Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval

PubMed Central

Arnould, Valérie M. R.; Reding, Romain; Bormann, Jeanne; Gengler, Nicolas; Soyeurt, Hélène

2015-01-01

Simple Summary Reducing the frequency of milk recording decreases the costs of official milk recording. However, this approach can negatively affect the accuracy of predicting daily yields. Equations to predict daily yield from morning or evening data were developed in this study for fatty milk components from traits recorded easily by milk recording organizations. The correlation values ranged from 96.4% to 97.6% (96.9% to 98.3%) when the daily yields were estimated from the morning (evening) milkings. The simplicity of the proposed models which do not include the milking interval should facilitate their use by breeding and milk recording organizations. Abstract Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a MI effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations. PMID:26479379

Prediction of visual evoked potentials at any surface location from a set of three recording electrodes.

PubMed

Mazinani, Babac A E; Waberski, Till D; van Ooyen, Andre; Walter, Peter

2008-05-01

Purpose of this study was to introduce a mathematical model which allows the calculation of a source dipole as the origin of the evoked activity based on the data of three simultaneously recorded VEPs from different locations at the scalp surface to predict field potentials at any neighboring location and to validate this model by comparison with actual recordings. In 10 healthy subjects (25-38, mean 29 years) continuous VEPs were recorded via 96 channels. On the base of the recordings at the positions POz', O1' and O2', a source dipole vector was calculated for each time point of the recordings and VEP responses were back projected for any of the 96 electrode positions. Differences between the calculated and the actually recorded responses were quantified by coefficients of variation (CV). The prediction precision and response size depended on the distance between the electrode of the predicted response and the recording electrodes. After compensating this relationship using a polynomial function, the CV of the mean difference between calculated and recorded responses of the 10 subjects was 2.8 +/- 1.2%. In conclusion, the "Mini-Brainmapping" model can provide precise topographical information with minimal additional recording efforts with good reliability. The implementation of this method in a routine diagnostic setting as an "easy-to-do" procedure would allow to examine a large number of patients and normal subjects in a short time, and thus, a solid data base could be created to correlate well defined pathologies with topographical VEP changes.

Genomic prediction of reproduction traits for Merino sheep.

PubMed

Bolormaa, S; Brown, D J; Swan, A A; van der Werf, J H J; Hayes, B J; Daetwyler, H D

2017-06-01

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording. © 2017 Stichting International Foundation for Animal Genetics.

Validation of diabetes mellitus and hypertension diagnosis in computerized medical records in primary health care

PubMed Central

2011-01-01

Background Computerized Clinical Records, which are incorporated in primary health care practice, have great potential for research. In order to use this information, data quality and reliability must be assessed to prevent compromising the validity of the results. The aim of this study is to validate the diagnosis of hypertension and diabetes mellitus in the computerized clinical records of primary health care, taking the diagnosis criteria established in the most prominently used clinical guidelines as the gold standard against which what measure the sensitivity, specificity, and determine the predictive values. The gold standard for diabetes mellitus was the diagnostic criteria established in 2003 American Diabetes Association Consensus Statement for diabetic subjects. The gold standard for hypertension was the diagnostic criteria established in the Joint National Committee published in 2003. Methods A cross-sectional multicentre validation study of diabetes mellitus and hypertension diagnoses in computerized clinical records of primary health care was carried out. Diagnostic criteria from the most prominently clinical practice guidelines were considered for standard reference. Sensitivity, specificity, positive and negative predictive values, and global agreement (with kappa index), were calculated. Results were shown overall and stratified by sex and age groups. Results The agreement for diabetes mellitus with the reference standard as determined by the guideline was almost perfect (κ = 0.990), with a sensitivity of 99.53%, a specificity of 99.49%, a positive predictive value of 91.23% and a negative predictive value of 99.98%. Hypertension diagnosis showed substantial agreement with the reference standard as determined by the guideline (κ = 0.778), the sensitivity was 85.22%, the specificity 96.95%, the positive predictive value 85.24%, and the negative predictive value was 96.95%. Sensitivity results were worse in patients who also had diabetes and in those aged 70 years or over. Conclusions Our results substantiate the validity of using diagnoses of diabetes and hypertension found within the computerized clinical records for epidemiologic studies. PMID:22035202

External validation and comparison of three prediction tools for risk of osteoporotic fractures using data from population based electronic health records: retrospective cohort study

PubMed Central

Cohen-Stavi, Chandra; Leventer-Roberts, Maya; Balicer, Ran D

2017-01-01

Objective To directly compare the performance and externally validate the three most studied prediction tools for osteoporotic fractures—QFracture, FRAX, and Garvan—using data from electronic health records. Design Retrospective cohort study. Setting Payer provider healthcare organisation in Israel. Participants 1 054 815 members aged 50 to 90 years for comparison between tools and cohorts of different age ranges, corresponding to those in each tools’ development study, for tool specific external validation. Main outcome measure First diagnosis of a major osteoporotic fracture (for QFracture and FRAX tools) and hip fractures (for all three tools) recorded in electronic health records from 2010 to 2014. Observed fracture rates were compared to probabilities predicted retrospectively as of 2010. Results The observed five year hip fracture rate was 2.7% and the rate for major osteoporotic fractures was 7.7%. The areas under the receiver operating curve (AUC) for hip fracture prediction were 82.7% for QFracture, 81.5% for FRAX, and 77.8% for Garvan. For major osteoporotic fractures, AUCs were 71.2% for QFracture and 71.4% for FRAX. All the tools underestimated the fracture risk, but the average observed to predicted ratios and the calibration slopes of FRAX were closest to 1. Tool specific validation analyses yielded hip fracture prediction AUCs of 88.0% for QFracture (among those aged 30-100 years), 81.5% for FRAX (50-90 years), and 71.2% for Garvan (60-95 years). Conclusions Both QFracture and FRAX had high discriminatory power for hip fracture prediction, with QFracture performing slightly better. This performance gap was more pronounced in previous studies, likely because of broader age inclusion criteria for QFracture validations. The simpler FRAX performed almost as well as QFracture for hip fracture prediction, and may have advantages if some of the input data required for QFracture are not available. However, both tools require calibration before implementation. PMID:28104610

Technology-assisted title and abstract screening for systematic reviews: a retrospective evaluation of the Abstrackr machine learning tool.

