likely relatively insensitive and certainly do not detect the dormant liver stage reservoir of infection. The prospects for P. vivax elimination would be improved with deeper understanding of glucose-6-phosphate dehydrogenase deficiency (G6PDd) distribution, anti-relapse therapy practices and manageability of P. vivax importation risk, especially in Java and Bali. PMID:22615978

Zero end-digit preference in recorded blood pressure and its impact on classification of patients for pharmacologic management in primary care — PREDICT-CVD–6

PubMed Central

Broad, Joanna; Wells, Sue; Marshall, Roger; Jackson, Rod

2007-01-01

Background Most blood pressure recordings end with a zero end-digit despite guidelines recommending measurement to the nearest 2 mmHg. The impact of rounding on management of cardiovascular disease (CVD) risk is unknown. Aim To document the use of rounding to zero end-digit and assess its potential impact on eligibility for pharmacologic management of CVD risk. Design of study Cross-sectional study. Setting A total of 23 676 patients having opportunistic CVD risk assessment in primary care practices in New Zealand. Method To simulate rounding in practice, for patients with systolic blood pressures recorded without a zero end-digit, a second blood pressure measure was generated by arithmetically rounding to the nearest zero end-digit. A 10-year Framingham CVD risk score was estimated using actual and rounded blood pressures. Eligibility for pharmacologic treatment was then determined using the Joint British Societies' JBS2 and the British Hypertension Society BHS–IV guidelines based on actual and rounded blood pressure values. Results Zero end-digits were recorded in 64% of systolic and 62% of diastolic blood pressures. When eligibility for drug treatment was based only on a Framingham 10-year CVD risk threshold of 20% or more, rounding misclassified one in 41 of all those patients subject to this error. Under the two guidelines which use different combinations of CVD risk and blood pressure thresholds, one in 19 would be misclassified under JBS2 and one in 12 under the BHS–IV guidelines mostly towards increased treatment. Conclusion Zero end-digit preference significantly increases a patient's likelihood of being classified as eligible for drug treatment. Guidelines that base treatment decisions primarily on absolute CVD risk are less susceptible to these errors. PMID:17976291

Zero end-digit preference in recorded blood pressure and its impact on classification of patients for pharmacologic management in primary care - PREDICT-CVD-6.

PubMed

Broad, Joanna; Wells, Sue; Marshall, Roger; Jackson, Rod

2007-11-01

Most blood pressure recordings end with a zero end-digit despite guidelines recommending measurement to the nearest 2 mmHg. The impact of rounding on management of cardiovascular disease (CVD) risk is unknown. To document the use of rounding to zero end-digit and assess its potential impact on eligibility for pharmacologic management of CVD risk. Cross-sectional study. A total of 23,676 patients having opportunistic CVD risk assessment in primary care practices in New Zealand. To simulate rounding in practice, for patients with systolic blood pressures recorded without a zero end-digit, a second blood pressure measure was generated by arithmetically rounding to the nearest zero end-digit. A 10-year Framingham CVD risk score was estimated using actual and rounded blood pressures. Eligibility for pharmacologic treatment was then determined using the Joint British Societies' JBS2 and the British Hypertension Society BHS-IV guidelines based on actual and rounded blood pressure values. Zero end-digits were recorded in 64% of systolic and 62% of diastolic blood pressures. When eligibility for drug treatment was based only on a Framingham 10year CVD risk threshold of 20% or more, rounding misclassified one in 41 of all those patients subject to this error. Under the two guidelines which use different combinations of CVD risk and blood pressure thresholds, one in 19 would be misclassified under JBS2 and one in 12 under the BHS-IV guidelines mostly towards increased treatment. Zero end-digit preference significantly increases a patient's likelihood of being classified as eligible for drug treatment. Guidelines that base treatment decisions primarily on absolute CVD risk are less susceptible to these errors.

Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System spacecraft system.Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis E.

2013-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This proposal describes an approach to validate the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft. The research described here is absolutely cutting edge. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional"validation by test only'' mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computationaf Fluid Dynamics can be used to veritY these requirements; however, the model must be validated by test data. The proposed research project includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT and OPEN FOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid . . . Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. To date, the author is the only person to look at the uncertainty in the entire computational domain. For the flow regime being analyzed (turbulent, threedimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Predictors of Errors of Novice Java Programmers

ERIC Educational Resources Information Center

Bringula, Rex P.; Manabat, Geecee Maybelline A.; Tolentino, Miguel Angelo A.; Torres, Edmon L.

2012-01-01

This descriptive study determined which of the sources of errors would predict the errors committed by novice Java programmers. Descriptive statistics revealed that the respondents perceived that they committed the identified eighteen errors infrequently. Thought error was perceived to be the main source of error during the laboratory programming…

Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression

NASA Astrophysics Data System (ADS)

Bukhari, W.; Hong, S.-M.

2015-01-01

Motion-adaptive radiotherapy aims to deliver a conformal dose to the target tumour with minimal normal tissue exposure by compensating for tumour motion in real time. The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting and gating respiratory motion that utilizes a model-based and a model-free Bayesian framework by combining them in a cascade structure. The algorithm, named EKF-GPR+, implements a gating function without pre-specifying a particular region of the patient’s breathing cycle. The algorithm first employs an extended Kalman filter (LCM-EKF) to predict the respiratory motion and then uses a model-free Gaussian process regression (GPR) to correct the error of the LCM-EKF prediction. The GPR is a non-parametric Bayesian algorithm that yields predictive variance under Gaussian assumptions. The EKF-GPR+ algorithm utilizes the predictive variance from the GPR component to capture the uncertainty in the LCM-EKF prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification allows us to pause the treatment beam over such instances. EKF-GPR+ implements the gating function by using simple calculations based on the predictive variance with no additional detection mechanism. A sparse approximation of the GPR algorithm is employed to realize EKF-GPR+ in real time. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPR+. The experimental results show that the EKF-GPR+ algorithm effectively reduces the prediction error in a root-mean-square (RMS) sense by employing the gating function, albeit at the cost of a reduced duty cycle. As an example, EKF-GPR+ reduces the patient-wise RMS error to 37%, 39% and 42% in percent ratios relative to no prediction for a duty cycle of 80% at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The experiments also confirm that EKF-GPR+ controls the duty cycle with reasonable accuracy.

Computational modeling of bedform evolution in rivers with implications for predictions of flood stage and bed evolution

USGS Publications Warehouse

Nelson, Jonathan M.; Shimizu, Yasuyuki; Giri, Sanjay; McDonald, Richard R.

2010-01-01

Uncertainties in flood stage prediction and bed evolution in rivers are frequently associated with the evolution of bedforms over a hydrograph. For the case of flood prediction, the evolution of the bedforms may alter the effective bed roughness, so predictions of stage and velocity based on assuming bedforms retain the same size and shape over a hydrograph will be incorrect. These same effects will produce errors in the prediction of the sediment transport and bed evolution, but in this latter case the errors are typically larger, as even small errors in the prediction of bedform form drag can make very large errors in predicting the rates of sediment motion and the associated erosion and deposition. In situations where flows change slowly, it may be possible to use empirical results that relate bedform morphology to roughness and effective form drag to avoid these errors; but in many cases where the bedforms evolve rapidly and are in disequilibrium with the instantaneous flow, these empirical methods cannot be accurately applied. Over the past few years, computational models for bedform development, migration, and adjustment to varying flows have been developed and tested with a variety of laboratory and field data. These models, which are based on detailed multidimensional flow modeling incorporating large eddy simulation, appear to be capable of predicting bedform dimensions during steady flows as well as their time dependence during discharge variations. In the work presented here, models of this type are used to investigate the impacts of bedform on stage and bed evolution in rivers during flood hydrographs. The method is shown to reproduce hysteresis in rating curves as well as other more subtle effects in the shape of flood waves. Techniques for combining the bedform evolution models with larger-scale models for river reach flow, sediment transport, and bed evolution are described and used to show the importance of including dynamic bedform effects in river modeling. For example calculations for a flood on the Kootenai River, errors of almost 1m in predicted stage and errors of about a factor of two in the predicted maximum depths of erosion can be attributed to bedform evolution. Thus, treating bedforms explicitly in flood and bed evolution models can decrease uncertainty and increase the accuracy of predictions.

Ecological risk assessment on heavy metals in soils: Use of soil diffuse reflectance mid-infrared Fourier-transform spectroscopy

PubMed Central

Wang, Cheng; Li, Wei; Guo, Mingxing; Ji, Junfeng

2017-01-01

The bioavailability of heavy metals in soil is controlled by their concentrations and soil properties. Diffuse reflectance mid-infrared Fourier-transform spectroscopy (DRIFTS) is capable of detecting specific organic and inorganic bonds in metal complexes and minerals and therefore, has been employed to predict soil composition and heavy metal contents. The present study explored the potential of DRIFTS for estimating soil heavy metal bioavailability. Soil and corresponding wheat grain samples from the Yangtze River Delta region were analyzed by DRIFTS and chemical methods. Statistical regression analyses were conducted to correlate the soil spectral information to the concentrations of Cd, Cr, Cu, Zn, Pb, Ni, Hg and Fe in wheat grains. The principal components in the spectra influencing soil heavy metal bioavailability were identified and used in prediction model construction. The established soil DRIFTS-based prediction models were applied to estimate the heavy metal concentrations in wheat grains in the mid-Yangtze River Delta area. The predicted heavy metal concentrations of wheat grain were highly consistent with the measured levels by chemical analysis, showing a significant correlation (r2 > 0.72) with acceptable root mean square error RMSE. In conclusion, DRIFTS is a promising technique for assessing the bioavailability of soil heavy metals and related ecological risk. PMID:28198802

Disrupted prediction errors index social deficits in autism spectrum disorder

PubMed Central

Balsters, Joshua H; Apps, Matthew A J; Bolis, Dimitris; Lehner, Rea; Gallagher, Louise; Wenderoth, Nicole

2017-01-01

Abstract Social deficits are a core symptom of autism spectrum disorder; however, the perturbed neural mechanisms underpinning these deficits remain unclear. It has been suggested that social prediction errors—coding discrepancies between the predicted and actual outcome of another’s decisions—might play a crucial role in processing social information. While the gyral surface of the anterior cingulate cortex signalled social prediction errors in typically developing individuals, this crucial social signal was altered in individuals with autism spectrum disorder. Importantly, the degree to which social prediction error signalling was aberrant correlated with diagnostic measures of social deficits. Effective connectivity analyses further revealed that, in typically developing individuals but not in autism spectrum disorder, the magnitude of social prediction errors was driven by input from the ventromedial prefrontal cortex. These data provide a novel insight into the neural substrates underlying autism spectrum disorder social symptom severity, and further research into the gyral surface of the anterior cingulate cortex and ventromedial prefrontal cortex could provide more targeted therapies to help ameliorate social deficits in autism spectrum disorder. PMID:28031223

Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans

PubMed Central

Thomas, Julie; Vanni-Mercier, Giovanna; Dreher, Jean-Claude

2013-01-01

Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies. PMID:24302894

High capacity reversible watermarking for audio by histogram shifting and predicted error expansion.

PubMed

Wang, Fei; Xie, Zhaoxin; Chen, Zuo

2014-01-01

Being reversible, the watermarking information embedded in audio signals can be extracted while the original audio data can achieve lossless recovery. Currently, the few reversible audio watermarking algorithms are confronted with following problems: relatively low SNR (signal-to-noise) of embedded audio; a large amount of auxiliary embedded location information; and the absence of accurate capacity control capability. In this paper, we present a novel reversible audio watermarking scheme based on improved prediction error expansion and histogram shifting. First, we use differential evolution algorithm to optimize prediction coefficients and then apply prediction error expansion to output stego data. Second, in order to reduce location map bits length, we introduced histogram shifting scheme. Meanwhile, the prediction error modification threshold according to a given embedding capacity can be computed by our proposed scheme. Experiments show that this algorithm improves the SNR of embedded audio signals and embedding capacity, drastically reduces location map bits length, and enhances capacity control capability.

The role of predicted solar activity in TOPEX/Poseidon orbit maintenance maneuver design

NASA Technical Reports Server (NTRS)

Frauenholz, Raymond B.; Shapiro, Bruce E.

1992-01-01

Following launch in June 1992, the TOPEX/Poseidon satellite will be placed in a near-circular frozen orbit at an altitude of about 1336 km. Orbit maintenance maneuvers are planned to assure all nodes of the 127-orbit 10-day repeat ground track remain within a 2 km equatorial longitude bandwidth. Orbit determination, maneuver execution, and atmospheric drag prediction errors limit overall targeting performance. This paper focuses on the effects of drag modeling errors, with primary emphasis on the role of SESC solar activity predictions, especially the 27-day outlook of the 10.7 cm solar flux and geomagnetic index used by a simplified version of the Jacchia-Roberts density model developed for this TOPEX/Poseidon application. For data evaluated from 1983-90, the SESC outlook performed better than a simpler persistence strategy, especially during the first 7-10 days. A targeting example illustrates the use of ground track biasing to compensate for expected orbit predictions errors, emphasizing the role of solar activity prediction errors.

Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.

PubMed

Fletcher, Timothy L; Popelier, Paul L A

2016-06-14

A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry only. These models then predict molecular electrostatic interaction energies. On the basis of 200 unseen test geometries for each amino acid, no amino acid shows a mean prediction error above 5.3 kJ mol(-1), while the lowest error observed is 2.8 kJ mol(-1). The mean error across the entire set is only 4.2 kJ mol(-1) (or 1 kcal mol(-1)). Charged systems are created by protonating or deprotonating selected amino acids, and these show no significant deviation in prediction error over their neutral counterparts. Similarly, the proposed methodology can also handle amino acids with aromatic side chains, without the need for modification. Thus, we present a generic method capable of accurately capturing multipolar polarizable electrostatics in amino acids.

Prediction of stream volatilization coefficients

USGS Publications Warehouse

Rathbun, Ronald E.

1990-01-01

Equations are developed for predicting the liquid-film and gas-film reference-substance parameters for quantifying volatilization of organic solutes from streams. Molecular weight and molecular-diffusion coefficients of the solute are used as correlating parameters. Equations for predicting molecular-diffusion coefficients of organic solutes in water and air are developed, with molecular weight and molal volume as parameters. Mean absolute errors of prediction for diffusion coefficients in water are 9.97% for the molecular-weight equation, 6.45% for the molal-volume equation. The mean absolute error for the diffusion coefficient in air is 5.79% for the molal-volume equation. Molecular weight is not a satisfactory correlating parameter for diffusion in air because two equations are necessary to describe the values in the data set. The best predictive equation for the liquid-film reference-substance parameter has a mean absolute error of 5.74%, with molal volume as the correlating parameter. The best equation for the gas-film parameter has a mean absolute error of 7.80%, with molecular weight as the correlating parameter.

Medical professional liability insurance and its relation to medical error and healthcare risk management for the practicing physician.

PubMed

Abbott, Richard L; Weber, Paul; Kelley, Betsy

2005-12-01

To review the history and current issues surrounding medical professional liability insurance and its relationship to medical error and healthcare risk management. Focused literature review and authors' experience. Medical professional liability insurance issues are reviewed in association with the occurrence of medical error and the role of healthcare risk management. The rising frequency and severity of claims and lawsuits incurred by physicians, as well as escalating defense costs, have dramatically increased over the past several years and have resulted in accelerated efforts to reduce medical errors and control practice risk for physicians. Medical error reduction and improved patient outcomes are closely linked to the goals of the medical risk manager by reducing exposure to adverse medical events. Management of professional liability risk by the physician-led malpractice insurance company not only protects the economic viability of physicians, but also addresses patient safety concerns. Physician-owned malpractice liability insurance companies will continue to be the dominant providers of insurance for practicing physicians and will serve as the primary source for loss prevention and risk management services. To succeed in the marketplace, the emergence and importance of the risk manager and incorporation of risk management principles throughout the professional liability company has become crucial to the financial stability and success of the insurance company. The risk manager provides the necessary advice and support requested by physicians to minimize medical liability risk in their daily practice.

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

NASA Astrophysics Data System (ADS)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

Choose and choose again: appearance-reality errors, pragmatics and logical ability.

PubMed

Deák, Gedeon O; Enright, Brian

2006-05-01

In the Appearance/Reality (AR) task some 3- and 4-year-old children make perseverative errors: they choose the same word for the appearance and the function of a deceptive object. Are these errors specific to the AR task, or signs of a general question-answering problem? Preschoolers completed five tasks: AR; simple successive forced-choice question pairs (QP); flexible naming of objects (FN); working memory (WM) span; and indeterminacy detection (ID). AR errors correlated with QP errors. Insensitivity to indeterminacy predicted perseveration in both tasks. Neither WM span nor flexible naming predicted other measures. Age predicted sensitivity to indeterminacy. These findings suggest that AR tests measure a pragmatic understanding; specifically, different questions about a topic usually call for different answers. This understanding is related to the ability to detect indeterminacy of each question in a series. AR errors are unrelated to the ability to represent an object as belonging to multiple categories, to working memory span, or to inhibiting previously activated words.

Cognitive strategies regulate fictive, but not reward prediction error signals in a sequential investment task.

PubMed

Gu, Xiaosi; Kirk, Ulrich; Lohrenz, Terry M; Montague, P Read

2014-08-01

Computational models of reward processing suggest that foregone or fictive outcomes serve as important information sources for learning and augment those generated by experienced rewards (e.g. reward prediction errors). An outstanding question is how these learning signals interact with top-down cognitive influences, such as cognitive reappraisal strategies. Using a sequential investment task and functional magnetic resonance imaging, we show that the reappraisal strategy selectively attenuates the influence of fictive, but not reward prediction error signals on investment behavior; such behavioral effect is accompanied by changes in neural activity and connectivity in the anterior insular cortex, a brain region thought to integrate subjective feelings with high-order cognition. Furthermore, individuals differ in the extent to which their behaviors are driven by fictive errors versus reward prediction errors, and the reappraisal strategy interacts with such individual differences; a finding also accompanied by distinct underlying neural mechanisms. These findings suggest that the variable interaction of cognitive strategies with two important classes of computational learning signals (fictive, reward prediction error) represent one contributing substrate for the variable capacity of individuals to control their behavior based on foregone rewards. These findings also expose important possibilities for understanding the lack of control in addiction based on possibly foregone rewarding outcomes. Copyright © 2013 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

A Bayesian approach to model structural error and input variability in groundwater modeling

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Lin, Y. F. F.; Liang, F.

2015-12-01

Effective water resource management typically relies on numerical models to analyze groundwater flow and solute transport processes. Model structural error (due to simplification and/or misrepresentation of the "true" environmental system) and input forcing variability (which commonly arises since some inputs are uncontrolled or estimated with high uncertainty) are ubiquitous in groundwater models. Calibration that overlooks errors in model structure and input data can lead to biased parameter estimates and compromised predictions. We present a fully Bayesian approach for a complete assessment of uncertainty for spatially distributed groundwater models. The approach explicitly recognizes stochastic input and uses data-driven error models based on nonparametric kernel methods to account for model structural error. We employ exploratory data analysis to assist in specifying informative prior for error models to improve identifiability. The inference is facilitated by an efficient sampling algorithm based on DREAM-ZS and a parameter subspace multiple-try strategy to reduce the required number of forward simulations of the groundwater model. We demonstrate the Bayesian approach through a synthetic case study of surface-ground water interaction under changing pumping conditions. It is found that explicit treatment of errors in model structure and input data (groundwater pumping rate) has substantial impact on the posterior distribution of groundwater model parameters. Using error models reduces predictive bias caused by parameter compensation. In addition, input variability increases parametric and predictive uncertainty. The Bayesian approach allows for a comparison among the contributions from various error sources, which could inform future model improvement and data collection efforts on how to best direct resources towards reducing predictive uncertainty.

Data driven CAN node reliability assessment for manufacturing system

NASA Astrophysics Data System (ADS)

Zhang, Leiming; Yuan, Yong; Lei, Yong

2017-01-01

The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

Lateral habenula neurons signal errors in the prediction of reward information

PubMed Central

Bromberg-Martin, Ethan S.; Hikosaka, Okihide

2011-01-01

Humans and animals have a remarkable ability to predict future events, which they achieve by persistently searching their environment for sources of predictive information. Yet little is known about the neural systems that motivate this behavior. We hypothesized that information-seeking is assigned value by the same circuits that support reward-seeking, so that neural signals encoding conventional “reward prediction errors” include analogous “information prediction errors”. To test this we recorded from neurons in the lateral habenula, a nucleus which encodes reward prediction errors, while monkeys chose between cues that provided different amounts of information about upcoming rewards. We found that a subpopulation of lateral habenula neurons transmitted signals resembling information prediction errors, responding when reward information was unexpectedly cued, delivered, or denied. Their signals evaluated information sources reliably even when the animal’s decisions did not. These neurons could provide a common instructive signal for reward-seeking and information-seeking behavior. PMID:21857659

Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest

NASA Astrophysics Data System (ADS)

Lehner, Flavio; Wood, Andrew W.; Llewellyn, Dagmar; Blatchford, Douglas B.; Goodbody, Angus G.; Pappenberger, Florian

2017-12-01

Seasonal streamflow predictions provide a critical management tool for water managers in the American Southwest. In recent decades, persistent prediction errors for spring and summer runoff volumes have been observed in a number of watersheds in the American Southwest. While mostly driven by decadal precipitation trends, these errors also relate to the influence of increasing temperature on streamflow in these basins. Here we show that incorporating seasonal temperature forecasts from operational global climate prediction models into streamflow forecasting models adds prediction skill for watersheds in the headwaters of the Colorado and Rio Grande River basins. Current dynamical seasonal temperature forecasts now show sufficient skill to reduce streamflow forecast errors in snowmelt-driven regions. Such predictions can increase the resilience of streamflow forecasting and water management systems in the face of continuing warming as well as decadal-scale temperature variability and thus help to mitigate the impacts of climate nonstationarity on streamflow predictability.

Stochastic estimation of plant-available soil water under fluctuating water table depths

NASA Astrophysics Data System (ADS)

Or, Dani; Groeneveld, David P.

1994-12-01

Preservation of native valley-floor phreatophytes while pumping groundwater for export from Owens Valley, California, requires reliable predictions of plant water use. These predictions are compared with stored soil water within well field regions and serve as a basis for managing groundwater resources. Soil water measurement errors, variable recharge, unpredictable climatic conditions affecting plant water use, and modeling errors make soil water predictions uncertain and error-prone. We developed and tested a scheme based on soil water balance coupled with implementation of Kalman filtering (KF) for (1) providing physically based soil water storage predictions with prediction errors projected from the statistics of the various inputs, and (2) reducing the overall uncertainty in both estimates and predictions. The proposed KF-based scheme was tested using experimental data collected at a location on the Owens Valley floor where the water table was artificially lowered by groundwater pumping and later allowed to recover. Vegetation composition and per cent cover, climatic data, and soil water information were collected and used for developing a soil water balance. Predictions and updates of soil water storage under different types of vegetation were obtained for a period of 5 years. The main results show that: (1) the proposed predictive model provides reliable and resilient soil water estimates under a wide range of external conditions; (2) the predicted soil water storage and the error bounds provided by the model offer a realistic and rational basis for decisions such as when to curtail well field operation to ensure plant survival. The predictive model offers a practical means for accommodating simple aspects of spatial variability by considering the additional source of uncertainty as part of modeling or measurement uncertainty.

Comparing Parameter Estimation Techniques for an Electrical Power Transformer Oil Temperature Prediction Model

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1999-01-01

This paper examines various sources of error in MIT's improved top oil temperature rise over ambient temperature model and estimation process. The sources of error are the current parameter estimation technique, quantization noise, and post-processing of the transformer data. Results from this paper will show that an output error parameter estimation technique should be selected to replace the current least squares estimation technique. The output error technique obtained accurate predictions of transformer behavior, revealed the best error covariance, obtained consistent parameter estimates, and provided for valid and sensible parameters. This paper will also show that the output error technique should be used to minimize errors attributed to post-processing (decimation) of the transformer data. Models used in this paper are validated using data from a large transformer in service.

Non-integer expansion embedding techniques for reversible image watermarking

NASA Astrophysics Data System (ADS)

Xiang, Shijun; Wang, Yi

2015-12-01

This work aims at reducing the embedding distortion of prediction-error expansion (PE)-based reversible watermarking. In the classical PE embedding method proposed by Thodi and Rodriguez, the predicted value is rounded to integer number for integer prediction-error expansion (IPE) embedding. The rounding operation makes a constraint on a predictor's performance. In this paper, we propose a non-integer PE (NIPE) embedding approach, which can proceed non-integer prediction errors for embedding data into an audio or image file by only expanding integer element of a prediction error while keeping its fractional element unchanged. The advantage of the NIPE embedding technique is that the NIPE technique can really bring a predictor into full play by estimating a sample/pixel in a noncausal way in a single pass since there is no rounding operation. A new noncausal image prediction method to estimate a pixel with four immediate pixels in a single pass is included in the proposed scheme. The proposed noncausal image predictor can provide better performance than Sachnev et al.'s noncausal double-set prediction method (where data prediction in two passes brings a distortion problem due to the fact that half of the pixels were predicted with the watermarked pixels). In comparison with existing several state-of-the-art works, experimental results have shown that the NIPE technique with the new noncausal prediction strategy can reduce the embedding distortion for the same embedding payload.

Evaluation and Enhancement of Calibration in the American College of Surgeons NSQIP Surgical Risk Calculator.

PubMed

Liu, Yaoming; Cohen, Mark E; Hall, Bruce L; Ko, Clifford Y; Bilimoria, Karl Y

2016-08-01

The American College of Surgeon (ACS) NSQIP Surgical Risk Calculator has been widely adopted as a decision aid and informed consent tool by surgeons and patients. Previous evaluations showed excellent discrimination and combined discrimination and calibration, but model calibration alone, and potential benefits of recalibration, were not explored. Because lack of calibration can lead to systematic errors in assessing surgical risk, our objective was to assess calibration and determine whether spline-based adjustments could improve it. We evaluated Surgical Risk Calculator model calibration, as well as discrimination, for each of 11 outcomes modeled from nearly 3 million patients (2010 to 2014). Using independent random subsets of data, we evaluated model performance for the Development (60% of records), Validation (20%), and Test (20%) datasets, where prediction equations from the Development dataset were recalibrated using restricted cubic splines estimated from the Validation dataset. We also evaluated performance on data subsets composed of higher-risk operations. The nonrecalibrated Surgical Risk Calculator performed well, but there was a slight tendency for predicted risk to be overestimated for lowest- and highest-risk patients and underestimated for moderate-risk patients. After recalibration, this distortion was eliminated, and p values for miscalibration were most often nonsignificant. Calibration was also excellent for subsets of higher-risk operations, though observed calibration was reduced due to instability associated with smaller sample sizes. Performance of NSQIP Surgical Risk Calculator models was shown to be excellent and improved with recalibration. Surgeons and patients can rely on the calculator to provide accurate estimates of surgical risk. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Comparison of Methodologies Using Estimated or Measured Values of Total Corneal Astigmatism for Toric Intraocular Lens Power Calculation.

PubMed

Ferreira, Tiago B; Ribeiro, Paulo; Ribeiro, Filomena J; O'Neill, João G

2017-12-01

To compare the prediction error in the calculation of toric intraocular lenses (IOLs) associated with methods that estimate the power of the posterior corneal surface (ie, Barrett toric calculator and Abulafia-Koch formula) with that of methods that consider real measures obtained using Scheimpflug imaging: a software that uses vectorial calculation (Panacea toric calculator: http://www.panaceaiolandtoriccalculator.com) and a ray tracing software (PhacoOptics, Aarhus Nord, Denmark). In 107 eyes of 107 patients undergoing cataract surgery with toric IOL implantation (Acrysof IQ Toric; Alcon Laboratories, Inc., Fort Worth, TX), predicted residual astigmatism by each calculation method was compared with manifest refractive astigmatism. Prediction error in residual astigmatism was calculated using vector analysis. All calculation methods resulted in overcorrection of with-the-rule astigmatism and undercorrection of against-the-rule astigmatism. Both estimation methods resulted in lower mean and centroid astigmatic prediction errors, and a larger number of eyes within 0.50 diopters (D) of absolute prediction error than methods considering real measures (P < .001). Centroid prediction error (CPE) was 0.07 D at 172° for the Barrett toric calculator and 0.13 D at 174° for the Abulafia-Koch formula (combined with Holladay calculator). For methods using real posterior corneal surface measurements, CPE was 0.25 D at 173° for the Panacea calculator and 0.29 D at 171° for the ray tracing software. The Barrett toric calculator and Abulafia-Koch formula yielded the lowest astigmatic prediction errors. Directly evaluating total corneal power for toric IOL calculation was not superior to estimating it. [J Refract Surg. 2017;33(12):794-800.]. Copyright 2017, SLACK Incorporated.

Toward isolating the role of dopamine in the acquisition of incentive salience attribution.

PubMed

Chow, Jonathan J; Nickell, Justin R; Darna, Mahesh; Beckmann, Joshua S

2016-10-01

Stimulus-reward learning has been heavily linked to the reward-prediction error learning hypothesis and dopaminergic function. However, some evidence suggests dopaminergic function may not strictly underlie reward-prediction error learning, but may be specific to incentive salience attribution. Utilizing a Pavlovian conditioned approach procedure consisting of two stimuli that were equally reward-predictive (both undergoing reward-prediction error learning) but functionally distinct in regard to incentive salience (levers that elicited sign-tracking and tones that elicited goal-tracking), we tested the differential role of D1 and D2 dopamine receptors and nucleus accumbens dopamine in the acquisition of sign- and goal-tracking behavior and their associated conditioned reinforcing value within individuals. Overall, the results revealed that both D1 and D2 inhibition disrupted performance of sign- and goal-tracking. However, D1 inhibition specifically prevented the acquisition of sign-tracking to a lever, instead promoting goal-tracking and decreasing its conditioned reinforcing value, while neither D1 nor D2 signaling was required for goal-tracking in response to a tone. Likewise, nucleus accumbens dopaminergic lesions disrupted acquisition of sign-tracking to a lever, while leaving goal-tracking in response to a tone unaffected. Collectively, these results are the first evidence of an intraindividual dissociation of dopaminergic function in incentive salience attribution from reward-prediction error learning, indicating that incentive salience, reward-prediction error, and their associated dopaminergic signaling exist within individuals and are stimulus-specific. Thus, individual differences in incentive salience attribution may be reflective of a differential balance in dopaminergic function that may bias toward the attribution of incentive salience, relative to reward-prediction error learning only. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effect of trabeculectomy on the accuracy of intraocular lens calculations in patients with open-angle glaucoma.

PubMed

Bae, Hyoung Won; Lee, Yun Ha; Kim, Do Wook; Lee, Taekjune; Hong, Samin; Seong, Gong Je; Kim, Chan Yun

2016-08-01

The objective of the study is to examine the effect of trabeculectomy on intraocular lens power calculations in patients with open-angle glaucoma (OAG) undergoing cataract surgery. The design is retrospective data analysis. There are a total of 55 eyes of 55 patients with OAG who had a cataract surgery alone or in combination with trabeculectomy. We classified OAG subjects into the following groups based on surgical history: only cataract surgery (OC group), cataract surgery after prior trabeculectomy (CAT group), and cataract surgery performed in combination with trabeculectomy (CCT group). Differences between actual and predicted postoperative refractive error. Mean error (ME, difference between postoperative and predicted SE) in the CCT group was significantly lower (towards myopia) than that of the OC group (P = 0.008). Additionally, mean absolute error (MAE, absolute value of ME) in the CAT group was significantly greater than in the OC group (P = 0.006). Using linear mixed models, the ME calculated with the SRK II formula was more accurate than the ME predicted by the SRK T formula in the CAT (P = 0.032) and CCT (P = 0.035) groups. The intraocular lens power prediction accuracy was lower in the CAT and CCT groups than in the OC group. The prediction error was greater in the CAT group than in the OC group, and the direction of the prediction error tended to be towards myopia in the CCT group. The SRK II formula may be more accurate in predicting residual refractive error in the CAT and CCT groups. © 2016 Royal Australian and New Zealand College of Ophthalmologists.

Short-term prediction of rain attenuation level and volatility in Earth-to-Satellite links at EHF band

NASA Astrophysics Data System (ADS)

de Montera, L.; Mallet, C.; Barthès, L.; Golé, P.

2008-08-01

This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20 50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.