PubMed

Gates, Allison; Johnson, Cydney; Hartling, Lisa

2018-03-12

Machine learning tools can expedite systematic review (SR) processes by semi-automating citation screening. Abstrackr semi-automates citation screening by predicting relevant records. We evaluated its performance for four screening projects. We used a convenience sample of screening projects completed at the Alberta Research Centre for Health Evidence, Edmonton, Canada: three SRs and one descriptive analysis for which we had used SR screening methods. The projects were heterogeneous with respect to search yield (median 9328; range 5243 to 47,385 records; interquartile range (IQR) 15,688 records), topic (Antipsychotics, Bronchiolitis, Diabetes, Child Health SRs), and screening complexity. We uploaded the records to Abstrackr and screened until it made predictions about the relevance of the remaining records. Across three trials for each project, we compared the predictions to human reviewer decisions and calculated the sensitivity, specificity, precision, false negative rate, proportion missed, and workload savings. Abstrackr's sensitivity was > 0.75 for all projects and the mean specificity ranged from 0.69 to 0.90 with the exception of Child Health SRs, for which it was 0.19. The precision (proportion of records correctly predicted as relevant) varied by screening task (median 26.6%; range 14.8 to 64.7%; IQR 29.7%). The median false negative rate (proportion of records incorrectly predicted as irrelevant) was 12.6% (range 3.5 to 21.2%; IQR 12.3%). The workload savings were often large (median 67.2%, range 9.5 to 88.4%; IQR 23.9%). The proportion missed (proportion of records predicted as irrelevant that were included in the final report, out of the total number predicted as irrelevant) was 0.1% for all SRs and 6.4% for the descriptive analysis. This equated to 4.2% (range 0 to 12.2%; IQR 7.8%) of the records in the final reports. Abstrackr's reliability and the workload savings varied by screening task. Workload savings came at the expense of potentially missing relevant records. How this might affect the results and conclusions of SRs needs to be evaluated. Studies evaluating Abstrackr as the second reviewer in a pair would be of interest to determine if concerns for reliability would diminish. Further evaluations of Abstrackr's performance and usability will inform its refinement and practical utility.

Psychoacoustic Analysis of Synthesized Jet Noise

NASA Technical Reports Server (NTRS)

Okcu, Selen; Rathsam, Jonathan; Rizzi, Stephen A.

2013-01-01

An aircraft noise synthesis capability is being developed so the annoyance caused by proposed aircraft can be assessed during the design stage. To make synthesized signals as realistic as possible, high fidelity simulation is required for source (e.g., engine noise, airframe noise), propagation and receiver effects. This psychoacoustic study tests whether the jet noise component of synthesized aircraft engine noise can be made more realistic using a low frequency oscillator (LFO) technique to simulate fluctuations in level observed in recordings. Jet noise predictions are commonly made in the frequency domain based on models of time-averaged empirical data. The synthesis process involves conversion of the frequency domain prediction into an audible pressure time history. However, because the predictions are time-invariant, the synthesized sound lacks fluctuations observed in recordings. Such fluctuations are hypothesized to be perceptually important. To introduce time-varying characteristics into jet noise synthesis, a method has been developed that modulates measured or predicted 1/3-octave band levels with a (<20Hz) LFO. The LFO characteristics are determined through analysis of laboratory jet noise recordings. For the aft emission angle, results indicate that signals synthesized using a generic LFO are perceived as more similar to recordings than those using no LFO, and signals synthesized with an angle-specific LFO are more similar to recordings than those synthesized with a generic LFO.

The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes.

PubMed

Staff, Michael; Roberts, Christopher; March, Lyn

2016-10-01

To describe the completeness of routinely collected primary care data that could be used by computer models to predict clinical outcomes among patients with Type 2 Diabetes (T2D). Data on blood pressure, weight, total cholesterol, HDL-cholesterol and glycated haemoglobin levels for regular patients were electronically extracted from the medical record software of 12 primary care practices in Australia for the period 2000-2012. The data was analysed for temporal trends and for associations between patient characteristics and completeness. General practitioners were surveyed to identify barriers to recording data and strategies to improve its completeness. Over the study period data completeness improved up to around 80% complete although the recording of weight remained poorer at 55%. T2D patients with Ischaemic Heart Disease were more likely to have their blood pressure recorded (OR 1.6, p=0.02). Practitioners reported not experiencing any major barriers to using their computer medical record system but did agree with some suggested strategies to improve record completeness. The completeness of routinely collected data suitable for input into computerised predictive models is improving although other dimensions of data quality need to be addressed. Copyright © 2016 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Artificial neural network predictions of lengths of stay on a post-coronary care unit.

PubMed

Mobley, B A; Leasure, R; Davidson, L

1995-01-01

To create and validate a model that predicts length of hospital unit stay. Ex post facto. Seventy-four independent admission variables in 15 general categories were utilized to predict possible stays of 1 to 20 days. Laboratory. Records of patients discharged from a post-coronary care unit in early 1993. An artificial neural network was trained on 629 records and tested on an additional 127 records of patients. The absolute disparity between the actual lengths of stays in the test records and the predictions of the network averaged 1.4 days per record, and the actual length of stay was predicted within 1 day 72% of the time. The artificial neural network demonstrated the capacity to utilize common patient admission characteristics to predict lengths of stay. This technology shows promise in aiding timely initiation of treatment and effective resource planning and cost control.

Role of Subdural Electrocorticography in Prediction of Long-Term Seizure Outcome in Epilepsy Surgery

ERIC Educational Resources Information Center

Asano, Eishi; Juhasz, Csaba; Shah, Aashit; Sood, Sandeep; Chugani, Harry T.

2009-01-01

Since prediction of long-term seizure outcome using preoperative diagnostic modalities remains suboptimal in epilepsy surgery, we evaluated whether interictal spike frequency measures obtained from extraoperative subdural electrocorticography (ECoG) recording could predict long-term seizure outcome. This study included 61 young patients (age…

Prediction and selection of vocabulary for two leisure activities.

PubMed

Dark, Leigha; Balandin, Susan

2007-01-01

People who use augmentative or alternative communication (AAC) need access to a relevant, socially valid vocabulary if they are to communicate successfully in a variety of contexts. Many people with complex communication needs who utilize some form of high technology or low technology AAC rely on others to predict and select vocabulary for them. In this study the ability of one speech pathologist, nine leisure support workers, and six people with cerebral palsy to accurately predict context-specific vocabulary was explored. Participants predicted vocabulary for two leisure activities - sailing session and Internet café - using the blank page method of vocabulary selection to identify the vocabulary items they considered important for each activity. This predicted vocabulary was then compared with the actual vocabulary used in each of the activities. A total of 187 (68%) of the words predicted for the sailing session were used during recorded conversations, with 88 words (32%) not appearing in the recorded samples. During the visit to the Internet café only 104 (47%) of the words predicted occurred in the recorded samples, with 117 words (53%) not occurring at all. These results support the need to socially validate any vocabulary in order to ensure that it is relevant and useful for the person using the AAC system.

[Usefulness of clinical prediction rules for ruling out deep vein thrombosis in a hospital emergency department].

PubMed

Rosa-Jiménez, Francisco; Rosa-Jiménez, Ascensión; Lozano-Rodríguez, Aquiles; Santoro-Martínez, María Del Carmen; Duro-López, María Del Carmen; Carreras-Álvarez de Cienfuegos, Amelia

2015-01-01

To compare the efficacy of the most familiar clinical prediction rules in combination with D-dimer testing to rule out a diagnosis of deep vein thrombosis (DVT) in a hospital emergency department. Retrospective cross-sectional analysis of the case records of all patients attending a hospital emergency department with suspected lower-limb DVT between 1998 and 2002. Ten clinical prediction scores were calculated and D-dimer levels were available for all patients. The gold standard was ultrasound diagnosis of DVT by an independent radiologist who was blinded to clinical records. For each prediction rule, we analyzed the effectiveness of the prediction strategy defined by "low clinical probability and negative D-dimer level" against the ultrasound diagnosis. A total of 861 case records were reviewed and 577 cases were selected; the mean (SD) age was 66.7 (14.2) years. DVT was diagnosed in 145 patients (25.1%). Only the Wells clinical prediction rule and 4 other models had a false negative rate under 2%. The Wells criteria and the score published by Johanning and colleagues identified higher percentages of cases (15.6% and 11.6%, respectively). This study shows that several clinical prediction rules can be safely used in the emergency department, although none of them have proven more effective than the Wells criteria.