Method and apparatus for faulty memory utilization

DOEpatents

Cher, Chen-Yong; Andrade Costa, Carlos H.; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

2016-04-19

A method for faulty memory utilization in a memory system includes: obtaining information regarding memory health status of at least one memory page in the memory system; determining an error tolerance of the memory page when the information regarding memory health status indicates that a failure is predicted to occur in an area of the memory system affecting the memory page; initiating a migration of data stored in the memory page when it is determined that the data stored in the memory page is non-error-tolerant; notifying at least one application regarding a predicted operating system failure and/or a predicted application failure when it is determined that data stored in the memory page is non-error-tolerant and cannot be migrated; and notifying at least one application regarding the memory failure predicted to occur when it is determined that data stored in the memory page is error-tolerant.

Improved model predictive control of resistive wall modes by error field estimator in EXTRAP T2R

NASA Astrophysics Data System (ADS)

Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

2016-12-01

Many implementations of a model-based approach for toroidal plasma have shown better control performance compared to the conventional type of feedback controller. One prerequisite of model-based control is the availability of a control oriented model. This model can be obtained empirically through a systematic procedure called system identification. Such a model is used in this work to design a model predictive controller to stabilize multiple resistive wall modes in EXTRAP T2R reversed-field pinch. Model predictive control is an advanced control method that can optimize the future behaviour of a system. Furthermore, this paper will discuss an additional use of the empirical model which is to estimate the error field in EXTRAP T2R. Two potential methods are discussed that can estimate the error field. The error field estimator is then combined with the model predictive control and yields better radial magnetic field suppression.

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records.

PubMed

Ouyang, Liwen; Apley, Daniel W; Mehrotra, Sanjay

2016-04-01

Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. The authors' objective is to develop a new design-of-experiments-based systematic chart validation and review (DSCVR) approach that is more powerful than the random validation sampling used in existing approaches. The DSCVR approach judiciously and efficiently selects the cases to validate (i.e., validate whether the response values are correct for those cases) for maximum information content, based only on their predictor variable values. The final predictive model will be fit using only the validation sample, ignoring the remainder of the unvalidated and unreliable error-prone data. A Fisher information based D-optimality criterion is used, and an algorithm for optimizing it is developed. The authors' method is tested in a simulation comparison that is based on a sudden cardiac arrest case study with 23 041 patients' records. This DSCVR approach, using the Fisher information based D-optimality criterion, results in a fitted model with much better predictive performance, as measured by the receiver operating characteristic curve and the accuracy in predicting whether a patient will experience the event, than a model fitted using a random validation sample. The simulation comparisons demonstrate that this DSCVR approach can produce predictive models that are significantly better than those produced from random validation sampling, especially when the event rate is low. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predicting the onset and persistence of episodes of depression in primary health care. The predictD-Spain study: Methodology

PubMed Central

Bellón, Juan Ángel; Moreno-Küstner, Berta; Torres-González, Francisco; Montón-Franco, Carmen; GildeGómez-Barragán, María Josefa; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; de Dios Luna, Juan; Cervilla, Jorge A; Gutierrez, Blanca; Martínez-Cañavate, María Teresa; Oliván-Blázquez, Bárbara; Vázquez-Medrano, Ana; Sánchez-Artiaga, María Soledad; March, Sebastia; Motrico, Emma; Ruiz-García, Victor Manuel; Brangier-Wainberg, Paulette Renée; del Mar Muñoz-García, María; Nazareth, Irwin; King, Michael

2008-01-01

Background The effects of putative risk factors on the onset and/or persistence of depression remain unclear. We aim to develop comprehensive models to predict the onset and persistence of episodes of depression in primary care. Here we explain the general methodology of the predictD-Spain study and evaluate the reliability of the questionnaires used. Methods This is a prospective cohort study. A systematic random sample of general practice attendees aged 18 to 75 has been recruited in seven Spanish provinces. Depression is being measured with the CIDI at baseline, and at 6, 12, 24 and 36 months. A set of individual, environmental, genetic, professional and organizational risk factors are to be assessed at each follow-up point. In a separate reliability study, a proportional random sample of 401 participants completed the test-retest (251 researcher-administered and 150 self-administered) between October 2005 and February 2006. We have also checked 118,398 items for data entry from a random sample of 480 patients stratified by province. Results All items and questionnaires had good test-retest reliability for both methods of administration, except for the use of recreational drugs over the previous six months. Cronbach's alphas were good and their factorial analyses coherent for the three scales evaluated (social support from family and friends, dissatisfaction with paid work, and dissatisfaction with unpaid work). There were 191 (0.16%) data entry errors. Conclusion The items and questionnaires were reliable and data quality control was excellent. When we eventually obtain our risk index for the onset and persistence of depression, we will be able to determine the individual risk of each patient evaluated in primary health care. PMID:18657275

Modelling fatigue and the use of fatigue models in work settings.

PubMed

Dawson, Drew; Ian Noy, Y; Härmä, Mikko; Akerstedt, Torbjorn; Belenky, Gregory

2011-03-01

In recent years, theoretical models of the sleep and circadian system developed in laboratory settings have been adapted to predict fatigue and, by inference, performance. This is typically done using the timing of prior sleep and waking or working hours as the primary input and the time course of the predicted variables as the primary output. The aim of these models is to provide employers, unions and regulators with quantitative information on the likely average level of fatigue, or risk, associated with a given pattern of work and sleep with the goal of better managing the risk of fatigue-related errors and accidents/incidents. The first part of this review summarises the variables known to influence workplace fatigue and draws attention to the considerable variability attributable to individual and task variables not included in current models. The second part reviews the current fatigue models described in the scientific and technical literature and classifies them according to whether they predict fatigue directly by using the timing of prior sleep and wake (one-step models) or indirectly by using work schedules to infer an average sleep-wake pattern that is then used to predict fatigue (two-step models). The third part of the review looks at the current use of fatigue models in field settings by organizations and regulators. Given their limitations it is suggested that the current generation of models may be appropriate for use as one element in a fatigue risk management system. The final section of the review looks at the future of these models and recommends a standardised approach for their use as an element of the 'defenses-in-depth' approach to fatigue risk management. Copyright © 2010 Elsevier Ltd. All rights reserved.

Modeling coherent errors in quantum error correction

NASA Astrophysics Data System (ADS)

Greenbaum, Daniel; Dutton, Zachary

2018-01-01

Analysis of quantum error correcting codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. Here we examine the accuracy of the Pauli approximation for noise containing coherent errors (characterized by a rotation angle ɛ) under the repetition code. We derive an analytic expression for the logical error channel as a function of arbitrary code distance d and concatenation level n, in the small error limit. We find that coherent physical errors result in logical errors that are partially coherent and therefore non-Pauli. However, the coherent part of the logical error is negligible at fewer than {ε }-({dn-1)} error correction cycles when the decoder is optimized for independent Pauli errors, thus providing a regime of validity for the Pauli approximation. Above this number of correction cycles, the persistent coherent logical error will cause logical failure more quickly than the Pauli model would predict, and this may need to be combated with coherent suppression methods at the physical level or larger codes.

Does a better model yield a better argument? An info-gap analysis

NASA Astrophysics Data System (ADS)

Ben-Haim, Yakov

2017-04-01

Theories, models and computations underlie reasoned argumentation in many areas. The possibility of error in these arguments, though of low probability, may be highly significant when the argument is used in predicting the probability of rare high-consequence events. This implies that the choice of a theory, model or computational method for predicting rare high-consequence events must account for the probability of error in these components. However, error may result from lack of knowledge or surprises of various sorts, and predicting the probability of error is highly uncertain. We show that the putatively best, most innovative and sophisticated argument may not actually have the lowest probability of error. Innovative arguments may entail greater uncertainty than more standard but less sophisticated methods, creating an innovation dilemma in formulating the argument. We employ info-gap decision theory to characterize and support the resolution of this problem and present several examples.

Text familiarity, word frequency, and sentential constraints in error detection.

PubMed

Pilotti, Maura; Chodorow, Martin; Schauss, Frances

2009-12-01

The present study examines whether the frequency of an error-bearing word and its predictability, arising from sentential constraints and text familiarity, either independently or jointly, would impair error detection by making proofreading driven by top-down processes. Prior to a proofreading task, participants were asked to read, copy, memorize, or paraphrase sentences, half of which contained errors. These tasks represented a continuum of progressively more demanding and time-consuming activities, which were thought to lead to comparable increases in text familiarity and thus predictability. Proofreading times were unaffected by whether the sentences had been encountered earlier. Proofreading was slower and less accurate for high-frequency words and for highly constrained sentences. Prior memorization produced divergent effects on accuracy depending on sentential constraints. The latter finding suggested that a substantial level of predictability, such as that produced by memorizing highly constrained sentences, can increase the probability of overlooking errors.

A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong

2001-01-01

This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

Resolving Mixed Algal Species in Hyperspectral Images

PubMed Central

Mehrubeoglu, Mehrube; Teng, Ming Y.; Zimba, Paul V.

2014-01-01

We investigated a lab-based hyperspectral imaging system's response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system's performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert's law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements. PMID:24451451

Tailoring a Human Reliability Analysis to Your Industry Needs

NASA Technical Reports Server (NTRS)

DeMott, D. L.

2016-01-01

Companies at risk of accidents caused by human error that result in catastrophic consequences include: airline industry mishaps, medical malpractice, medication mistakes, aerospace failures, major oil spills, transportation mishaps, power production failures and manufacturing facility incidents. Human Reliability Assessment (HRA) is used to analyze the inherent risk of human behavior or actions introducing errors into the operation of a system or process. These assessments can be used to identify where errors are most likely to arise and the potential risks involved if they do occur. Using the basic concepts of HRA, an evolving group of methodologies are used to meet various industry needs. Determining which methodology or combination of techniques will provide a quality human reliability assessment is a key element to developing effective strategies for understanding and dealing with risks caused by human errors. There are a number of concerns and difficulties in "tailoring" a Human Reliability Assessment (HRA) for different industries. Although a variety of HRA methodologies are available to analyze human error events, determining the most appropriate tools to provide the most useful results can depend on industry specific cultures and requirements. Methodology selection may be based on a variety of factors that include: 1) how people act and react in different industries, 2) expectations based on industry standards, 3) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 4) type and availability of data, 5) how the industry views risk & reliability, and 6) types of emergencies, contingencies and routine tasks. Other considerations for methodology selection should be based on what information is needed from the assessment. If the principal concern is determination of the primary risk factors contributing to the potential human error, a more detailed analysis method may be employed versus a requirement to provide a numerical value as part of a probabilistic risk assessment. Industries involved with humans operating large equipment or transport systems (ex. railroads or airlines) would have more need to address the man machine interface than medical workers administering medications. Human error occurs in every industry; in most cases the consequences are relatively benign and occasionally beneficial. In cases where the results can have disastrous consequences, the use of Human Reliability techniques to identify and classify the risk of human errors allows a company more opportunities to mitigate or eliminate these types of risks and prevent costly tragedies.

Evidence for aversive withdrawal response to own errors.

PubMed

Hochman, Eldad Yitzhak; Milman, Valery; Tal, Liron

2017-10-01

Recent model suggests that error detection gives rise to defensive motivation prompting protective behavior. Models of active avoidance behavior predict it should grow larger with threat imminence and avoidance. We hypothesized that in a task requiring left or right key strikes, error detection would drive an avoidance reflex manifested by rapid withdrawal of an erring finger growing larger with threat imminence and avoidance. In experiment 1, three groups differing by error-related threat imminence and avoidance performed a flanker task requiring left or right force sensitive-key strikes. As predicted, errors were followed by rapid force release growing faster with threat imminence and opportunity to evade threat. In experiment 2, we established a link between error key release time (KRT) and the subjective sense of inner-threat. In a simultaneous, multiple regression analysis of three error-related compensatory mechanisms (error KRT, flanker effect, error correction RT), only error KRT was significantly associated with increased compulsive checking tendencies. We propose that error response withdrawal reflects an error-withdrawal reflex. Copyright © 2017 Elsevier B.V. All rights reserved.

A TCP model for external beam treatment of intermediate-risk prostate cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, Sean; Putten, Wil van der

2013-03-15

Purpose: Biological models offer the ability to predict clinical outcomes. The authors describe a model to predict the clinical response of intermediate-risk prostate cancer to external beam radiotherapy for a variety of fractionation regimes. Methods: A fully heterogeneous population averaged tumor control probability model was fit to clinical outcome data for hyper, standard, and hypofractionated treatments. The tumor control probability model was then employed to predict the clinical outcome of extreme hypofractionation regimes, as utilized in stereotactic body radiotherapy. Results: The tumor control probability model achieves an excellent level of fit, R{sup 2} value of 0.93 and a root meanmore » squared error of 1.31%, to the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals Less-Than-Or-Slanted-Equal-To 1.0% are produced by the tumor control probability model when compared to clinical outcome data for stereotactic body radiotherapy. Conclusions: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.« less

Independent data validation of an in vitro method for ...

EPA Pesticide Factsheets

In vitro bioaccessibility assays (IVBA) estimate arsenic (As) relative bioavailability (RBA) in contaminated soils to improve the accuracy of site-specific human exposure assessments and risk calculations. For an IVBA assay to gain acceptance for use in risk assessment, it must be shown to reliably predict in vivo RBA that is determined in an established animal model. Previous studies correlating soil As IVBA with RBA have been limited by the use of few soil types as the source of As. Furthermore, the predictive value of As IVBA assays has not been validated using an independent set of As-contaminated soils. Therefore, the current study was undertaken to develop a robust linear model to predict As RBA in mice using an IVBA assay and to independently validate the predictive capability of this assay using a unique set of As-contaminated soils. Thirty-six As-contaminated soils varying in soil type, As contaminant source, and As concentration were included in this study, with 27 soils used for initial model development and nine soils used for independent model validation. The initial model reliably predicted As RBA values in the independent data set, with a mean As RBA prediction error of 5.3% (range 2.4 to 8.4%). Following validation, all 36 soils were used for final model development, resulting in a linear model with the equation: RBA = 0.59 * IVBA + 9.8 and R2 of 0.78. The in vivo-in vitro correlation and independent data validation presented here provide

Effect of Replacing Race with Apolipoprotein L1 Genotype in Calculation of Kidney Donor Risk Index

PubMed Central

Julian, B. A.; Gaston, R. S.; Brown, W. M.; Reeves-Daniel, A. M.; Israni, A. K.; Schladt, D. P.; Pastan, S. O.; Mohan, S.; Freedman, B. I.; Divers, J.

2016-01-01

Renal allografts from deceased African Americans with two apolipoprotein L1 gene (APOL1) renal-risk variants fail sooner than kidneys from donors with fewer variants. Kidney Donor Risk Index (KDRI) was developed to evaluate organ offers by predicting allograft longevity and includes African American race as a risk factor. Substituting APOL1 genotype for race may refine the KDRI. For 622 deceased African American kidney donors, we applied 10-fold cross-validation approach to estimate contribution of APOL1 variants to a revised KDRI. Cross-validation was repeated 10,000 times to generate distribution of effect size associated with APOL1 genotype. Average effect size was used to derive the revised KDRI weighting. Mean current-KDRI score for all donors was 1.4930 versus mean revised-KDRI score 1.2518 for 529 donors with 0/1 variant and 1.8527 for 93 donors with 2 variants. Original and revised KDRIs had comparable survival prediction errors after transplantation, but the spread in Kidney Donor Profile Index based on presence/absence of 2 APOL1 variants was 37 percentage points. Replacing donor race with APOL1 genotype in KDRI better defines risk associated with kidneys transplanted from deceased African American donors, substantially improves KDRI score for 85-90% of kidneys offered, and enhances the link between donor quality and recipient need. PMID:27862962

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

EPA Science Inventory

BackgroundExposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of a...

Is ozone model bias driven by errors in cloud predictions? A quantitative assessment using satellite cloud retrievals in WRF-Chem

NASA Astrophysics Data System (ADS)

Ryu, Y. H.; Hodzic, A.; Barré, J.; Descombes, G.; Minnis, P.

2017-12-01

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much of the bias in O3 predictions is caused by inaccurate cloud predictions. This study quantifies the errors in surface O3 predictions associated with clouds in summertime over CONUS using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Cloud fields used for photochemistry are corrected based on satellite cloud retrievals in sensitivity simulations. It is found that the WRF-Chem model is able to detect about 60% of clouds in the right locations and generally underpredicts cloud optical depths. The errors in hourly O3 due to the errors in cloud predictions can be up to 60 ppb. On average in summertime over CONUS, the errors in 8-h average O3 of 1-6 ppb are found to be attributable to those in cloud predictions under cloudy sky conditions. The contribution of changes in photolysis rates due to clouds is found to be larger ( 80 % on average) than that of light-dependent BVOC emissions. The effects of cloud corrections on O­3 are about 2 times larger in VOC-limited than NOx-limited regimes, suggesting that the benefits of accurate cloud predictions would be greater in VOC-limited than NOx-limited regimes.