Individual Factors Predicting Mental Health Court Diversion Outcome

ERIC Educational Resources Information Center

Verhaaff, Ashley; Scott, Hannah

2015-01-01

Objective: This study examined which individual factors predict mental health court diversion outcome among a sample of persons with mental illness participating in a postcharge diversion program. Method: The study employed secondary analysis of existing program records for 419 persons with mental illness in a court diversion program. Results:…

Personality and behavior prediction and consistency across cultures: A multimethod study of Blacks and Whites in South Africa.

PubMed

Fetvadjiev, Velichko H; Meiring, Deon; van de Vijver, Fons J R; Nel, J Alewyn; Sekaja, Lusanda; Laher, Sumaya

2018-03-01

The cross-cultural universality of behavior's consistency and predictability from personality, assumed in trait models though challenged in cultural psychological models, has usually been operationalized in terms of beliefs and perceptions, and assessed using single-instance self-reports. In a multimethod study of actual behavior across a range of situations, we examined predictability and consistency in participants from the more collectivistic Black ethnic group and the more individualistic White group in South Africa. Participants completed personality questionnaires before the behavior measurements. In Study 1, 107 Black and 241 White students kept diaries for 21 days, recording their behaviors and the situations in which they had occurred. In Study 2, 57 Black and 52 White students were video-recorded in 12 situations in laboratory settings, and external observers scored their behaviors. Across both studies, behavior was predicted by personality on average equally well in the 2 groups, and equally well when using trait-adjective- and behavior-based personality measures. The few cultural differences in situational variability were not in line with individualism-collectivism; however, subjective perceptions of variability, operationalized as dialectical beliefs, were more in line with individualism-collectivism: Blacks viewed their behavior as more variable than Whites. We propose drawing a distinction between subjective beliefs and objective behavior in the study of personality and culture. Larger cultural differences can be expected in beliefs and perceptions than in the links between personality and actual behavior. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Molecular Decay of the Tooth Gene Enamelin (ENAM) Mirrors the Loss of Enamel in the Fossil Record of Placental Mammals

PubMed Central

Meredith, Robert W.; Gatesy, John; Murphy, William J.; Ryder, Oliver A.; Springer, Mark S.

2009-01-01

Vestigial structures occur at both the anatomical and molecular levels, but studies documenting the co-occurrence of morphological degeneration in the fossil record and molecular decay in the genome are rare. Here, we use morphology, the fossil record, and phylogenetics to predict the occurrence of “molecular fossils” of the enamelin (ENAM) gene in four different orders of placental mammals (Tubulidentata, Pholidota, Cetacea, Xenarthra) with toothless and/or enamelless taxa. Our results support the “molecular fossil” hypothesis and demonstrate the occurrence of frameshift mutations and/or stop codons in all toothless and enamelless taxa. We then use a novel method based on selection intensity estimates for codons (ω) to calculate the timing of iterated enamel loss in the fossil record of aardvarks and pangolins, and further show that the molecular evolutionary history of ENAM predicts the occurrence of enamel in basal representatives of Xenarthra (sloths, anteaters, armadillos) even though frameshift mutations are ubiquitous in ENAM sequences of living xenarthrans. The molecular decay of ENAM parallels the morphological degeneration of enamel in the fossil record of placental mammals and provides manifest evidence for the predictive power of Darwin's theory. PMID:19730686

EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

PubMed

Wang, L E; Shaw, Pamela A; Mathelier, Hansie M; Kimmel, Stephen E; French, Benjamin

2016-03-01

The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

Incorporation of Mobile Application (App) Measures Into the Diagnosis of Smartphone Addiction.

PubMed

Lin, Yu-Hsuan; Lin, Po-Hsien; Chiang, Chih-Lin; Lee, Yang-Han; Yang, Cheryl C H; Kuo, Terry B J; Lin, Sheng-Hsuan

2017-07-01

Global smartphone expansion has brought about unprecedented addictive behaviors. The current diagnosis of smartphone addiction is based solely on information from clinical interview. This study aimed to incorporate application (app)-recorded data into psychiatric criteria for the diagnosis of smartphone addiction and to examine the predictive ability of the app-recorded data for the diagnosis of smartphone addiction. Smartphone use data of 79 college students were recorded by a newly developed app for 1 month between December 1, 2013, and May 31, 2014. For each participant, psychiatrists made a diagnosis for smartphone addiction based on 2 approaches: (1) only diagnostic interview (standard diagnosis) and (2) both diagnostic interview and app-recorded data (app-incorporated diagnosis). The app-incorporated diagnosis was further used to build app-incorporated diagnostic criteria. In addition, the app-recorded data were pooled as a score to predict smartphone addiction diagnosis. When app-incorporated diagnosis was used as a gold standard for 12 candidate criteria, 7 criteria showed significant accuracy (area under receiver operating characteristic curve [AUC] > 0.7) and were constructed as app-incorporated diagnostic criteria, which demonstrated remarkable accuracy (92.4%) for app-incorporated diagnosis. In addition, both frequency and duration of daily smartphone use significantly predicted app-incorporated diagnosis (AUC = 0.70 for frequency; AUC = 0.72 for duration). The combination of duration, frequency, and frequency trend for 1 month can accurately predict smartphone addiction diagnosis (AUC = 0.79 for app-incorporated diagnosis; AUC = 0.71 for standard diagnosis). The app-incorporated diagnosis, combining both psychiatric interview and app-recorded data, demonstrated substantial accuracy for smartphone addiction diagnosis. In addition, the app-recorded data performed as an accurate screening tool for app-incorporated diagnosis. © Copyright 2017 Physicians Postgraduate Press, Inc.

Using Admission Assessments to Predict Final Grades in a College Music Program

ERIC Educational Resources Information Center

Lehmann, Andreas C.

2014-01-01

Entrance examinations and auditions are common admission procedures for college music programs, yet few researchers have attempted to look at the long-term predictive validity of such selection processes. In this study, archival data from 93 student records of a German music academy were used to predict development of musicianship skills over the…

Ground-motion parameters of the southwestern Indiana earthquake of 18 June 2002 and the disparity between the observed and predicted values

USGS Publications Warehouse

Street, R.; Wiegand, J.; Woolery, E.W.; Hart, P.

2005-01-01

The M 4.5 southwestern Indiana earthquake of 18 June 2002 triggered 46 blast monitors in Indiana, Illinois, and Kentucky. The resulting free-field particle velocity records, along with similar data from previous earthquakes in the study area, provide a clear standard for judging the reliability of current maps for predicting ground motions greater than 2 Hz in southwestern Indiana and southeastern Illinois. Peak horizontal accelerations and velocities, and 5% damped pseudo-accelerations for the earthquake, generally exceeded ground motions predicted for the top of the bedrock by factors of 2 or more, even after soil amplifications were taken into consideration. It is suggested, but not proven, that the low shear-wave velocity and weathered bedrock in the area are also amplifying the higher-frequency ground motions that have been repeatedly recorded by the blast monitors in the study area. It is also shown that there is a good correlation between the peak ground motions and 5% pseudo-accelerations recorded for the event, and the Modified Mercalli intensities interpreted for the event by the U.S. Geological Survey.