How good are the Garvey-Kelson predictions of nuclear masses?

NASA Astrophysics Data System (ADS)

Morales, Irving O.; López Vieyra, J. C.; Hirsch, J. G.; Frank, A.

2009-09-01

The Garvey-Kelson relations are used in an iterative process to predict nuclear masses in the neighborhood of nuclei with measured masses. Average errors in the predicted masses for the first three iteration shells are smaller than those obtained with the best nuclear mass models. Their quality is comparable with the Audi-Wapstra extrapolations, offering a simple and reproducible procedure for short range mass predictions. A systematic study of the way the error grows as a function of the iteration and the distance to the known masses region, shows that a correlation exists between the error and the residual neutron-proton interaction, produced mainly by the implicit assumption that V varies smoothly along the nuclear landscape.

Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

NASA Astrophysics Data System (ADS)

Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei

2018-01-01

This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.

The introduction of an acute physiological support service for surgical patients is an effective error reduction strategy.

PubMed

Clarke, D L; Kong, V Y; Naidoo, L C; Furlong, H; Aldous, C

2013-01-01

Acute surgical patients are particularly vulnerable to human error. The Acute Physiological Support Team (APST) was created with the twin objectives of identifying high-risk acute surgical patients in the general wards and reducing both the incidence of error and impact of error on these patients. A number of error taxonomies were used to understand the causes of human error and a simple risk stratification system was adopted to identify patients who are particularly at risk of error. During the period November 2012-January 2013 a total of 101 surgical patients were cared for by the APST at Edendale Hospital. The average age was forty years. There were 36 females and 65 males. There were 66 general surgical patients and 35 trauma patients. Fifty-six patients were referred on the day of their admission. The average length of stay in the APST was four days. Eleven patients were haemo-dynamically unstable on presentation and twelve were clinically septic. The reasons for referral were sepsis,(4) respiratory distress,(3) acute kidney injury AKI (38), post-operative monitoring (39), pancreatitis,(3) ICU down-referral,(7) hypoxia,(5) low GCS,(1) coagulopathy.(1) The mortality rate was 13%. A total of thirty-six patients experienced 56 errors. A total of 143 interventions were initiated by the APST. These included institution or adjustment of intravenous fluids (101), blood transfusion,(12) antibiotics,(9) the management of neutropenic sepsis,(1) central line insertion,(3) optimization of oxygen therapy,(7) correction of electrolyte abnormality,(8) correction of coagulopathy.(2) CONCLUSION: Our intervention combined current taxonomies of error with a simple risk stratification system and is a variant of the defence in depth strategy of error reduction. We effectively identified and corrected a significant number of human errors in high-risk acute surgical patients. This audit has helped understand the common sources of error in the general surgical wards and will inform on-going error reduction initiatives. Copyright © 2013 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Forecast models for suicide: Time-series analysis with data from Italy.

PubMed

Preti, Antonio; Lentini, Gianluca

2016-01-01

The prediction of suicidal behavior is a complex task. To fine-tune targeted preventative interventions, predictive analytics (i.e. forecasting future risk of suicide) is more important than exploratory data analysis (pattern recognition, e.g. detection of seasonality in suicide time series). This study sets out to investigate the accuracy of forecasting models of suicide for men and women. A total of 101 499 male suicides and of 39 681 female suicides - occurred in Italy from 1969 to 2003 - were investigated. In order to apply the forecasting model and test its accuracy, the time series were split into a training set (1969 to 1996; 336 months) and a test set (1997 to 2003; 84 months). The main outcome was the accuracy of forecasting models on the monthly number of suicides. These measures of accuracy were used: mean absolute error; root mean squared error; mean absolute percentage error; mean absolute scaled error. In both male and female suicides a change in the trend pattern was observed, with an increase from 1969 onwards to reach a maximum around 1990 and decrease thereafter. The variances attributable to the seasonal and trend components were, respectively, 24% and 64% in male suicides, and 28% and 41% in female ones. Both annual and seasonal historical trends of monthly data contributed to forecast future trends of suicide with a margin of error around 10%. The finding is clearer in male than in female time series of suicide. The main conclusion of the study is that models taking seasonality into account seem to be able to derive information on deviation from the mean when this occurs as a zenith, but they fail to reproduce it when it occurs as a nadir. Preventative efforts should concentrate on the factors that influence the occurrence of increases above the main trend in both seasonal and cyclic patterns of suicides.

Sensitivity and specificity of dosing alerts for dosing errors among hospitalized pediatric patients

PubMed Central

Stultz, Jeremy S; Porter, Kyle; Nahata, Milap C

2014-01-01

Objectives To determine the sensitivity and specificity of a dosing alert system for dosing errors and to compare the sensitivity of a proprietary system with and without institutional customization at a pediatric hospital. Methods A retrospective analysis of medication orders, orders causing dosing alerts, reported adverse drug events, and dosing errors during July, 2011 was conducted. Dosing errors with and without alerts were identified and the sensitivity of the system with and without customization was compared. Results There were 47 181 inpatient pediatric orders during the studied period; 257 dosing errors were identified (0.54%). The sensitivity of the system for identifying dosing errors was 54.1% (95% CI 47.8% to 60.3%) if customization had not occurred and increased to 60.3% (CI 54.0% to 66.3%) with customization (p=0.02). The sensitivity of the system for underdoses was 49.6% without customization and 60.3% with customization (p=0.01). Specificity of the customized system for dosing errors was 96.2% (CI 96.0% to 96.3%) with a positive predictive value of 8.0% (CI 6.8% to 9.3). All dosing errors had an alert over-ridden by the prescriber and 40.6% of dosing errors with alerts were administered to the patient. The lack of indication-specific dose ranges was the most common reason why an alert did not occur for a dosing error. Discussion Advances in dosing alert systems should aim to improve the sensitivity and positive predictive value of the system for dosing errors. Conclusions The dosing alert system had a low sensitivity and positive predictive value for dosing errors, but might have prevented dosing errors from reaching patients. Customization increased the sensitivity of the system for dosing errors. PMID:24496386

An Ensemble System Based on Hybrid EGARCH-ANN with Different Distributional Assumptions to Predict S&P 500 Intraday Volatility

NASA Astrophysics Data System (ADS)

Lahmiri, S.; Boukadoum, M.

2015-10-01

Accurate forecasting of stock market volatility is an important issue in portfolio risk management. In this paper, an ensemble system for stock market volatility is presented. It is composed of three different models that hybridize the exponential generalized autoregressive conditional heteroscedasticity (GARCH) process and the artificial neural network trained with the backpropagation algorithm (BPNN) to forecast stock market volatility under normal, t-Student, and generalized error distribution (GED) assumption separately. The goal is to design an ensemble system where each single hybrid model is capable to capture normality, excess skewness, or excess kurtosis in the data to achieve complementarity. The performance of each EGARCH-BPNN and the ensemble system is evaluated by the closeness of the volatility forecasts to realized volatility. Based on mean absolute error and mean of squared errors, the experimental results show that proposed ensemble model used to capture normality, skewness, and kurtosis in data is more accurate than the individual EGARCH-BPNN models in forecasting the S&P 500 intra-day volatility based on one and five-minute time horizons data.

Combining empirical approaches and error modelling to enhance predictive uncertainty estimation in extrapolation for operational flood forecasting. Tests on flood events on the Loire basin, France.

NASA Astrophysics Data System (ADS)

Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles

2017-04-01

An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in France (major spring floods in June 2016 on the Loire river tributaries and flash floods in fall 2016) will be shown and discussed. References Bourgin, F. (2014). How to assess the predictive uncertainty in hydrological modelling? An exploratory work on a large sample of watersheds, AgroParisTech Wang, Q. J., Shrestha, D. L., Robertson, D. E. and Pokhrel, P (2012). A log-sinh transformation for data normalization and variance stabilization. Water Resources Research, , W05514, doi:10.1029/2011WR010973

Validation of the Kp Geomagnetic Index Forecast at CCMC

NASA Astrophysics Data System (ADS)

Frechette, B. P.; Mays, M. L.

2017-12-01

The Community Coordinated Modeling Center (CCMC) Space Weather Research Center (SWRC) sub-team provides space weather services to NASA robotic mission operators and science campaigns and prototypes new models, forecasting techniques, and procedures. The Kp index is a measure of geomagnetic disturbances for space weather in the magnetosphere such as geomagnetic storms and substorms. In this study, we performed validation on the Newell et al. (2007) Kp prediction equation from December 2010 to July 2017. The purpose of this research is to understand the Kp forecast performance because it's critical for NASA missions to have confidence in the space weather forecast. This research was done by computing the Kp error for each forecast (average, minimum, maximum) and each synoptic period. Then to quantify forecast performance we computed the mean error, mean absolute error, root mean square error, multiplicative bias and correlation coefficient. A contingency table was made for each forecast and skill scores were computed. The results are compared to the perfect score and reference forecast skill score. In conclusion, the skill score and error results show that the minimum of the predicted Kp over each synoptic period from the Newell et al. (2007) Kp prediction equation performed better than the maximum or average of the prediction. However, persistence (reference forecast) outperformed all of the Kp forecasts (minimum, maximum, and average). Overall, the Newell Kp prediction still predicts within a range of 1, even though persistence beats it.

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

PubMed

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

Effects of uncertainty and variability on population declines and IUCN Red List classifications.

PubMed

Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

2018-01-22

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories. © 2018 Society for Conservation Biology.

Normal accidents: human error and medical equipment design.

PubMed

Dain, Steven

2002-01-01

High-risk systems, which are typical of our technologically complex era, include not just nuclear power plants but also hospitals, anesthesia systems, and the practice of medicine and perfusion. In high-risk systems, no matter how effective safety devices are, some types of accidents are inevitable because the system's complexity leads to multiple and unexpected interactions. It is important for healthcare providers to apply a risk assessment and management process to decisions involving new equipment and procedures or staffing matters in order to minimize the residual risks of latent errors, which are amenable to correction because of the large window of opportunity for their detection. This article provides an introduction to basic risk management and error theory principles and examines ways in which they can be applied to reduce and mitigate the inevitable human errors that accompany high-risk systems. The article also discusses "human factor engineering" (HFE), the process which is used to design equipment/ human interfaces in order to mitigate design errors. The HFE process involves interaction between designers and endusers to produce a series of continuous refinements that are incorporated into the final product. The article also examines common design problems encountered in the operating room that may predispose operators to commit errors resulting in harm to the patient. While recognizing that errors and accidents are unavoidable, organizations that function within a high-risk system must adopt a "safety culture" that anticipates problems and acts aggressively through an anonymous, "blameless" reporting mechanism to resolve them. We must continuously examine and improve the design of equipment and procedures, personnel, supplies and materials, and the environment in which we work to reduce error and minimize its effects. Healthcare providers must take a leading role in the day-to-day management of the "Perioperative System" and be a role model in promoting a culture of safety in their organizations.

Safety and Performance Analysis of the Non-Radar Oceanic/Remote Airspace In-Trail Procedure

NASA Technical Reports Server (NTRS)

Carreno, Victor A.; Munoz, Cesar A.

2007-01-01

This document presents a safety and performance analysis of the nominal case for the In-Trail Procedure (ITP) in a non-radar oceanic/remote airspace. The analysis estimates the risk of collision between the aircraft performing the ITP and a reference aircraft. The risk of collision is only estimated for the ITP maneuver and it is based on nominal operating conditions. The analysis does not consider human error, communication error conditions, or the normal risk of flight present in current operations. The hazards associated with human error and communication errors are evaluated in an Operational Hazards Analysis presented elsewhere.

Measured and predicted rotor performance for the SERI advanced wind turbine blades

NASA Astrophysics Data System (ADS)

Tangler, J.; Smith, B.; Kelley, N.; Jager, D.

1992-02-01

Measured and predicted rotor performance for the Solar Energy Research Institute (SERI) advanced wind turbine blades were compared to assess the accuracy of predictions and to identify the sources of error affecting both predictions and measurements. An awareness of these sources of error contributes to improved prediction and measurement methods that will ultimately benefit future rotor design efforts. Propeller/vane anemometers were found to underestimate the wind speed in turbulent environments such as the San Gorgonio Pass wind farm area. Using sonic or cup anemometers, good agreement was achieved between predicted and measured power output for wind speeds up to 8 m/sec. At higher wind speeds an optimistic predicted power output and the occurrence of peak power at wind speeds lower than measurements resulted from the omission of turbulence and yaw error. In addition, accurate two-dimensional (2-D) airfoil data prior to stall and a post stall airfoil data synthesization method that reflects three-dimensional (3-D) effects were found to be essential for accurate performance prediction.

NASA Lewis Stirling engine computer code evaluation

NASA Technical Reports Server (NTRS)

Sullivan, Timothy J.

1989-01-01

In support of the U.S. Department of Energy's Stirling Engine Highway Vehicle Systems program, the NASA Lewis Stirling engine performance code was evaluated by comparing code predictions without engine-specific calibration factors to GPU-3, P-40, and RE-1000 Stirling engine test data. The error in predicting power output was -11 percent for the P-40 and 12 percent for the Re-1000 at design conditions and 16 percent for the GPU-3 at near-design conditions (2000 rpm engine speed versus 3000 rpm at design). The efficiency and heat input predictions showed better agreement with engine test data than did the power predictions. Concerning all data points, the error in predicting the GPU-3 brake power was significantly larger than for the other engines and was mainly a result of inaccuracy in predicting the pressure phase angle. Analysis into this pressure phase angle prediction error suggested that improvements to the cylinder hysteresis loss model could have a significant effect on overall Stirling engine performance predictions.

CPO Prediction: Accuracy Assessment and Impact on UT1 Intensive Results

NASA Technical Reports Server (NTRS)

Malkin, Zinovy

2010-01-01

The UT1 Intensive results heavily depend on the celestial pole offset (CPO) model used during data processing. Since accurate CPO values are available with a delay of two to four weeks, CPO predictions are necessarily applied to the UT1 Intensive data analysis, and errors in the predictions can influence the operational UT1 accuracy. In this paper we assess the real accuracy of CPO prediction using the actual IERS and PUL predictions made in 2007-2009. Also, results of operational processing were analyzed to investigate the actual impact of EOP prediction errors on the rapid UT1 results. It was found that the impact of CPO prediction errors is at a level of several microseconds, whereas the impact of the inaccuracy in the polar motion prediction may be about one order of magnitude larger for ultra-rapid UT1 results. The situation can be amended if the IERS Rapid solution will be updated more frequently.

Association of grey matter changes with stability and flexibility of prediction in akinetic-rigid Parkinson's disease.

PubMed

Trempler, Ima; Binder, Ellen; El-Sourani, Nadiya; Schiffler, Patrick; Tenberge, Jan-Gerd; Schiffer, Anne-Marike; Fink, Gereon R; Schubotz, Ricarda I

2018-06-01

Parkinson's disease (PD), which is caused by degeneration of dopaminergic neurons in the midbrain, results in a heterogeneous clinical picture including cognitive decline. Since the phasic signal of dopamine neurons is proposed to guide learning by signifying mismatches between subjects' expectations and external events, we here investigated whether akinetic-rigid PD patients without mild cognitive impairment exhibit difficulties in dealing with either relevant (requiring flexibility) or irrelevant (requiring stability) prediction errors. Following our previous study on flexibility and stability in prediction (Trempler et al. J Cogn Neurosci 29(2):298-309, 2017), we then assessed whether deficits would correspond with specific structural alterations in dopaminergic regions as well as in inferior frontal cortex, medial prefrontal cortex, and the hippocampus. Twenty-one healthy controls and twenty-one akinetic-rigid PD patients on and off medication performed a task which required to serially predict upcoming items. Switches between predictable sequences had to be indicated via button press, whereas sequence omissions had to be ignored. Independent of the disease, midbrain volume was related to a general response bias to unexpected events, whereas right putamen volume correlated with the ability to discriminate between relevant and irrelevant prediction errors. However, patients compared with healthy participants showed deficits in stabilisation against irrelevant prediction errors, associated with thickness of right inferior frontal gyrus and left medial prefrontal cortex. Flexible updating due to relevant prediction errors was also affected in patients compared with controls and associated with right hippocampus volume. Dopaminergic medication influenced behavioural performance across, but not within the patients. Our exploratory study warrants further research on deficient prediction error processing and its structural correlates as a core of cognitive symptoms occurring already in early stages of the disease.