Validity of self-reported cancer history in the health examinees (HEXA) study: A comparison of self-report and cancer registry records.

PubMed

Cho, Sooyoung; Shin, Aesun; Song, Daesub; Park, Jae Kyung; Kim, Yeonjung; Choi, Ji-Yeob; Kang, Daehee; Lee, Jong-Koo

2017-10-01

To assess the validity of the cohort study participants' self-reported cancer history via data linkage to a cancer registry database. We included 143,965 participants from the Health Examinees (HEXA) study recruited between 2004 and 2013 who gave informed consent for record linkage to the Korean Central Cancer Registry (KCCR). The sensitivity and the positive predictive value of self-reported histories of cancer were calculated and 95% confidence intervals were estimated. A total of 4,860 participants who had at least one record in the KCCR were included in the calculation of sensitivity. In addition, 3,671 participants who reported a cancer history at enrollment were included in the calculation of positive predictive value. The overall sensitivity of self-reported cancer history was 72.0%. Breast cancer history among women showed the highest sensitivity (81.2%), whereas the lowest sensitivity was observed for liver cancer (53.7%) and cervical cancer (52.1%). The overall positive predictive value was 81.9%. The highest positive predictive value was observed for thyroid cancer (96.1%) and prostate cancer (96.1%), and the lowest was observed for cervical cancer (43.7%). The accuracy of self-reported cancer history varied by cancer site and may not be sufficient to ascertain cancer incidence, especially for cervical and bladder cancers. Copyright © 2017. Published by Elsevier Ltd.

Predictive value of stroke discharge diagnoses in the Danish National Patient Register.

PubMed

Lühdorf, Pernille; Overvad, Kim; Schmidt, Erik B; Johnsen, Søren P; Bach, Flemming W

2017-08-01

To determine the positive predictive values for stroke discharge diagnoses, including subarachnoidal haemorrhage, intracerebral haemorrhage and cerebral infarction in the Danish National Patient Register. Participants in the Danish cohort study Diet, Cancer and Health with a stroke discharge diagnosis in the National Patient Register between 1993 and 2009 were identified and their medical records were retrieved for validation of the diagnoses. A total of 3326 records of possible cases of stroke were reviewed. The overall positive predictive value for stroke was 69.3% (95% confidence interval (CI) 67.8-70.9%). The predictive values differed according to hospital characteristics, with the highest predictive value of 87.8% (95% CI 85.5-90.1%) found in departments of neurology and the lowest predictive value of 43.0% (95% CI 37.6-48.5%) found in outpatient clinics. The overall stroke diagnosis in the Danish National Patient Register had a limited predictive value. We therefore recommend the critical use of non-validated register data for research on stroke. The possibility of optimising the predictive values based on more advanced algorithms should be considered.

Electronic Health Record-Enabled Big-Data Approaches to Nephrotoxin-Associated Acute Kidney Injury Risk Prediction.

PubMed

Sutherland, Scott M

2018-06-09

Nephrotoxin-associated acute kidney injury (NTx-AKI) has become one of the most common causes of AKI among hospitalized adults and children; across acute and intensive care populations, exposure to nephrotoxins accounts for 15-25% of AKI. Although some interventions have shown promise in observational studies, no treatments currently exist for NTx-AKI once it occurs. Thus, nearly all effective strategies are aimed at prevention. The primary obstacle to prevention is risk prediction and the determination of which patients are more likely to develop NTx-AKI when exposed to medications with nephrotoxic potential. Historically, traditional statistical modeling has been applied to previously recognized clinical risk factors to identify predictors of NTx-AKI. However, increased electronic health record adoption and the evolution of "big-data" approaches to predictive analytics may offer a unique opportunity to prevent NTx-AKI events. This article describes prior and current approaches to NTx-AKI prediction and offers three novel use cases for electronic health record-enabled NTx-AKI forecasting and risk profiling. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Predicting who will drop out of nursing courses: a machine learning exercise.

PubMed

Moseley, Laurence G; Mead, Donna M

2008-05-01

The concepts of causation and prediction are different, and have different implications for practice. This distinction is applied here to studies of the problem of student attrition (although it is more widely applicable). Studies of attrition from nursing courses have tended to concentrate on causation, trying, largely unsuccessfully, to elicit what causes drop out. However, the problem may more fruitfully be cast in terms of predicting who is likely to drop out. One powerful method for attempting to make predictions is rule induction. This paper reports the use of the Answer Tree package from SPSS for that purpose. The main data set consisted of 3978 records on 528 nursing students, split into a training set and a test set. The source was standard university student records. The method obtained 84% sensitivity, 70% specificity, and 94% accuracy on previously unseen cases. The method requires large amounts of high quality data. When such data are available, rule induction offers a way to reduce attrition. It would be desirable to compare its results with those of predictions made by tutors using more informal conventional methods.

Validation of asthma recording in electronic health records: a systematic review

PubMed Central

Nissen, Francis; Quint, Jennifer K; Wilkinson, Samantha; Mullerova, Hana; Smeeth, Liam; Douglas, Ian J

2017-01-01

Objective To describe the methods used to validate asthma diagnoses in electronic health records and summarize the results of the validation studies. Background Electronic health records are increasingly being used for research on asthma to inform health services and health policy. Validation of the recording of asthma diagnoses in electronic health records is essential to use these databases for credible epidemiological asthma research. Methods We searched EMBASE and MEDLINE databases for studies that validated asthma diagnoses detected in electronic health records up to October 2016. Two reviewers independently assessed the full text against the predetermined inclusion criteria. Key data including author, year, data source, case definitions, reference standard, and validation statistics (including sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were summarized in two tables. Results Thirteen studies met the inclusion criteria. Most studies demonstrated a high validity using at least one case definition (PPV >80%). Ten studies used a manual validation as the reference standard; each had at least one case definition with a PPV of at least 63%, up to 100%. We also found two studies using a second independent database to validate asthma diagnoses. The PPVs of the best performing case definitions ranged from 46% to 58%. We found one study which used a questionnaire as the reference standard to validate a database case definition; the PPV of the case definition algorithm in this study was 89%. Conclusion Attaining high PPVs (>80%) is possible using each of the discussed validation methods. Identifying asthma cases in electronic health records is possible with high sensitivity, specificity or PPV, by combining multiple data sources, or by focusing on specific test measures. Studies testing a range of case definitions show wide variation in the validity of each definition, suggesting this may be important for obtaining asthma definitions with optimal validity. PMID:29238227

A Geographic-Information-Systems-Based Approach to Analysis of Characteristics Predicting Student Persistence and Graduation

ERIC Educational Resources Information Center

Ousley, Chris

2010-01-01

This study sought to provide empirical evidence regarding the use of spatial analysis in enrollment management to predict persistence and graduation. The research utilized data from the 2000 U.S. Census and applicant records from The University of Arizona to study the spatial distributions of enrollments. Based on the initial results, stepwise…

Investigating the Written Exam Scores' Prediction Power of TEOG Exam Scores

ERIC Educational Resources Information Center

Kontas, Hakki; Özpolat, Esen Turan

2017-01-01

The purpose of this study was to investigate exam scores' predicting Transition from Primary to Secondary Education (TEOG) exam scores. The research data were obtained from the records of 1035 students studying at the first term of eighth grade in 2015-2016 academic year in e-school system. The research was on relational screening model. Linear…

Predicting Employment Outcomes of Consumers of State-Operated Comprehensive Rehabilitation Centers

ERIC Educational Resources Information Center

Beach, David Thomas

2009-01-01

This study used records from a state-operated comprehensive rehabilitation center to investigate possible predictive factors related to completing comprehensive rehabilitation center programs and successful vocational rehabilitation (VR) case closure. An analysis of demographic data of randomly selected comprehensive rehabilitation center…

Can data extraction from general practitioners' electronic records be used to predict clinical outcomes for patients with type 2 diabetes?

PubMed

Staff, Michael

2012-01-01

The review of clinical data extraction from electronic records is increasingly being used as a tool to assist general practitioners (GPs) manage their patients in Australia. Type 2 diabetes (T2DM) is a chronic condition cared for primarily in the general practice setting that lends itself to the application of tools in this area. To assess the feasibility of extracting data from a general practice medical record software package to predict clinically significant outcomes for patients with T2DM. A pilot study was conducted involving two large practices where routinely collected clinical data were extracted and inputted into the United Kingdom Prospective Diabetes Study Outcomes Model to predict life expectancy. An initial assessment of the completeness of data available was performed and then for those patients aged between 45 and 64 years with adequate data life expectancies estimated. A total of 1019 patients were identified as current patients with T2DM. There were sufficient data available on 40% of patients from one practice and 49% from the other to provide inputs into the UKPDS Outcomes Model. Predicted life expectancy was similar across the practices with women having longer life expectancies than men. Improved compliance with current management guidelines for glycaemic, lipid and blood pressure control was demonstrated to increase life expectancy between 1.0 and 2.4 years dependent on gender and age group. This pilot demonstrated that clinical data extraction from electronic records is feasible although there are several limitations chiefly caused by the incompleteness of data for patients with T2DM.

Application of the Streamflow Prediction Tool to Estimate Sediment Dredging Volumes in Texas Coastal Waterways

NASA Astrophysics Data System (ADS)

Yeates, E.; Dreaper, G.; Afshari, S.; Tavakoly, A. A.

2017-12-01

Over the past six fiscal years, the United States Army Corps of Engineers (USACE) has contracted an average of about a billion dollars per year for navigation channel dredging. To execute these funds effectively, USACE Districts must determine which navigation channels need to be dredged in a given year. Improving this prioritization process results in more efficient waterway maintenance. This study uses the Streamflow Prediction Tool, a runoff routing model based on global weather forecast ensembles, to estimate dredged volumes. This study establishes regional linear relationships between cumulative flow and dredged volumes over a long-term simulation covering 30 years (1985-2015), using drainage area and shoaling parameters. The study framework integrates the National Hydrography Dataset (NHDPlus Dataset) with parameters from the Corps Shoaling Analysis Tool (CSAT) and dredging record data from USACE District records. Results in the test cases of the Houston Ship Channel and the Sabine and Port Arthur Harbor waterways in Texas indicate positive correlation between the simulated streamflows and actual dredging records.

Can information on functional and cognitive status improve short-term mortality risk prediction among community-dwelling older people? A cohort study using a UK primary care database

PubMed Central

Sultana, Janet; Fontana, Andrea; Giorgianni, Francesco; Basile, Giorgio; Patorno, Elisabetta; Pilotto, Alberto; Molokhia, Mariam; Stewart, Robert; Sturkenboom, Miriam; Trifirò, Gianluca

2018-01-01

Background Functional and cognitive domains have rarely been evaluated for their prognostic value in general practice databases. The aim of this study was to identify functional and cognitive domains in The Health Improvement Network (THIN) and to evaluate their additional value for the prediction of 1-month and 1-year mortality in elderly people. Materials and methods A cohort study was conducted using a UK nationwide general practitioner database. A total of 1,193,268 patients aged 65 years or older, of whom 15,300 had dementia, were identified from 2000 to 2012. Information on mobility, dressing and accommodation was recorded frequently enough to be analyzed further in THIN. Cognition data could not be used due to very poor recording of data in THIN. One-year and 1-month mortality was predicted using logistic models containing variables such as age, sex, disease score and functionality status. Results A significant but moderate improvement in 1-year and 1-month mortality prediction in elderly people was observed by adding accommodation to the variables age, sex and disease score, as the c-statistic (95% confidence interval [CI]) increased from 0.71 (0.70–0.72) to 0.76 (0.75–0.77) and 0.73 (0.71–0.75) to 0.79 (0.77–0.80), respectively. A less notable improvement in the prediction of 1-year and 1-month mortality was observed in people with dementia. Conclusion Functional domains moderately improved the accuracy of a model including age, sex and comorbidities in predicting 1-year and 1-month mortality risk among community-dwelling older people, but they were much less able to predict mortality in people with dementia. Cognition could not be explored as a predictor of mortality due to insufficient data being recorded. PMID:29296099

Predicting Computer Science Ph.D. Completion: A Case Study

ERIC Educational Resources Information Center

Cox, G. W.; Hughes, W. E., Jr.; Etzkorn, L. H.; Weisskopf, M. E.

2009-01-01

This paper presents the results of an analysis of indicators that can be used to predict whether a student will succeed in a Computer Science Ph.D. program. The analysis was conducted by studying the records of 75 students who have been in the Computer Science Ph.D. program of the University of Alabama in Huntsville. Seventy-seven variables were…

Implications of Extended Solar Minima

NASA Technical Reports Server (NTRS)

Adams, Mitzi L.; Davis, J. M.

2009-01-01

Since the discovery of periodicity in the solar cycle, the historical record of sunspot number has been carefully examined, attempting to make predictions about the next cycle. Much emphasis has been on predicting the maximum amplitude and length of the next cycle. Because current space-based and suborbital instruments are designed to study active phenomena, there is considerable interest in estimating the length and depth of the current minimum. We have developed criteria for the definition of a minimum and applied it to the historical sunspot record starting in 1749. In doing so, we find that 1) the current minimum is not yet unusually long and 2) there is no obvious way of predicting when, using our definition, the current minimum may end. However, by grouping the data into 22- year cycles there is an interesting pattern of extended minima that recurs every fourth or fifth 22-year cycle. A preliminary comparison of this pattern with other records, suggests the possibility of a correlation between extended minima and lower levels of solar irradiance.

Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach.

PubMed

Ko, Kyung Dae; El-Ghazawi, Tarek; Kim, Dongkyu; Morizono, Hiroki

2014-05-01

Motor neuron diseases (MNDs) are a class of progressive neurological diseases that damage the motor neurons. An accurate diagnosis is important for the treatment of patients with MNDs because there is no standard cure for the MNDs. However, the rates of false positive and false negative diagnoses are still very high in this class of diseases. In the case of Amyotrophic Lateral Sclerosis (ALS), current estimates indicate 10% of diagnoses are false-positives, while 44% appear to be false negatives. In this study, we developed a new methodology to profile specific medical information from patient medical records for predicting the progression of motor neuron diseases. We implemented a system using Hbase and the Random forest classifier of Apache Mahout to profile medical records provided by the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) site, and we achieved 66% accuracy in the prediction of ALS progress.