[What Surgeons Should Know about Risk Management].

PubMed

Strametz, R; Tannheimer, M; Rall, M

2017-02-01

Background: The fact that medical treatment is associated with errors has long been recognized. Based on the principle of "first do no harm", numerous efforts have since been made to prevent such errors or limit their impact. However, recent statistics show that these measures do not sufficiently prevent grave mistakes with serious consequences. Preventable mistakes such as wrong patient or wrong site surgery still frequently occur in error statistics. Methods: Based on insight from research on human error, in due consideration of recent legislative regulations in Germany, the authors give an overview of the clinical risk management tools needed to identify risks in surgery, analyse their causes, and determine adequate measures to manage those risks depending on their relevance. The use and limitations of critical incident reporting systems (CIRS), safety checklists and crisis resource management (CRM) are highlighted. Also the rationale for IT systems to support the risk management process is addressed. Results/Conclusion: No single tool of risk management can be effective as a standalone instrument, but unfolds its effect only when embedded in a superordinate risk management system, which integrates tailor-made elements to increase patient safety into the workflows of each organisation. Competence in choosing adequate tools, effective IT systems to support the risk management process as well as leadership and commitment to constructive handling of human error are crucial components to establish a safety culture in surgery. Georg Thieme Verlag KG Stuttgart · New York.

Missed Diagnosis of Cardiovascular Disease in Outpatient General Medicine: Insights from Malpractice Claims Data.

PubMed

Quinn, Gene R; Ranum, Darrell; Song, Ellen; Linets, Margarita; Keohane, Carol; Riah, Heather; Greenberg, Penny

2017-10-01

Diagnostic errors are an underrecognized source of patient harm, and cardiovascular disease can be challenging to diagnose in the ambulatory setting. Although malpractice data can inform diagnostic error reduction efforts, no studies have examined outpatient cardiovascular malpractice cases in depth. A study was conducted to examine the characteristics of outpatient cardiovascular malpractice cases brought against general medicine practitioners. Some 3,407 closed malpractice claims were analyzed in outpatient general medicine from CRICO Strategies' Comparative Benchmarking System database-the largest detailed database of paid and unpaid malpractice in the world-and multivariate models were created to determine the factors that predicted case outcomes. Among the 153 patients in cardiovascular malpractice cases for whom patient comorbidities were coded, the majority (63%) had at least one traditional cardiac risk factor, such as diabetes, tobacco use, or previous cardiovascular disease. Cardiovascular malpractice cases were more likely to involve an allegation of error in diagnosis (75% vs. 47%, p <0.0001), have high clinical severity (86% vs. 49%, p <0.0001) and result in death (75% vs. 27%, p <0.0001), as compared to noncardiovascular cases. Initial diagnoses of nonspecific chest pain and mimics of cardiovascular pain (for example, esophageal disease) were common and independently increased the likelihood of a claim resulting in a payment (p <0.01). Cardiovascular malpractice cases against outpatient general medicine physicians mostly occur in patients with conventional risk factors for coronary artery disease and are often diagnosed with common mimics of cardiovascular pain. These findings suggest that these patients may be high-yield targets for preventing diagnostic errors in the ambulatory setting. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Can we predict 4-year graduation in podiatric medical school using admission data?

PubMed

Sesodia, Sanjay; Molnar, David; Shaw, Graham P

2012-01-01

This study examined the predictive ability of educational background and demographic variables, available at the admission stage, to identify applicants who will graduate in 4 years from podiatric medical school. A logistic regression model was used to identify two predictors of 4-year graduation: age at matriculation and total Medical College Admission Test score. The model was cross-validated using a second independent sample from the same population. Cross-validation gives greater confidence that the results could be more generally applied. Total Medical College Admission Test score was the strongest predictor of 4-year graduation, with age at matriculation being a statistically significant but weaker predictor. Despite the model's capacity to predict 4-year graduation better than random assignment, a sufficient amount of error in prediction remained, suggesting that important predictors are missing from the model. Furthermore, the high rate of false-positives makes it inappropriate to use age and Medical College Admission Test score as admission screens in an attempt to eliminate attrition by not accepting at-risk students.

Accounting for sampling variability, injury under-reporting, and sensor error in concussion injury risk curves.

PubMed

Elliott, Michael R; Margulies, Susan S; Maltese, Matthew R; Arbogast, Kristy B

2015-09-18

There has been recent dramatic increase in the use of sensors affixed to the heads or helmets of athletes to measure the biomechanics of head impacts that lead to concussion. The relationship between injury and linear or rotational head acceleration measured by such sensors can be quantified with an injury risk curve. The utility of the injury risk curve relies on the accuracy of both the clinical diagnosis and the biomechanical measure. The focus of our analysis was to demonstrate the influence of three sources of error on the shape and interpretation of concussion injury risk curves: sampling variability associated with a rare event, concussion under-reporting, and sensor measurement error. We utilized Bayesian statistical methods to generate synthetic data from previously published concussion injury risk curves developed using data from helmet-based sensors on collegiate football players and assessed the effect of the three sources of error on the risk relationship. Accounting for sampling variability adds uncertainty or width to the injury risk curve. Assuming a variety of rates of unreported concussions in the non-concussed group, we found that accounting for under-reporting lowers the rotational acceleration required for a given concussion risk. Lastly, after accounting for sensor error, we find strengthened relationships between rotational acceleration and injury risk, further lowering the magnitude of rotational acceleration needed for a given risk of concussion. As more accurate sensors are designed and more sensitive and specific clinical diagnostic tools are introduced, our analysis provides guidance for the future development of comprehensive concussion risk curves. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mean Bias in Seasonal Forecast Model and ENSO Prediction Error.

PubMed

Kim, Seon Tae; Jeong, Hye-In; Jin, Fei-Fei

2017-07-20

This study uses retrospective forecasts made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in predicting the amplitude of El Niño Southern Oscillation (ENSO)-driven sea surface temperature variability. When utilizing Bjerknes coupled stability (BJ) index analysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by those in the growth rate estimated by the BJ index. ENSO amplitude forecast errors are most strongly associated with the errors in both the thermocline slope response and surface wind response to forcing over the tropical Pacific, leading to errors in thermocline feedback. This study concludes that upper ocean temperature bias in the equatorial Pacific, which becomes more intense with increasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in ENSO amplitude.

The impact of response measurement error on the analysis of designed experiments

DOE PAGES

Anderson-Cook, Christine Michaela; Hamada, Michael Scott; Burr, Thomas Lee

2016-11-01

This study considers the analysis of designed experiments when there is measurement error in the true response or so-called response measurement error. We consider both additive and multiplicative response measurement errors. Through a simulation study, we investigate the impact of ignoring the response measurement error in the analysis, that is, by using a standard analysis based on t-tests. In addition, we examine the role of repeat measurements in improving the quality of estimation and prediction in the presence of response measurement error. We also study a Bayesian approach that accounts for the response measurement error directly through the specification ofmore » the model, and allows including additional information about variability in the analysis. We consider the impact on power, prediction, and optimization. Copyright © 2015 John Wiley & Sons, Ltd.« less

The impact of response measurement error on the analysis of designed experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson-Cook, Christine Michaela; Hamada, Michael Scott; Burr, Thomas Lee

This study considers the analysis of designed experiments when there is measurement error in the true response or so-called response measurement error. We consider both additive and multiplicative response measurement errors. Through a simulation study, we investigate the impact of ignoring the response measurement error in the analysis, that is, by using a standard analysis based on t-tests. In addition, we examine the role of repeat measurements in improving the quality of estimation and prediction in the presence of response measurement error. We also study a Bayesian approach that accounts for the response measurement error directly through the specification ofmore » the model, and allows including additional information about variability in the analysis. We consider the impact on power, prediction, and optimization. Copyright © 2015 John Wiley & Sons, Ltd.« less

Assessing the effect of public health information by incentivised risk estimation: An example on Swedish snus.

PubMed

Bergsvik, Daniel; Rogeberg, Ole

2018-04-01

The provision of accurate information on health damaging behaviours and products is a widely accepted and widespread governmental task. It is easily mismanaged. This study demonstrates a simple method which can help to evaluate whether such information corrects recipient risk beliefs. Participants assess risks numerically, before and after being exposed to a relevant risk communication. Accuracy is incentivised by awarding financial prizes to answers closest to a pursued risk belief. To illustrate this method, 228 students from the University of Oslo, Norway, were asked to estimate the mortality risk of Swedish snus and cigarettes twice, before and after being exposed to one of three risk communications with information on the health dangers of snus. The data allow us to measure how participants updated their risk beliefs after being exposed to different risk communications. Risk information from the government strongly distorted risk perceptions for snus. A newspaper article discussing the relative risks of cigarettes and snus reduced belief errors regarding snus risks, but increased belief errors regarding smoking. The perceived quality of the risk communication was not associated with decreased belief errors. Public health information can potentially make the public less informed on risks about harmful products or behaviours. This risk can be reduced by targeting identified, measurable belief errors and empirically assessing how alternative communications affect these. The proposed method of incentivised risk estimation might be helpful in future assessments of risk communications. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Neuronal Reward and Decision Signals: From Theories to Data

PubMed Central

Schultz, Wolfram

2015-01-01

Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act. PMID:26109341

Structured inspection of medications carried and stored by emergency medical services agencies identifies practices that may lead to medication errors.

PubMed

Kupas, Douglas F; Shayhorn, Meghan A; Green, Paul; Payton, Thomas F

2012-01-01

Medications are essential to emergency medical services (EMS) agencies when providing lifesaving care, but the EMS environment has challenges related to safe medication storage when compared with a hospital setting. We developed a structured process, based on common pharmacy practices, to review medications carried by EMS agencies to identify situations that may lead to medication error and to determine some best practices that may reduce potential errors and the risk of patient harm. To provide a descriptive account of EMS practices related to carrying and storing medications that have the potential for causing a medication administration error or patient harm. Using a structured process for inspection, an emergency medicine pharmacist and emergency physician(s) reviewed the medication carrying and storage practices of all nine advanced life support ambulance agencies within a five-county EMS region. Each medication carried and stored by the EMS agency was inspected for predetermined and spontaneously observed issues that could lead to medication error. These issues were documented and photographed. Two EMS medical directors reviewed each potential error for the risk of producing patient harm and assigned each to a category of high, moderate, or low risk. Because issues of temperature on EMS medications have been addressed elsewhere, this study concentrated on potential for EMS medication administration errors exclusive of storage temperatures. When reviewing medications carried by the nine EMS agencies, 38 medication safety issues were identified (range 1 to 8 per EMS agency). Of these, 16 were considered to be high risk, 14 moderate risk, and eight low risk for patient harm. Examples of potential issues included carrying expired medications, container-labeling issues, different medications stored in look-alike vials or prefilled syringes in the same compartment, and carrying crystalloid solutions next to solutions premixed with a medication. When reviewing medications stored at the EMS agency stations, eight safety issues were identified (range from 0 to 4 per station), including five moderate-risk and three low-risk issues. No agency had any high-risk medication issues related to storage of medication stock in the station. We observed potential medication safety issues related to how medications are carried and stored at all nine EMS agencies in a five-county region. Understanding these issues may assist EMS agencies in reducing the potential for a medication error and risk of patient harm. More research is needed to determine whether following these suggested best practices for carrying medications on EMS vehicles actually reduces errors in medication administration by EMS providers or decreases patient harm.

Risk behaviours for organism transmission in health care delivery-A two month unstructured observational study.

PubMed

Lindberg, Maria; Lindberg, Magnus; Skytt, Bernice

2017-05-01

Errors in infection control practices risk patient safety. The probability for errors can increase when care practices become more multifaceted. It is therefore fundamental to track risk behaviours and potential errors in various care situations. The aim of this study was to describe care situations involving risk behaviours for organism transmission that could lead to subsequent healthcare-associated infections. Unstructured nonparticipant observations were performed at three medical wards. Healthcare personnel (n=27) were shadowed, in total 39h, on randomly selected weekdays between 7:30 am and 12 noon. Content analysis was used to inductively categorize activities into tasks and based on the character into groups. Risk behaviours for organism transmission were deductively classified into types of errors. Multiple response crosstabs procedure was used to visualize the number and proportion of errors in tasks. One-Way ANOVA with Bonferroni post Hoc test was used to determine differences among the three groups of activities. The qualitative findings gives an understanding of that risk behaviours for organism transmission goes beyond the five moments of hand hygiene and also includes the handling and placement of materials and equipment. The tasks with the highest percentage of errors were; 'personal hygiene', 'elimination' and 'dressing/wound care'. The most common types of errors in all identified tasks were; 'hand disinfection', 'glove usage', and 'placement of materials'. Significantly more errors (p<0.0001) were observed the more multifaceted (single, combined or interrupted) the activity was. The numbers and types of errors as well as the character of activities performed in care situations described in this study confirm the need to improve current infection control practices. It is fundamental that healthcare personnel practice good hand hygiene however effective preventive hygiene is complex in healthcare activities due to the multifaceted care situations, especially when activities are interrupted. A deeper understanding of infection control practices that goes beyond the sense of security by means of hand disinfection and use of gloves is needed as materials and surfaces in the care environment might be contaminated and thus pose a risk for organism transmission. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive error detection in pianists: a combined ERP and motion capture study

PubMed Central

Maidhof, Clemens; Pitkäniemi, Anni; Tervaniemi, Mari

2013-01-01

Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP) studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70–100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one's own actions. However, in previous investigations, correct and incorrect pitch events were confounded by their different tempi. In addition, no data about the underlying movements were available. In the present study, we exploratively recorded the ERPs and 3D movement data of pianists' fingers simultaneously while they performed fingering exercises from memory. Results showed a pre-error negativity for incorrect keystrokes when both correct and incorrect keystrokes were performed with comparable tempi. Interestingly, even correct notes immediately preceding erroneous keystrokes elicited a very similar negativity. In addition, we explored the possibility of computing ERPs time-locked to a kinematic landmark in the finger motion trajectories defined by when a finger makes initial contact with the key surface, that is, at the onset of tactile feedback. Results suggest that incorrect notes elicited a small difference after the onset of tactile feedback, whereas correct notes preceding incorrect ones elicited negativity before the onset of tactile feedback. The results tentatively suggest that tactile feedback plays an important role in error-monitoring during piano performance, because the comparison between predicted and actual sensory (tactile) feedback may provide the information necessary for the detection of an upcoming error. PMID:24133428

Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network

NASA Astrophysics Data System (ADS)

Wang, Zhen-yu; Zhang, Li-jie

2017-10-01

Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s

An improved reversible data hiding algorithm based on modification of prediction errors

NASA Astrophysics Data System (ADS)

Jafar, Iyad F.; Hiary, Sawsan A.; Darabkh, Khalid A.

2014-04-01

Reversible data hiding algorithms are concerned with the ability of hiding data and recovering the original digital image upon extraction. This issue is of interest in medical and military imaging applications. One particular class of such algorithms relies on the idea of histogram shifting of prediction errors. In this paper, we propose an improvement over one popular algorithm in this class. The improvement is achieved by employing a different predictor, the use of more bins in the prediction error histogram in addition to multilevel embedding. The proposed extension shows significant improvement over the original algorithm and its variations.

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception

PubMed Central

Davis, Matthew H.

2016-01-01

Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conversely, Predictive Coding suggests that expected features are suppressed so that unexpected features of the speech input (Prediction Errors) are processed further. The present work is aimed at distinguishing between these two accounts of how prior knowledge influences speech perception. By combining behavioural, univariate, and multivariate fMRI measures of how sensory detail and prior expectations influence speech perception with computational modelling, we provide evidence in favour of Prediction Error computations. Increased sensory detail and informative expectations have additive behavioural and univariate neural effects because they both improve the accuracy of word report and reduce the BOLD signal in lateral temporal lobe regions. However, sensory detail and informative expectations have interacting effects on speech representations shown by multivariate fMRI in the posterior superior temporal sulcus. When prior knowledge was absent, increased sensory detail enhanced the amount of speech information measured in superior temporal multivoxel patterns, but with informative expectations, increased sensory detail reduced the amount of measured information. Computational simulations of Sharpened Signals and Prediction Errors during speech perception could both explain these behavioural and univariate fMRI observations. However, the multivariate fMRI observations were uniquely simulated by a Prediction Error and not a Sharpened Signal model. The interaction between prior expectation and sensory detail provides evidence for a Predictive Coding account of speech perception. Our work establishes methods that can be used to distinguish representations of Prediction Error and Sharpened Signals in other perceptual domains. PMID:27846209

Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing

PubMed Central

Lefebvre, Germain; Blakemore, Sarah-Jayne

2017-01-01

Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice. PMID:28800597

Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing.