Predicting Student Responsiveness to Fast ForWord Using DIBELS Subtests

ERIC Educational Resources Information Center

Cavallo, Fernando

2012-01-01

The current study was completed through a retrospective analysis of school records of elementary school students in the Northeast Region of the Philadelphia School District (PSD) who have participated in the Fast ForWord (FFW) Language program. The data requested from student records included: demographic information (e.g., gender, grade, age,…

Predicting the Maximum Earthquake Magnitude from Seismic Data in Israel and Its Neighboring Countries.

PubMed

Last, Mark; Rabinowitz, Nitzan; Leonard, Gideon

2016-01-01

This paper explores several data mining and time series analysis methods for predicting the magnitude of the largest seismic event in the next year based on the previously recorded seismic events in the same region. The methods are evaluated on a catalog of 9,042 earthquake events, which took place between 01/01/1983 and 31/12/2010 in the area of Israel and its neighboring countries. The data was obtained from the Geophysical Institute of Israel. Each earthquake record in the catalog is associated with one of 33 seismic regions. The data was cleaned by removing foreshocks and aftershocks. In our study, we have focused on ten most active regions, which account for more than 80% of the total number of earthquakes in the area. The goal is to predict whether the maximum earthquake magnitude in the following year will exceed the median of maximum yearly magnitudes in the same region. Since the analyzed catalog includes only 28 years of complete data, the last five annual records of each region (referring to the years 2006-2010) are kept for testing while using the previous annual records for training. The predictive features are based on the Gutenberg-Richter Ratio as well as on some new seismic indicators based on the moving averages of the number of earthquakes in each area. The new predictive features prove to be much more useful than the indicators traditionally used in the earthquake prediction literature. The most accurate result (AUC = 0.698) is reached by the Multi-Objective Info-Fuzzy Network (M-IFN) algorithm, which takes into account the association between two target variables: the number of earthquakes and the maximum earthquake magnitude during the same year.

Predicting the Maximum Earthquake Magnitude from Seismic Data in Israel and Its Neighboring Countries

PubMed Central

2016-01-01

This paper explores several data mining and time series analysis methods for predicting the magnitude of the largest seismic event in the next year based on the previously recorded seismic events in the same region. The methods are evaluated on a catalog of 9,042 earthquake events, which took place between 01/01/1983 and 31/12/2010 in the area of Israel and its neighboring countries. The data was obtained from the Geophysical Institute of Israel. Each earthquake record in the catalog is associated with one of 33 seismic regions. The data was cleaned by removing foreshocks and aftershocks. In our study, we have focused on ten most active regions, which account for more than 80% of the total number of earthquakes in the area. The goal is to predict whether the maximum earthquake magnitude in the following year will exceed the median of maximum yearly magnitudes in the same region. Since the analyzed catalog includes only 28 years of complete data, the last five annual records of each region (referring to the years 2006–2010) are kept for testing while using the previous annual records for training. The predictive features are based on the Gutenberg-Richter Ratio as well as on some new seismic indicators based on the moving averages of the number of earthquakes in each area. The new predictive features prove to be much more useful than the indicators traditionally used in the earthquake prediction literature. The most accurate result (AUC = 0.698) is reached by the Multi-Objective Info-Fuzzy Network (M-IFN) algorithm, which takes into account the association between two target variables: the number of earthquakes and the maximum earthquake magnitude during the same year. PMID:26812351

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Pencina, Michael J; Ioannidis, John P A

2017-01-01

Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies. © The Author 2016. 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 nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

NASA Astrophysics Data System (ADS)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

Validation of Predictors of Fall Events in Hospitalized Patients With Cancer.

PubMed

Weed-Pfaff, Samantha H; Nutter, Benjamin; Bena, James F; Forney, Jennifer; Field, Rosemary; Szoka, Lynn; Karius, Diana; Akins, Patti; Colvin, Christina M; Albert, Nancy M

2016-10-01

A seven-item cancer-specific fall risk tool (Cleveland Clinic Capone-Albert [CC-CA] Fall Risk Score) was shown to have a strong concordance index for predicting falls; however, validation of the model is needed. The aims of this study were to validate that the CC-CA Fall Risk Score, made up of six factors, predicts falls in patients with cancer and to determine if the CC-CA Fall Risk Score performs better than the Morse Fall Tool. Using a prospective, comparative methodology, data were collected from electronic health records of patients hospitalized for cancer care in four hospitals. Risk factors from each tool were recorded, when applicable. Multivariable models were created to predict the probability of a fall. A concordance index for each fall tool was calculated. The CC-CA Fall Risk Score provided higher discrimination than the Morse Fall Tool in predicting fall events in patients hospitalized for cancer management.

Pediatric ocular trauma score as a prognostic tool in the management of pediatric traumatic cataracts.

PubMed

Shah, Mehul A; Agrawal, Rupesh; Teoh, Ryan; Shah, Shreya M; Patel, Kashyap; Gupta, Satyam; Gosai, Siddharth

2017-05-01

To introduce and validate the pediatric ocular trauma score (POTS) - a mathematical model to predict visual outcome trauma in children with traumatic cataract METHODS: In this retrospective cohort study, medical records of consecutive children with traumatic cataracts aged 18 and below were retrieved and analysed. Data collected included age, gender, visual acuity, anterior segment and posterior segment findings, nature of surgery, treatment for amblyopia, follow-up, and final outcome was recorded on a precoded data information sheet. POTS was derived based on the ocular trauma score (OTS), adjusting for age of patient and location of the injury. Visual outcome was predicted using the OTS and the POTS and using receiver operating characteristic (ROC) curves. POTS predicted outcomes were more accurate compared to that of OTS (p = 0.014). POTS is a more sensitive and specific score with more accurate predicted outcomes compared to OTS, and is a viable tool to predict visual outcomes of pediatric ocular trauma with traumatic cataract.

Real-time control of walking using recordings from dorsal root ganglia.

PubMed

Holinski, B J; Everaert, D G; Mushahwar, V K; Stein, R B

2013-10-01

The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback. Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.

A comparison of self-report and health care provider data to assess surveillance definitions of influenza-like illness in outpatients.

PubMed

Barbara, Angela M; Loeb, Mark; Dolovich, Lisa; Brazil, Kevin; Russell, Margaret L

2012-01-01

Several surveillance definitions of influenza-like illness (ILI) have been proposed, based on the presence of symptoms. Symptom data can be obtained from patients, medical records, or both. Past research has found that agreements between health record data and self-report are variable depending on the specific symptom. Therefore, we aimed to explore the implications of using data on influenza symptoms extracted from medical records, similar data collected prospectively from outpatients, and the combined data from both sources as predictors of laboratory-confirmed influenza. Using data from the Hutterite Influenza Prevention Study, we calculated: 1) the sensitivity, specificity and predictive values of individual symptoms within surveillance definitions; 2) how frequently surveillance definitions correlated to laboratory-confirmed influenza; and 3) the predictive value of surveillance definitions. Of the 176 participants with reports from participants and medical records, 142 (81%) were tested for influenza and 37 (26%) were PCR positive for influenza. Fever (alone) and fever combined with cough and/or sore throat were highly correlated with being PCR positive for influenza for all data sources. ILI surveillance definitions, based on symptom data from medical records only or from both medical records and self-report, were better predictors of laboratory-confirmed influenza with higher odds ratios and positive predictive values. The choice of data source to determine ILI will depend on the patient population, outcome of interest, availability of data source, and use for clinical decision making, research, or surveillance.

Analyses of a 426-Day Record of Seafloor Gravity and Pressure Time Series in the North Sea

NASA Astrophysics Data System (ADS)

Rosat, S.; Escot, B.; Hinderer, J.; Boy, J.-P.

2017-04-01

Continuous gravity observations of ocean and solid tides are usually done with land-based gravimeters. In this study, we analyze a 426-day record of time-varying gravity acquired by an ocean-bottom Scintrex spring gravimeter between August 2005 and November 2006 at the Troll A site located in the North Sea at a depth of 303 m. Sea-bottom pressure changes were also recorded in parallel with a Paroscientific quartz pressure sensor. From these data, we show a comparison of the noise level of the seafloor gravimeter with respect to two standard land-based relative gravimeters: a Scintrex CG5 and a GWR Superconducting Gravimeter that were recording at the J9 gravimetric observatory of Strasbourg (France). We also compare the analyzed gravity records with the predicted solid and oceanic tides. The oceanic tides recorded by the seafloor barometer are also analyzed and compared to the predicted ones using FES2014b ocean model. Observed diurnal and semi-diurnal components are in good agreement with FES2014b predictions. Smallest constituents reflect some differences that may be attributed to non-linearity occurring at the Troll A site. Using the barotropic TUGO-m dynamic model of sea-level response to ECMWF atmospheric pressure and winds forcing, we show a good agreement with the detided ocean-bottom pressure residuals. About 4 hPa of standard deviation of remaining sea-bottom pressure are, however, not explained by the TUGO-m dynamic model.

Early prediction of cerebral palsy by computer-based video analysis of general movements: a feasibility study.

PubMed

Adde, Lars; Helbostad, Jorunn L; Jensenius, Alexander R; Taraldsen, Gunnar; Grunewaldt, Kristine H; Støen, Ragnhild

2010-08-01

The aim of this study was to investigate the predictive value of a computer-based video analysis of the development of cerebral palsy (CP) in young infants. A prospective study of general movements used recordings from 30 high-risk infants (13 males, 17 females; mean gestational age 31wks, SD 6wks; range 23-42wks) between 10 and 15 weeks post term when fidgety movements should be present. Recordings were analysed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analyses. CP status was reported at 5 years. Thirteen infants developed CP (eight hemiparetic, four quadriparetic, one dyskinetic; seven ambulatory, three non-ambulatory, and three unknown function), of whom one had fidgety movements. Variability of the centroid of motion had a sensitivity of 85% and a specificity of 71% in identifying CP. By combining this with variables reflecting the amount of motion, specificity increased to 88%. Nine out of 10 children with CP, and for whom information about functional level was available, were correctly predicted with regard to ambulatory and non-ambulatory function. Prediction of CP can be provided by computer-based video analysis in young infants. The method may serve as an objective and feasible tool for early prediction of CP in high-risk infants.

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.

Automated Pressure Injury Risk Assessment System Incorporated Into an Electronic Health Record System.

PubMed

Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

Pressure injury risk assessment is the first step toward preventing pressure injuries, but traditional assessment tools are time-consuming, resulting in work overload and fatigue for nurses. The objectives of the study were to build an automated pressure injury risk assessment system (Auto-PIRAS) that can assess pressure injury risk using data, without requiring nurses to collect or input additional data, and to evaluate the validity of this assessment tool. A retrospective case-control study and a system development study were conducted in a 1,355-bed university hospital in Seoul, South Korea. A total of 1,305 pressure injury patients and 5,220 nonpressure injury patients participated for the development of a risk scoring algorithm: 687 and 2,748 for the validation of the algorithm and 237 and 994 for validation after clinical implementation, respectively. A total of 4,211 pressure injury-related clinical variables were extracted from the electronic health record (EHR) systems to develop a risk scoring algorithm, which was validated and incorporated into the EHR. That program was further evaluated for predictive and concurrent validity. Auto-PIRAS, incorporated into the EHR system, assigned a risk assessment score of high, moderate, or low and displayed this on the Kardex nursing record screen. Risk scores were updated nightly according to 10 predetermined risk factors. The predictive validity measures of the algorithm validation stage were as follows: sensitivity = .87, specificity = .90, positive predictive value = .68, negative predictive value = .97, Youden index = .77, and the area under the receiver operating characteristic curve = .95. The predictive validity measures of the Braden Scale were as follows: sensitivity = .77, specificity = .93, positive predictive value = .72, negative predictive value = .95, Youden index = .70, and the area under the receiver operating characteristic curve = .85. The kappa of the Auto-PIRAS and Braden Scale risk classification result was .73. The predictive performance of the Auto-PIRAS was similar to Braden Scale assessments conducted by nurses. Auto-PIRAS is expected to be used as a system that assesses pressure injury risk automatically without additional data collection by nurses.

Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

PubMed

Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

2015-01-01

Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

Accuracy of parent-reported measles-containing vaccination status of children with measles.

PubMed

Liu, G; Liao, Z; Xu, X; Liang, Y; Xiong, Y; Ni, J

2017-03-01

The validity of parent-reported measles-containing vaccination history in children with measles has not been assessed. This study evaluated the accuracy of parental recall of measles-containing vaccination histories in Shenzhen, China. A retrospective study was performed to compare the data from the electronic records with parental recall. The electronic records were regarded as accurate data about the children's measles-containing vaccination status. We collected data from the National Notifiable Diseases Surveillance System and the Immunization Program Information Management System in Shenzhen city, China. Between 2009 and 2014, there were 163 children with measles who had electronic vaccination records; the vaccination status of these cases was reported by the parents in the field epidemiological investigation. We validated parental recall with electronic records. The agreement between parental recall and electronic records was 78.7%. The kappa value was 0.57. The parent-reported measles-containing vaccination rate was higher than the electronic record (48.5% vs 41.7%, χ 2  = 53.64, P < 0.001). The true positive rate for parental recall was 82.4%, and the true negative rate was 75.8%. The positive predictive value was 70.9%, and the negative predictive value was 76.6%. In children with measles, parental recall slightly overestimated the measles vaccination rate, and the vaccination status recalled by parents was in moderate agreement with the electronic record. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Accuracy of Self-Reported Cervical and Breast Cancer Screening by Women with Intellectual Disability

ERIC Educational Resources Information Center

Son, Esther; Parish, Susan L.; Swaine, Jamie G.; Luken, Karen

2013-01-01

This study examines the accuracy of self-report of cervical and breast cancer screening by women with intellectual disability ("n" ?=? 155). Data from face-to-face interviews and medical records were analyzed. Total agreement, sensitivity, specificity, positive predictive value and negative predictive value were calculated. Total…

White cell count in the normal range and short-term and long-term mortality: international comparisons of electronic health record cohorts in England and New Zealand.