PubMed

Palminteri, Stefano; Lefebvre, Germain; Kilford, Emma J; Blakemore, Sarah-Jayne

2017-08-01

Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice.

Understanding adverse events: human factors.

PubMed Central

Reason, J

1995-01-01

(1) Human rather than technical failures now represent the greatest threat to complex and potentially hazardous systems. This includes healthcare systems. (2) Managing the human risks will never be 100% effective. Human fallibility can be moderated, but it cannot be eliminated. (3) Different error types have different underlying mechanisms, occur in different parts of the organisation, and require different methods of risk management. The basic distinctions are between: Slips, lapses, trips, and fumbles (execution failures) and mistakes (planning or problem solving failures). Mistakes are divided into rule based mistakes and knowledge based mistakes. Errors (information-handling problems) and violations (motivational problems) Active versus latent failures. Active failures are committed by those in direct contact with the patient, latent failures arise in organisational and managerial spheres and their adverse effects may take a long time to become evident. (4) Safety significant errors occur at all levels of the system, not just at the sharp end. Decisions made in the upper echelons of the organisation create the conditions in the workplace that subsequently promote individual errors and violations. Latent failures are present long before an accident and are hence prime candidates for principled risk management. (5) Measures that involve sanctions and exhortations (that is, moralistic measures directed to those at the sharp end) have only very limited effectiveness, especially so in the case of highly trained professionals. (6) Human factors problems are a product of a chain of causes in which the individual psychological factors (that is, momentary inattention, forgetting, etc) are the last and least manageable links. Attentional "capture" (preoccupation or distraction) is a necessary condition for the commission of slips and lapses. Yet, its occurrence is almost impossible to predict or control effectively. The same is true of the factors associated with forgetting. States of mind contributing to error are thus extremely difficult to manage; they can happen to the best of people at any time. (7) People do not act in isolation. Their behaviour is shaped by circumstances. The same is true for errors and violations. The likelihood of an unsafe act being committed is heavily influenced by the nature of the task and by the local workplace conditions. These, in turn, are the product of "upstream" organisational factors. Great gains in safety can ve achieved through relatively small modifications of equipment and workplaces. (8) Automation and increasing advanced equipment do not cure human factors problems, they merely relocate them. In contrast, training people to work effectively in teams costs little, but has achieved significant enhancements of human performance in aviation. (9) Effective risk management depends critically on a confidential and preferable anonymous incident monitoring system that records the individual, task, situational, and organisational factors associated with incidents and near misses. (10) Effective risk management means the simultaneous and targeted deployment of limited remedial resources at different levels of the system: the individual or team, the task, the situation, and the organisation as a whole. PMID:10151618

A Bayesian model for estimating multi-state disease progression.

PubMed

Shen, Shiwen; Han, Simon X; Petousis, Panayiotis; Weiss, Robert E; Meng, Frank; Bui, Alex A T; Hsu, William

2017-02-01

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Errors in prescriptions and their preparation at the outpatient pharmacy of a regional hospital].

PubMed

Alvarado A, Carolina; Ossa G, Ximena; Bustos M, Luis

2017-01-01

Adverse effects of medications are an important cause of morbidity and hospital admissions. Errors in prescription or preparation of medications by pharmacy personnel are a factor that may influence these occurrence of the adverse effects Aim: To assess the frequency and type of errors in prescriptions and in their preparation at the pharmacy unit of a regional public hospital. Prescriptions received by ambulatory patients and those being discharged from the hospital, were reviewed using a 12-item checklist. The preparation of such prescriptions at the pharmacy unit was also reviewed using a seven item checklist. Seventy two percent of prescriptions had at least one error. The most common mistake was the impossibility of determining the concentration of the prescribed drug. Prescriptions for patients being discharged from the hospital had the higher number of errors. When a prescription had more than two drugs, the risk of error increased 2.4 times. Twenty four percent of prescription preparations had at least one error. The most common mistake was the labeling of drugs with incomplete medical indications. When a preparation included more than three drugs, the risk of preparation error increased 1.8 times. Prescription and preparation of medication delivered to patients had frequent errors. The most important risk factor for errors was the number of drugs prescribed.

Using beta binomials to estimate classification uncertainty for ensemble models.

PubMed

Clark, Robert D; Liang, Wenkel; Lee, Adam C; Lawless, Michael S; Fraczkiewicz, Robert; Waldman, Marvin

2014-01-01

Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set.

Antiretroviral medication prescribing errors are common with hospitalization of HIV-infected patients.

PubMed

Commers, Tessa; Swindells, Susan; Sayles, Harlan; Gross, Alan E; Devetten, Marcel; Sandkovsky, Uriel

2014-01-01

Errors in prescribing antiretroviral therapy (ART) often occur with the hospitalization of HIV-infected patients. The rapid identification and prevention of errors may reduce patient harm and healthcare-associated costs. A retrospective review of hospitalized HIV-infected patients was carried out between 1 January 2009 and 31 December 2011. Errors were documented as omission, underdose, overdose, duplicate therapy, incorrect scheduling and/or incorrect therapy. The time to error correction was recorded. Relative risks (RRs) were computed to evaluate patient characteristics and error rates. A total of 289 medication errors were identified in 146/416 admissions (35%). The most common was drug omission (69%). At an error rate of 31%, nucleoside reverse transcriptase inhibitors were associated with an increased risk of error when compared with protease inhibitors (RR 1.32; 95% CI 1.04-1.69) and co-formulated drugs (RR 1.59; 95% CI 1.19-2.09). Of the errors, 31% were corrected within the first 24 h, but over half (55%) were never remedied. Admissions with an omission error were 7.4 times more likely to have all errors corrected within 24 h than were admissions without an omission. Drug interactions with ART were detected on 51 occasions. For the study population (n = 177), an increased risk of admission error was observed for black (43%) compared with white (28%) individuals (RR 1.53; 95% CI 1.16-2.03) but no significant differences were observed between white patients and other minorities or between men and women. Errors in inpatient ART were common, and the majority were never detected. The most common errors involved omission of medication, and nucleoside reverse transcriptase inhibitors had the highest rate of prescribing error. Interventions to prevent and correct errors are urgently needed.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains.

PubMed

Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

2017-05-01

Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xing, Y; Macq, B; Bondar, L

Purpose: To quantify the accuracy in predicting the Bragg peak position using simulated in-room measurements of prompt gamma (PG) emissions for realistic treatment error scenarios that combine several sources of errors. Methods: Prompt gamma measurements by a knife-edge slit camera were simulated using an experimentally validated analytical simulation tool. Simulations were performed, for 143 treatment error scenarios, on an anthropomorphic phantom and a pencil beam scanning plan for nasal cavity. Three types of errors were considered: translation along each axis, rotation around each axis, and CT-calibration errors with magnitude ranging respectively, between −3 and 3 mm, −5 and 5 degrees,more » and between −5 and +5%. We investigated the correlation between the Bragg peak (BP) shift and the horizontal shift of PG profiles. The shifts were calculated between the planned (reference) position and the position by the error scenario. The prediction error for one spot was calculated as the absolute difference between the PG profile shift and the BP shift. Results: The PG shift was significantly and strongly correlated with the BP shift for 92% of the cases (p<0.0001, Pearson correlation coefficient R>0.8). Moderate but significant correlations were obtained for all cases that considered only CT-calibration errors and for 1 case that combined translation and CT-errors (p<0.0001, R ranged between 0.61 and 0.8). The average prediction errors for the simulated scenarios ranged between 0.08±0.07 and 1.67±1.3 mm (grand mean 0.66±0.76 mm). The prediction error was moderately correlated with the value of the BP shift (p=0, R=0.64). For the simulated scenarios the average BP shift ranged between −8±6.5 mm and 3±1.1 mm. Scenarios that considered combinations of the largest treatment errors were associated with large BP shifts. Conclusion: Simulations of in-room measurements demonstrate that prompt gamma profiles provide reliable estimation of the Bragg peak position for complex error scenarios. Yafei Xing and Luiza Bondar are funded by BEWARE grants from the Walloon Region. The work presents simulations results for a prompt gamma camera prototype developed by IBA.« less

Error Patterns in Ordering Fractions among At-Risk Fourth-Grade Students

ERIC Educational Resources Information Center

Malone, Amelia Schneider; Fuchs, Lynn S.

2015-01-01

The 3 purposes of this study were to: (a) describe fraction ordering errors among at-risk 4th-grade students; (b) assess the effect of part-whole understanding and accuracy of fraction magnitude estimation on the probability of committing errors; and (c) examine the effect of students' ability to explain comparing problems on the probability of…

Error Patterns in Ordering Fractions among At-Risk Fourth-Grade Students

ERIC Educational Resources Information Center

Malone, Amelia S.; Fuchs, Lynn S.

2017-01-01

The three purposes of this study were to (a) describe fraction ordering errors among at-risk fourth grade students, (b) assess the effect of part-whole understanding and accuracy of fraction magnitude estimation on the probability of committing errors, and (c) examine the effect of students' ability to explain comparing problems on the probability…

Real and hypothetical monetary rewards modulate risk taking in the brain.

PubMed

Xu, Sihua; Pan, Yu; Wang, You; Spaeth, Andrea M; Qu, Zhe; Rao, Hengyi

2016-07-07

Both real and hypothetical monetary rewards are widely used as reinforcers in risk taking and decision making studies. However, whether real and hypothetical monetary rewards modulate risk taking and decision making in the same manner remains controversial. In this study, we used event-related potentials (ERP) with a balloon analogue risk task (BART) paradigm to examine the effects of real and hypothetical monetary rewards on risk taking in the brain. Behavioral data showed reduced risk taking after negative feedback (money loss) during the BART with real rewards compared to those with hypothetical rewards, suggesting increased loss aversion with real monetary rewards. The ERP data demonstrated a larger feedback-related negativity (FRN) in response to money loss during risk taking with real rewards compared to those with hypothetical rewards, which may reflect greater prediction error or regret emotion after real monetary losses. These findings demonstrate differential effects of real versus hypothetical monetary rewards on risk taking behavior and brain activity, suggesting a caution when drawing conclusions about real choices from hypothetical studies of intended behavior, especially when large rewards are used. The results have implications for future utility of real and hypothetical monetary rewards in studies of risk taking and decision making.

Prediction and error of baldcypress stem volume from stump diameter

Treesearch

Bernard R. Parresol

1998-01-01

The need to estimate the volume of removals occurs for many reasons, such as in trespass cases, severance tax reports, and post-harvest assessments. A logarithmic model is presented for prediction of baldcypress total stem cubic foot volume using stump diameter as the independent variable. Because the error of prediction is as important as the volume estimate, the...

Effects of ocean initial perturbation on developing phase of ENSO in a coupled seasonal prediction model

NASA Astrophysics Data System (ADS)

Lee, Hyun-Chul; Kumar, Arun; Wang, Wanqiu

2018-03-01

Coupled prediction systems for seasonal and inter-annual variability in the tropical Pacific are initialized from ocean analyses. In ocean initial states, small scale perturbations are inevitably smoothed or distorted by the observational limits and data assimilation procedures, which tends to induce potential ocean initial errors for the El Nino-Southern Oscillation (ENSO) prediction. Here, the evolution and effects of ocean initial errors from the small scale perturbation on the developing phase of ENSO are investigated by an ensemble of coupled model predictions. Results show that the ocean initial errors at the thermocline in the western tropical Pacific grow rapidly to project on the first mode of equatorial Kelvin wave and propagate to the east along the thermocline. In boreal spring when the surface buoyancy flux weakens in the eastern tropical Pacific, the subsurface errors influence sea surface temperature variability and would account for the seasonal dependence of prediction skill in the NINO3 region. It is concluded that the ENSO prediction in the eastern tropical Pacific after boreal spring can be improved by increasing the observational accuracy of subsurface ocean initial states in the western tropical Pacific.

Recruitment into diabetes prevention programs: what is the impact of errors in self-reported measures of obesity?

PubMed

Hernan, Andrea; Philpot, Benjamin; Janus, Edward D; Dunbar, James A

2012-07-08

Error in self-reported measures of obesity has been frequently described, but the effect of self-reported error on recruitment into diabetes prevention programs is not well established. The aim of this study was to examine the effect of using self-reported obesity data from the Finnish diabetes risk score (FINDRISC) on recruitment into the Greater Green Triangle Diabetes Prevention Project (GGT DPP). The GGT DPP was a structured group-based lifestyle modification program delivered in primary health care settings in South-Eastern Australia. Between 2004-05, 850 FINDRISC forms were collected during recruitment for the GGT DPP. Eligible individuals, at moderate to high risk of developing diabetes, were invited to undertake baseline tests, including anthropometric measurements performed by specially trained nurses. In addition to errors in calculating total risk scores, accuracy of self-reported data (height, weight, waist circumference (WC) and Body Mass Index (BMI)) from FINDRISCs was compared with baseline data, with impact on participation eligibility presented. Overall, calculation errors impacted on eligibility in 18 cases (2.1%). Of n = 279 GGT DPP participants with measured data, errors (total score calculation, BMI or WC) in self-report were found in n = 90 (32.3%). These errors were equally likely to result in under- or over-reported risk. Under-reporting was more common in those reporting lower risk scores (Spearman-rho = -0.226, p-value < 0.001). However, underestimation resulted in only 6% of individuals at high risk of diabetes being incorrectly categorised as moderate or low risk of diabetes. Overall FINDRISC was found to be an effective tool to screen and recruit participants at moderate to high risk of diabetes, accurately categorising levels of overweight and obesity using self-report data. The results could be generalisable to other diabetes prevention programs using screening tools which include self-reported levels of obesity.

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

NASA Astrophysics Data System (ADS)

Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

2018-06-01

Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

Early math and reading achievement are associated with the error positivity.

PubMed

Kim, Matthew H; Grammer, Jennie K; Marulis, Loren M; Carrasco, Melisa; Morrison, Frederick J; Gehring, William J

2016-12-01

Executive functioning (EF) and motivation are associated with academic achievement and error-related ERPs. The present study explores whether early academic skills predict variability in the error-related negativity (ERN) and error positivity (Pe). Data from 113 three- to seven-year-old children in a Go/No-Go task revealed that stronger early reading and math skills predicted a larger Pe. Closer examination revealed that this relation was quadratic and significant for children performing at or near grade level, but not significant for above-average achievers. Early academics did not predict the ERN. These findings suggest that the Pe - which reflects individual differences in motivational processes as well as attention - may be associated with early academic achievement. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Action Monitoring in boys with ADHD, their Nonaffected Siblings and Normal Controls: Evidence for an Endophenotype

PubMed Central

Albrecht, Bjoern; Brandeis, Daniel; Uebel, Henrik; Heinrich, Hartmut; Mueller, Ueli C.; Hasselhorn, Marcus; Steinhausen, Hans-Christoph; Rothenberger, Aribert; Banaschewski, Tobias

2008-01-01

Background Attention deficit/hyperactivity disorder is a very common and highly heritable child psychiatric disorder associated with dysfunctions in fronto-striatal networks that control attention and response organisation. Aim of this study was to investigate whether features of action monitoring related to dopaminergic functions represent endophenotypes which are brain functions on the pathway from genes and environmental risk factors to behaviour. Methods Action monitoring and error processing as indicated by behavioural and electrophysiological parameters during a flanker task were examined in boys with ADHD combined type according to DSM-IV (N=68), their nonaffected siblings (N=18) and healthy controls with no known family history of ADHD (N=22). Results Boys with ADHD displayed slower and more variable reaction-times. Error negativity (Ne) was smaller in boys with ADHD compared to healthy controls, while nonaffected siblings displayed intermediate amplitudes following a linear model predicted by genetic concordance. The three groups did not differ on error positivity (Pe). N2 amplitude enhancement due to conflict (incongruent flankers) was reduced in the ADHD group. Nonaffected siblings also displayed intermediate N2 enhancement. Conclusions Converging evidence from behavioural and ERP findings suggests that action monitoring and initial error processing, both related to dopaminergically modulated functions of anterior cingulate cortex, might be an endophenotype related to ADHD. PMID:18339358

Reward prediction error signal enhanced by striatum-amygdala interaction explains the acceleration of probabilistic reward learning by emotion.

PubMed

Watanabe, Noriya; Sakagami, Masamichi; Haruno, Masahiko

2013-03-06

Learning does not only depend on rationality, because real-life learning cannot be isolated from emotion or social factors. Therefore, it is intriguing to determine how emotion changes learning, and to identify which neural substrates underlie this interaction. Here, we show that the task-independent presentation of an emotional face before a reward-predicting cue increases the speed of cue-reward association learning in human subjects compared with trials in which a neutral face is presented. This phenomenon was attributable to an increase in the learning rate, which regulates reward prediction errors. Parallel to these behavioral findings, functional magnetic resonance imaging demonstrated that presentation of an emotional face enhanced reward prediction error (RPE) signal in the ventral striatum. In addition, we also found a functional link between this enhanced RPE signal and increased activity in the amygdala following presentation of an emotional face. Thus, this study revealed an acceleration of cue-reward association learning by emotion, and underscored a role of striatum-amygdala interactions in the modulation of the reward prediction errors by emotion.

Motivational state controls the prediction error in Pavlovian appetitive-aversive interactions.

PubMed

Laurent, Vincent; Balleine, Bernard W; Westbrook, R Frederick

2018-01-01

Contemporary theories of learning emphasize the role of a prediction error signal in driving learning, but the nature of this signal remains hotly debated. Here, we used Pavlovian conditioning in rats to investigate whether primary motivational and emotional states interact to control prediction error. We initially generated cues that positively or negatively predicted an appetitive food outcome. We then assessed how these cues modulated aversive conditioning when a novel cue was paired with a foot shock. We found that a positive predictor of food enhances, whereas a negative predictor of that same food impairs, aversive conditioning. Critically, we also showed that the enhancement produced by the positive predictor is removed by reducing the value of its associated food. In contrast, the impairment triggered by the negative predictor remains insensitive to devaluation of its associated food. These findings provide compelling evidence that the motivational value attributed to a predicted food outcome can directly control appetitive-aversive interactions and, therefore, that motivational processes can modulate emotional processes to generate the final error term on which subsequent learning is based. Copyright © 2017 Elsevier Inc. All rights reserved.

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning.

PubMed

Roemmich, Ryan T; Long, Andrew W; Bastian, Amy J

2016-10-24

In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here, we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual-feedback-dependent process that monitors learning and corrects residual errors but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change. Copyright © 2016 Elsevier Ltd. All rights reserved.

Practice increases procedural errors after task interruption.

PubMed

Altmann, Erik M; Hambrick, David Z

2017-05-01

Positive effects of practice are ubiquitous in human performance, but a finding from memory research suggests that negative effects are possible also. The finding is that memory for items on a list depends on the time interval between item presentations. This finding predicts a negative effect of practice on procedural performance under conditions of task interruption. As steps of a procedure are performed more quickly, memory for past performance should become less accurate, increasing the rate of skipped or repeated steps after an interruption. We found this effect, with practice generally improving speed and accuracy, but impairing accuracy after interruptions. The results show that positive effects of practice can interact with architectural constraints on episodic memory to have negative effects on performance. In practical terms, the results suggest that practice can be a risk factor for procedural errors in task environments with a high incidence of task interruption. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with penalized regression splines

PubMed Central

Bergen, Silas; Sheppard, Lianne; Kaufman, Joel D.; Szpiro, Adam A.

2016-01-01

Summary Air pollution epidemiology studies are trending towards a multi-pollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted using observed exposures at misaligned monitoring locations. This induces measurement error, which can bias the estimated health effects and affect standard error estimates. We characterize this measurement error and develop an analytic bias correction when using penalized regression splines to predict exposure. Our simulations show bias from multi-pollutant measurement error can be severe, and in opposite directions or simultaneously positive or negative. Our analytic bias correction combined with a non-parametric bootstrap yields accurate coverage of 95% confidence intervals. We apply our methodology to analyze the association of systolic blood pressure with PM2.5 and NO2 in the NIEHS Sister Study. We find that NO2 confounds the association of systolic blood pressure with PM2.5 and vice versa. Elevated systolic blood pressure was significantly associated with increased PM2.5 and decreased NO2. Correcting for measurement error bias strengthened these associations and widened 95% confidence intervals. PMID:27789915

Optical Coherence Tomography–Based Corneal Power Measurement and Intraocular Lens Power Calculation Following Laser Vision Correction (An American Ophthalmological Society Thesis)

PubMed Central

Huang, David; Tang, Maolong; Wang, Li; Zhang, Xinbo; Armour, Rebecca L.; Gattey, Devin M.; Lombardi, Lorinna H.; Koch, Douglas D.

2013-01-01

Purpose: To use optical coherence tomography (OCT) to measure corneal power and improve the selection of intraocular lens (IOL) power in cataract surgeries after laser vision correction. Methods: Patients with previous myopic laser vision corrections were enrolled in this prospective study from two eye centers. Corneal thickness and power were measured by Fourier-domain OCT. Axial length, anterior chamber depth, and automated keratometry were measured by a partial coherence interferometer. An OCT-based IOL formula was developed. The mean absolute error of the OCT-based formula in predicting postoperative refraction was compared to two regression-based IOL formulae for eyes with previous laser vision correction. Results: Forty-six eyes of 46 patients all had uncomplicated cataract surgery with monofocal IOL implantation. The mean arithmetic prediction error of postoperative refraction was 0.05 ± 0.65 diopter (D) for the OCT formula, 0.14 ± 0.83 D for the Haigis-L formula, and 0.24 ± 0.82 D for the no-history Shammas-PL formula. The mean absolute error was 0.50 D for OCT compared to a mean absolute error of 0.67 D for Haigis-L and 0.67 D for Shammas-PL. The adjusted mean absolute error (average prediction error removed) was 0.49 D for OCT, 0.65 D for Haigis-L (P=.031), and 0.62 D for Shammas-PL (P=.044). For OCT, 61% of the eyes were within 0.5 D of prediction error, whereas 46% were within 0.5 D for both Haigis-L and Shammas-PL (P=.034). Conclusions: The predictive accuracy of OCT-based IOL power calculation was better than Haigis-L and Shammas-PL formulas in eyes after laser vision correction. PMID:24167323

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

NASA Astrophysics Data System (ADS)

David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

2017-04-01

This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

NASA Astrophysics Data System (ADS)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

2017-03-01

Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Predicting kidney disease progression in patients with acute kidney injury after cardiac surgery.

PubMed

Mizuguchi, K Annette; Huang, Chuan-Chin; Shempp, Ian; Wang, Justin; Shekar, Prem; Frendl, Gyorgy

2018-06-01

The study objective was to identify patients who are likely to develop progressive kidney dysfunction (acute kidney disease) before their hospital discharge after cardiac surgery, allowing targeted monitoring of kidney function in this at-risk group with periodic serum creatinine measurements. Risks of progression to acute kidney disease (a state in between acute kidney injury and chronic kidney disease) were modeled from acute kidney injury stages (Kidney Disease: Improving Global Outcomes) in patients undergoing cardiac surgery. A modified Poisson regression with robust error variance was used to evaluate the association between acute kidney injury stages and the development of acute kidney disease (defined as doubling of creatinine 2-4 weeks after surgery) in this observational study. Acute kidney disease occurred in 4.4% of patients with no preexisting kidney disease and 4.8% of patients with preexisting chronic kidney disease. Acute kidney injury predicted development of acute kidney disease in a graded manner in which higher stages of acute kidney injury predicted higher relative risk of progressive kidney disease (area under the receiver operator characteristic curve = 0.82). This correlation persisted regardless of baseline kidney function (P < .001). Of note, development of acute kidney disease was associated with higher mortality and need for renal replacement therapy. The degree of acute kidney injury can identify patients who will have a higher risk of progression to acute kidney disease. These patients may benefit from close follow-up of renal function because they are at risk of progressing to chronic kidney disease or end-stage renal disease. Copyright © 2018 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

The Role of Cognitive Factors in Predicting Balance and Fall Risk in a Neuro-Rehabilitation Setting.

PubMed

Saverino, A; Waller, D; Rantell, K; Parry, R; Moriarty, A; Playford, E D

2016-01-01

There is a consistent body of evidence supporting the role of cognitive functions, particularly executive function, in the elderly and in neurological conditions which become more frequent with ageing. The aim of our study was to assess the role of different domains of cognitive functions to predict balance and fall risk in a sample of adults with various neurological conditions in a rehabilitation setting. This was a prospective, cohort study conducted in a single centre in the UK. 114 participants consecutively admitted to a Neuro-Rehabilitation Unit were prospectively assessed for fall accidents. Baseline assessment included a measure of balance (Berg Balance Scale) and a battery of standard cognitive tests measuring executive function, speed of information processing, verbal and visual memory, visual perception and intellectual function. The outcomes of interest were the risk of becoming a faller, balance and fall rate. Two tests of executive function were significantly associated with fall risk, the Stroop Colour Word Test (IRR 1.01, 95% CI 1.00-1.03) and the number of errors on part B of the Trail Making Test (IRR 1.23, 95% CI 1.03-1.49). Composite scores of executive function, speed of information processing and visual memory domains resulted in 2 to 3 times increased likelihood of having better balance (OR 2.74 95% CI 1.08 to 6.94, OR 2.72 95% CI 1.16 to 6.36 and OR 2.44 95% CI 1.11 to 5.35 respectively). Our results show that specific subcomponents of executive functions are able to predict fall risk, while a more global cognitive dysfunction is associated with poorer balance.

The Role of Cognitive Factors in Predicting Balance and Fall Risk in a Neuro-Rehabilitation Setting

PubMed Central

Saverino, A.; Waller, D.; Rantell, K.; Parry, R.; Moriarty, A.; Playford, E. D.

2016-01-01

Introduction There is a consistent body of evidence supporting the role of cognitive functions, particularly executive function, in the elderly and in neurological conditions which become more frequent with ageing. The aim of our study was to assess the role of different domains of cognitive functions to predict balance and fall risk in a sample of adults with various neurological conditions in a rehabilitation setting. Methods This was a prospective, cohort study conducted in a single centre in the UK. 114 participants consecutively admitted to a Neuro-Rehabilitation Unit were prospectively assessed for fall accidents. Baseline assessment included a measure of balance (Berg Balance Scale) and a battery of standard cognitive tests measuring executive function, speed of information processing, verbal and visual memory, visual perception and intellectual function. The outcomes of interest were the risk of becoming a faller, balance and fall rate. Results Two tests of executive function were significantly associated with fall risk, the Stroop Colour Word Test (IRR 1.01, 95% CI 1.00–1.03) and the number of errors on part B of the Trail Making Test (IRR 1.23, 95% CI 1.03–1.49). Composite scores of executive function, speed of information processing and visual memory domains resulted in 2 to 3 times increased likelihood of having better balance (OR 2.74 95% CI 1.08 to 6.94, OR 2.72 95% CI 1.16 to 6.36 and OR 2.44 95% CI 1.11 to 5.35 respectively). Conclusions Our results show that specific subcomponents of executive functions are able to predict fall risk, while a more global cognitive dysfunction is associated with poorer balance. PMID:27115880

Multisite Parent-Centered Risk Assessment to Reduce Pediatric Oral Chemotherapy Errors

PubMed Central

Walsh, Kathleen E.; Mazor, Kathleen M.; Roblin, Douglas; Biggins, Colleen; Wagner, Joann L.; Houlahan, Kathleen; Li, Justin W.; Keuker, Christopher; Wasilewski-Masker, Karen; Donovan, Jennifer; Kanaan, Abir; Weingart, Saul N.

2013-01-01

Purpose: Observational studies describe high rates of errors in home oral chemotherapy use in children. In hospitals, proactive risk assessment methods help front-line health care workers develop error prevention strategies. Our objective was to engage parents of children with cancer in a multisite study using proactive risk assessment methods to identify how errors occur at home and propose risk reduction strategies. Methods: We recruited parents from three outpatient pediatric oncology clinics in the northeast and southeast United States to participate in failure mode and effects analyses (FMEA). An FMEA is a systematic team-based proactive risk assessment approach in understanding ways a process can fail and develop prevention strategies. Steps included diagram the process, brainstorm and prioritize failure modes (places where things go wrong), and propose risk reduction strategies. We focused on home oral chemotherapy administration after a change in dose because prior studies identified this area as high risk. Results: Parent teams consisted of four parents at two of the sites and 10 at the third. Parents developed a 13-step process map, with two to 19 failure modes per step. The highest priority failure modes included miscommunication when receiving instructions from the clinician (caused by conflicting instructions or parent lapses) and unsafe chemotherapy handling at home. Recommended risk assessment strategies included novel uses of technology to improve parent access to information, clinicians, and other parents while at home. Conclusion: Parents of pediatric oncology patients readily participated in a proactive risk assessment method, identifying processes that pose a risk for medication errors involving home oral chemotherapy. PMID:23633976

Machine learning approaches for estimation of prediction interval for the model output.

PubMed

Shrestha, Durga L; Solomatine, Dimitri P

2006-03-01

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

A log-sinh transformation for data normalization and variance stabilization

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Shrestha, D. L.; Robertson, D. E.; Pokhrel, P.

2012-05-01

When quantifying model prediction uncertainty, it is statistically convenient to represent model errors that are normally distributed with a constant variance. The Box-Cox transformation is the most widely used technique to normalize data and stabilize variance, but it is not without limitations. In this paper, a log-sinh transformation is derived based on a pattern of errors commonly seen in hydrological model predictions. It is suited to applications where prediction variables are positively skewed and the spread of errors is seen to first increase rapidly, then slowly, and eventually approach a constant as the prediction variable becomes greater. The log-sinh transformation is applied in two case studies, and the results are compared with one- and two-parameter Box-Cox transformations.

Pattern placement errors: application of in-situ interferometer-determined Zernike coefficients in determining printed image deviations

NASA Astrophysics Data System (ADS)

Roberts, William R.; Gould, Christopher J.; Smith, Adlai H.; Rebitz, Ken

2000-08-01

Several ideas have recently been presented which attempt to measure and predict lens aberrations for new low k1 imaging systems. Abbreviated sets of Zernike coefficients have been produced and used to predict Across Chip Linewidth Variation. Empirical use of the wavefront aberrations can now be used in commercially available lithography simulators to predict pattern distortion and placement errors. Measurement and Determination of Zernike coefficients has been a significant effort of many. However the use of this data has generally been limited to matching lenses or picking best fit lense pairs. We will use wavefront aberration data collected using the Litel InspecStep in-situ Interferometer as input data for Prolith/3D to model and predict pattern placement errors and intrafield overlay variation. Experiment data will be collected and compared to the simulated predictions.

Y-balance test: a reliability study involving multiple raters.

PubMed

Shaffer, Scott W; Teyhen, Deydre S; Lorenson, Chelsea L; Warren, Rick L; Koreerat, Christina M; Straseske, Crystal A; Childs, John D

2013-11-01

The Y-balance test (YBT) is one of the few field expedient tests that have shown predictive validity for injury risk in an athletic population. However, analysis of the YBT in a heterogeneous population of active adults (e.g., military, specific occupations) involving multiple raters with limited experience in a mass screening setting is lacking. The primary purpose of this study was to determine interrater test-retest reliability of the YBT in a military setting using multiple raters. Sixty-four service members (53 males, 11 females) actively conducting military training volunteered to participate. Interrater test-retest reliability of the maximal reach had intraclass correlation coefficients (2,1) of 0.80 to 0.85 with a standard error of measurement ranging from 3.1 to 4.2 cm for the 3 reach directions (anterior, posteromedial, and posterolateral). Interrater test-retest reliability of the average reach of 3 trails had an intraclass correlation coefficients (2,3) range of 0.85 to 0.93 with an associated standard error of measurement ranging from 2.0 to 3.5cm. The YBT showed good interrater test-retest reliability with an acceptable level of measurement error among multiple raters screening active duty service members. In addition, 31.3% (n = 20 of 64) of participants exhibited an anterior reach asymmetry of >4cm, suggesting impaired balance symmetry and potentially increased risk for injury. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.

USGS Publications Warehouse

Cooley, Richard L.; Vecchia, Aldo V.

1987-01-01

A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Checefsky, Walter A.; Abidin, Anas Z.; Tsai, Halley; Wang, Xixi; Hobbs, Susan K.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2015-03-01

While the proximal femur is preferred for measuring bone mineral density (BMD) in fracture risk estimation, the introduction of volumetric quantitative computed tomography has revealed stronger associations between BMD and spinal fracture status. In this study, we propose to capture properties of trabecular bone structure in spinal vertebrae with advanced second-order statistical features for purposes of fracture risk assessment. For this purpose, axial multi-detector CT (MDCT) images were acquired from 28 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. A semi-automated method was used to annotate the trabecular compartment in the central vertebral slice with a circular region of interest (ROI) to exclude cortical bone; pixels within were converted to values indicative of BMD. Six second-order statistical features derived from gray-level co-occurrence matrices (GLCM) and the mean BMD within the ROI were then extracted and used in conjunction with a generalized radial basis functions (GRBF) neural network to predict the failure load of the specimens; true failure load was measured through biomechanical testing. Prediction performance was evaluated with a root-mean-square error (RMSE) metric. The best prediction performance was observed with GLCM feature `correlation' (RMSE = 1.02 ± 0.18), which significantly outperformed all other GLCM features (p < 0.01). GLCM feature correlation also significantly outperformed MDCTmeasured mean BMD (RMSE = 1.11 ± 0.17) (p< 10-4). These results suggest that biomechanical strength prediction in spinal vertebrae can be significantly improved through characterization of trabecular bone structure with GLCM-derived texture features.

Assessing frost damages using dynamic models in walnut trees: exposure rather than vulnerability controls frost risks.

PubMed

Guillaume, Charrier; Isabelle, Chuine; Marc, Bonhomme; Thierry, Améglio

2018-05-01

Frost damages develop when exposure overtakes frost vulnerability. Frost risk assessment therefore needs dynamic simulation of frost hardiness using temperature and photoperiod in interaction with developmental stage. Two models, including or not the effect of photoperiod, were calibrated using five years of frost hardiness monitoring (2007-2012), in two locations (low and high elevation) for three walnut genotypes with contrasted phenology and maximum hardiness (Juglans regia cv Franquette, J. regia × nigra 'Early' and 'Late'). The photothermal model predicted more accurate values for all genotypes (efficiency = 0.879; Root Mean Standard Error Predicted (RMSEP) = 2.55 °C) than the thermal model (efficiency = 0.801; RMSEP = 3.24 °C). Predicted frost damages were strongly correlated to minimum temperature of the freezing events (ρ = -0.983) rather than actual frost hardiness (ρ = -0.515), or ratio of phenological stage completion (ρ = 0.336). Higher frost risks are consequently predicted during winter, at high elevation, whereas spring is only risky at low elevation in early genotypes exhibiting faster dehardening rate. However, early frost damages, although of lower value, may negatively affect fruit production the subsequent year (R 2  = 0.381, P = 0.057). These results highlight the interacting pattern between frost exposure and vulnerability at different scales and the necessity of intra-organ studies to understand the time course of frost vulnerability in flower buds along the winter. © 2017 John Wiley & Sons Ltd.

The cerebellum for jocks and nerds alike.

PubMed

Popa, Laurentiu S; Hewitt, Angela L; Ebner, Timothy J

2014-01-01

Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains.

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI.

PubMed

Colas, Jaron T; Pauli, Wolfgang M; Larsen, Tobias; Tyszka, J Michael; O'Doherty, John P

2017-10-01

Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI

PubMed Central

Pauli, Wolfgang M.; Larsen, Tobias; Tyszka, J. Michael; O’Doherty, John P.

2017-01-01

Prediction-error signals consistent with formal models of “reinforcement learning” (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models—namely, “actor/critic” models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning. PMID:29049406

The cerebellum for jocks and nerds alike

PubMed Central

Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.

2014-01-01

Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains. PMID:24987338

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

Xu, Yadong; Serre, Marc L.; Reyes, Jeanette M.; Vizuete, William

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

Effect of tumor amplitude and frequency on 4D modeling of Vero4DRT system.

PubMed

Miura, Hideharu; Ozawa, Shuichi; Hayata, Masahiro; Tsuda, Shintaro; Yamada, Kiyoshi; Nagata, Yasushi

2017-01-01

An important issue in indirect dynamic tumor tracking with the Vero4DRT system is the accuracy of the model predictions of the internal target position based on surrogate infrared (IR) marker measurement. We investigated the predictive uncertainty of 4D modeling using an external IR marker, focusing on the effect of the target and surrogate amplitudes and periods. A programmable respiratory motion table was used to simulate breathing induced organ motion. Sinusoidal motion sequences were produced by a dynamic phantom with different amplitudes and periods. To investigate the 4D modeling error, the following amplitudes (peak-to-peak: 10-40 mm) and periods (2-8 s) were considered. The 95th percentile 4D modeling error (4D- E 95% ) between the detected and predicted target position ( μ  + 2SD) was calculated to investigate the 4D modeling error. 4D- E 95% was linearly related to the target motion amplitude with a coefficient of determination R 2  = 0.99 and ranged from 0.21 to 0.88 mm. The 4D modeling error ranged from 1.49 to 0.14 mm and gradually decreased with increasing target motion period. We analyzed the predictive error in 4D modeling and the error due to the amplitude and period of target. 4D modeling error substantially increased with increasing amplitude and decreasing period of the target motion.

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di, Sheng; Berrocal, Eduardo; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore » minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.« less

Mesoscale Predictability and Error Growth in Short Range Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Gingrich, Mark

Although it was originally suggested that small-scale, unresolved errors corrupt forecasts at all scales through an inverse error cascade, some authors have proposed that those mesoscale circulations resulting from stationary forcing on the larger scale may inherit the predictability of the large-scale motions. Further, the relative contributions of large- and small-scale uncertainties in producing error growth in the mesoscales remain largely unknown. Here, 100 member ensemble forecasts are initialized from an ensemble Kalman filter (EnKF) to simulate two winter storms impacting the East Coast of the United States in 2010. Four verification metrics are considered: the local snow water equivalence, total liquid water, and 850 hPa temperatures representing mesoscale features; and the sea level pressure field representing a synoptic feature. It is found that while the predictability of the mesoscale features can be tied to the synoptic forecast, significant uncertainty existed on the synoptic scale at lead times as short as 18 hours. Therefore, mesoscale details remained uncertain in both storms due to uncertainties at the large scale. Additionally, the ensemble perturbation kinetic energy did not show an appreciable upscale propagation of error for either case. Instead, the initial condition perturbations from the cycling EnKF were maximized at large scales and immediately amplified at all scales without requiring initial upscale propagation. This suggests that relatively small errors in the synoptic-scale initialization may have more importance in limiting predictability than errors in the unresolved, small-scale initial conditions.

Assessing the predictive value of the American Board of Family Practice In-training Examination.

PubMed

Replogle, William H; Johnson, William D

2004-03-01

The American Board of Family Practice In-training Examination (ABFP ITE) is a cognitive examination similar in content to the ABFP Certification Examination (CE). The ABFP ITE is widely used in family medicine residency programs. It was originally developed and intended to be used for assessment of groups of residents. Despite lack of empirical support, however, some residency programs are using ABFP ITE scores as individual resident performance indicators. This study's objective was to estimate the positive predictive value of the ABFP ITE for identifying residents at risk for poor performance on the ABFP CE or a subsequent ABFP ITE. We used a normal distribution model for correlated test scores and Monte Carlo simulation to investigate the effect of test reliability (measurement errors) on the positive predictive value of the ABFP ITE. The positive predictive value of the composite score was .72. The positive predictive value of the eight specialty subscales ranged from .26 to .57. Only the composite score of the ABFP ITE has acceptable positive predictive value to be used as part of a comprehension resident evaluation system. The ABFP ITE specialty subscales do not have sufficient positive predictive value or reliability to warrant use as performance indicators.

Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Voisin, Sophie; Pinto, Frank M; Morin-Ducote, Garnetta

2013-01-01

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADsmore » images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.« less

Achievable accuracy of hip screw holding power estimation by insertion torque measurement.

PubMed

Erani, Paolo; Baleani, Massimiliano

2018-02-01

To ensure stability of proximal femoral fractures, the hip screw must firmly engage into the femoral head. Some studies suggested that screw holding power into trabecular bone could be evaluated, intraoperatively, through measurement of screw insertion torque. However, those studies used synthetic bone, instead of trabecular bone, as host material or they did not evaluate accuracy of predictions. We determined prediction accuracy, also assessing the impact of screw design and host material. We measured, under highly-repeatable experimental conditions, disregarding clinical procedure complexities, insertion torque and pullout strength of four screw designs, both in 120 synthetic and 80 trabecular bone specimens of variable density. For both host materials, we calculated the root-mean-square error and the mean-absolute-percentage error of predictions based on the best fitting model of torque-pullout data, in both single-screw and merged dataset. Predictions based on screw-specific regression models were the most accurate. Host material impacts on prediction accuracy: the replacement of synthetic with trabecular bone decreased both root-mean-square errors, from 0.54 ÷ 0.76 kN to 0.21 ÷ 0.40 kN, and mean-absolute-percentage errors, from 14 ÷ 21% to 10 ÷ 12%. However, holding power predicted on low insertion torque remained inaccurate, with errors up to 40% for torques below 1 Nm. In poor-quality trabecular bone, tissue inhomogeneities likely affect pullout strength and insertion torque to different extents, limiting the predictive power of the latter. This bias decreases when the screw engages good-quality bone. Under this condition, predictions become more accurate although this result must be confirmed by close in-vitro simulation of the clinical procedure. Copyright © 2018 Elsevier Ltd. All rights reserved.

Judging Statistical Models of Individual Decision Making under Risk Using In- and Out-of-Sample Criteria

PubMed Central

Drichoutis, Andreas C.; Lusk, Jayson L.

2014-01-01

Despite the fact that conceptual models of individual decision making under risk are deterministic, attempts to econometrically estimate risk preferences require some assumption about the stochastic nature of choice. Unfortunately, the consequences of making different assumptions are, at present, unclear. In this paper, we compare three popular error specifications (Fechner, contextual utility, and Luce error) for three different preference functionals (expected utility, rank-dependent utility, and a mixture of those two) using in- and out-of-sample selection criteria. We find drastically different inferences about structural risk preferences across the competing functionals and error specifications. Expected utility theory is least affected by the selection of the error specification. A mixture model combining the two conceptual models assuming contextual utility provides the best fit of the data both in- and out-of-sample. PMID:25029467

Judging statistical models of individual decision making under risk using in- and out-of-sample criteria.

PubMed

Drichoutis, Andreas C; Lusk, Jayson L

2014-01-01

Despite the fact that conceptual models of individual decision making under risk are deterministic, attempts to econometrically estimate risk preferences require some assumption about the stochastic nature of choice. Unfortunately, the consequences of making different assumptions are, at present, unclear. In this paper, we compare three popular error specifications (Fechner, contextual utility, and Luce error) for three different preference functionals (expected utility, rank-dependent utility, and a mixture of those two) using in- and out-of-sample selection criteria. We find drastically different inferences about structural risk preferences across the competing functionals and error specifications. Expected utility theory is least affected by the selection of the error specification. A mixture model combining the two conceptual models assuming contextual utility provides the best fit of the data both in- and out-of-sample.

Speech Errors across the Lifespan

ERIC Educational Resources Information Center

Vousden, Janet I.; Maylor, Elizabeth A.

2006-01-01

Dell, Burger, and Svec (1997) proposed that the proportion of speech errors classified as anticipations (e.g., "moot and mouth") can be predicted solely from the overall error rate, such that the greater the error rate, the lower the anticipatory proportion (AP) of errors. We report a study examining whether this effect applies to changes in error…

Social cognition and revictimization risk.

PubMed

DePrince, Anne P

2005-01-01

The ability to accurately detect violations in social contracts likely helps people to avoid or to withdraw from relationships in which they are at risk of being cheated or harmed. Betrayal trauma theory argues that detecting violations of social contracts may be counter-productive to survival under certain conditions, such as when a victim is dependent on a perpetrator. When dependent on a perpetrator (as in the case of child abuse perpetrated by a caregiver), the victim may be better able to preserve the necessary attachment with the caregiver by remaining unaware of the abuse. Thus, the victim may develop a compromised capacity to detect violations of social contracts in the caregiving relationship. Over time, the victim may develop more generalized problems detecting violations in social exchange rules; in turn, generalized problems in detecting violations of social contracts may increase risk for later victimization. Participants in the current study were asked to detect violations in three types of conditional (if-then) rules: abstract, social contract (rules involving a social exchange), and precautionary (rules involving safety). Young adults who reported experiences of revictimization made more errors on social contract and precautionary problems than a no revictimization group; group performance did not differ for abstract problems, suggesting these findings are not explained by general deficits in conditional reasoning. Pathological dissociation significantly predicted errors on social contract and precautionary problems.

Landscape epidemiology and machine learning: A geospatial approach to modeling West Nile virus risk in the United States

NASA Astrophysics Data System (ADS)

Young, Sean Gregory

The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is a sub-discipline of artificial intelligence that can be used to create predictive models from large and complex datasets. West Nile virus (WNV) is a relatively new infectious disease in the United States, and has a fairly well-understood transmission cycle that is believed to be highly dependent on environmental conditions. This study takes a geospatial approach to the study of WNV risk, using both landscape epidemiology and machine learning techniques. A combination of remotely sensed and in situ variables are used to predict WNV incidence with a correlation coefficient as high as 0.86. A novel method of mitigating the small numbers problem is also tested and ultimately discarded. Finally a consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US.

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

PubMed Central

Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.

2014-01-01

Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

PubMed

Lewnard, Joseph A; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R; Glesby, Marshall J; Ko, Albert I; Carvalho, Edgar M; Schriefer, Albert; Weinberger, Daniel M

2014-10-01

Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

Estimating gestational age at birth from fundal height and additional anthropometrics: a prospective cohort study.

PubMed

Pugh, S J; Ortega-Villa, A M; Grobman, W; Newman, R B; Owen, J; Wing, D A; Albert, P S; Grantz, K L

2018-02-23

Accurate assessment of gestational age (GA) is critical to paediatric care, but is limited in developing countries without access to ultrasound. Our objectives were to assess the accuracy of prediction of GA at birth and preterm birth classification using routinely collected anthropometry measures. Prospective cohort study. United States. A total of 2334 non-obese and 468 obese pregnant women. Enrolment GA was determined based on last menstrual period, confirmed by first-trimester ultrasound. Maternal anthropometry and fundal height (FH) were measured by a standardised protocol at study visits; FH alone was additionally abstracted from medical charts. Neonatal anthropometry measurements were obtained at birth. To estimate GA at delivery, we developed three predictor models using longitudinal FH alone and with maternal and neonatal anthropometry. For all predictors, we repeatedly sampled observations to construct training (60%) and test (40%) sets. Linear mixed models incorporated longitudinal maternal anthropometry and a shared parameter model incorporated neonatal anthropometry. We assessed models' accuracy under varied scenarios. Estimated GA at delivery. Prediction error for various combinations of anthropometric measures ranged between 13.9 and 14.9 days. Longitudinal FH alone predicted GA within 14.9 days with relatively stable prediction errors across individual race/ethnicities [whites (13.9 days), blacks (15.1 days), Hispanics (15.5 days) and Asians (13.1 days)], and correctly identified 75% of preterm births. The model was robust to additional scenarios. In low-risk, non-obese women, longitudinal FH measures alone can provide a reasonably accurate assessment of GA when ultrasound measures are not available. Longitudinal fundal height alone predicts gestational age at birth when ultrasound measures are unavailable. © 2018 Royal College of Obstetricians and Gynaecologists.

Evidence Report: Risk of Performance Errors Due to Training Deficiencies

NASA Technical Reports Server (NTRS)

Barshi, Immanuel

2012-01-01

The Risk of Performance Errors Due to Training Deficiencies is identified by the National Aeronautics and Space Administration (NASA) Human Research Program (HRP) as a recognized risk to human health and performance in space. The HRP Program Requirements Document (PRD) defines these risks. This Evidence Report provides a summary of the evidence that has been used to identify and characterize this risk. Given that training content, timing, intervals, and delivery methods must support crew task performance, and given that training paradigms will be different for long-duration missions with increased crew autonomy, there is a risk that operators will lack the skills or knowledge necessary to complete critical tasks, resulting in flight and ground crew errors and inefficiencies, failed mission and program objectives, and an increase in crew injuries.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829