PubMed

Shah, Anoop Dinesh; Thornley, Simon; Chung, Sheng-Chia; Denaxas, Spiros; Jackson, Rod; Hemingway, Harry

2017-02-17

Electronic health records offer the opportunity to discover new clinical implications for established blood tests, but international comparisons have been lacking. We tested the association of total white cell count (WBC) with all-cause mortality in England and New Zealand. Primary care practices in England (ClinicAl research using LInked Bespoke studies and Electronic health Records (CALIBER)) and New Zealand (PREDICT). Analysis of linked electronic health record data sets: CALIBER (primary care, hospitalisation, mortality and acute coronary syndrome registry) and PREDICT (cardiovascular risk assessments in primary care, hospitalisations, mortality, dispensed medication and laboratory results). People aged 30-75 years with no prior cardiovascular disease (CALIBER: N=686 475, 92.0% white; PREDICT: N=194 513, 53.5% European, 14.7% Pacific, 13.4% Maori), followed until death, transfer out of practice (in CALIBER) or study end. HRs for mortality were estimated using Cox models adjusted for age, sex, smoking, diabetes, systolic blood pressure, ethnicity and total:high-density lipoprotein (HDL) cholesterol ratio. We found 'J'-shaped associations between WBC and mortality; the second quintile was associated with lowest risk in both cohorts. High WBC within the reference range (8.65-10.05×10 9 /L) was associated with significantly increased mortality compared to the middle quintile (6.25-7.25×10 9 /L); adjusted HR 1.51 (95% CI 1.43 to 1.59) in CALIBER and 1.33 (95% CI 1.06 to 1.65) in PREDICT. WBC outside the reference range was associated with even greater mortality. The association was stronger over the first 6 months of follow-up, but similar across ethnic groups. Clinically recorded WBC within the range considered 'normal' is associated with mortality in ethnically different populations from two countries, particularly within the first 6 months. Large-scale international comparisons of electronic health record cohorts might yield new insights from widely performed clinical tests. NCT02014610. 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/.

Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.

PubMed

Nakas, Christos T; Schütz, Narayan; Werners, Marcus; Leichtle, Alexander B

2016-01-01

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Text mining approach to predict hospital admissions using early medical records from the emergency department.

PubMed

Lucini, Filipe R; S Fogliatto, Flavio; C da Silveira, Giovani J; L Neyeloff, Jeruza; Anzanello, Michel J; de S Kuchenbecker, Ricardo; D Schaan, Beatriz

2017-04-01

Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Preoperative Electrocardiogram Score for Predicting New-Onset Postoperative Atrial Fibrillation in Patients Undergoing Cardiac Surgery.

PubMed

Gu, Jiwei; Andreasen, Jan J; Melgaard, Jacob; Lundbye-Christensen, Søren; Hansen, John; Schmidt, Erik B; Thorsteinsson, Kristinn; Graff, Claus

2017-02-01

To investigate if electrocardiogram (ECG) markers from routine preoperative ECGs can be used in combination with clinical data to predict new-onset postoperative atrial fibrillation (POAF) following cardiac surgery. Retrospective observational case-control study. Single-center university hospital. One hundred consecutive adult patients (50 POAF, 50 without POAF) who underwent coronary artery bypass grafting, valve surgery, or combinations. Retrospective review of medical records and registration of POAF. Clinical data and demographics were retrieved from the Western Denmark Heart Registry and patient records. Paper tracings of preoperative ECGs were collected from patient records, and ECG measurements were read by two independent readers blinded to outcome. A subset of four clinical variables (age, gender, body mass index, and type of surgery) were selected to form a multivariate clinical prediction model for POAF and five ECG variables (QRS duration, PR interval, P-wave duration, left atrial enlargement, and left ventricular hypertrophy) were used in a multivariate ECG model. Adding ECG variables to the clinical prediction model significantly improved the area under the receiver operating characteristic curve from 0.54 to 0.67 (with cross-validation). The best predictive model for POAF was a combined clinical and ECG model with the following four variables: age, PR-interval, QRS duration, and left atrial enlargement. ECG markers obtained from a routine preoperative ECG may be helpful in predicting new-onset POAF in patients undergoing cardiac surgery. Copyright © 2017 Elsevier Inc. All rights reserved.

Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity.

PubMed

Buxton, Rachel; McKenna, Megan F; Clapp, Mary; Meyer, Erik; Stabenau, Erik; Angeloni, Lisa M; Crooks, Kevin; Wittemyer, George

2018-04-20

Passive acoustic monitoring has the potential to be a powerful approach for assessing biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examine the ability of acoustic indices to predict the diversity and abundance of biological sounds within recordings. First we reviewed the acoustic index literature and found that over 60 indices have been applied to a range of objectives with varying success. We then implemented a subset of the most successful indices on acoustic data collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental U.S., developing a predictive model of the diversity of animal sounds observed in recordings. For terrestrial recordings, random forest models using a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R 2 > = 0.94, mean squared error MSE < = 170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively impacted by insect, weather, and anthropogenic sounds. For marine recordings, random forest models predicted Shannon diversity, richness, and total number of biological sounds with low accuracy (R 2 < = 0.40, MSE > = 195), indicating that alternative methods are necessary in marine habitats. Our results suggest that using a combination of relevant indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats in the face of accelerating human-caused ecological change. This article is protected by copyright. All rights reserved.

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models.

PubMed

Alzeer, Abdullah H; Jones, Josette; Bair, Matthew J

2018-05-01

Several opioid risk assessment tools are available to prescribers to evaluate opioid analgesic abuse among chronic patients. The objectives of this study are to 1) identify variables available in the literature to predict opioid abuse; 2) explore and compare methods (population, database, and analysis) used to develop statistical models that predict opioid abuse; and 3) understand how outcomes were defined in each statistical model predicting opioid abuse. The OVID database was searched for this study. The search was limited to articles written in English and published from January 1990 to April 2016. This search generated 1,409 articles. Only seven studies and nine models met our inclusion-exclusion criteria. We found nine models and identified 75 distinct variables. Three studies used administrative claims data, and four studies used electronic health record data. The majority, four out of seven articles (six out of nine models), were primarily dependent on the presence or absence of opioid abuse or dependence (ICD-9 diagnosis code) to define opioid abuse. However, two articles used a predefined list of opioid-related aberrant behaviors. We identified variables used to predict opioid abuse from electronic health records and administrative data. Medication variables are the recurrent variables in the articles reviewed (33 variables). Age and gender are the most consistent demographic variables in predicting opioid abuse. Overall, there is similarity in the sampling method and inclusion/exclusion criteria (age, number of prescriptions, follow-up period, and data analysis methods). Intuitive research to utilize unstructured data may increase opioid abuse models' accuracy.

Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length

NASA Astrophysics Data System (ADS)

Ricciuto, Daniel M.; King, Anthony W.; Dragoni, D.; Post, Wilfred M.

2011-03-01

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.

Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome

PubMed Central

Hu, Danqing; Huang, Zhengxing; Chan, Tak-Ming; Dong, Wei; Lu, Xudong; Duan, Huilong

2016-01-01

Background: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner. Methods: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records. Firstly, both a rule-based medical language processing method and a machine learning method (i.e., Conditional Random Fields (CRFs)) are developed to extract essential patient features from unstructured admission records. After that, state-of-the-art supervised machine learning algorithms are applied to construct MACE prediction models from data. Results: We comparatively evaluate the performance of the proposed approach on a real clinical dataset consisting of 2930 ACS patient samples collected from a Chinese hospital. Our best model achieved 72% AUC in MACE prediction. In comparison of the performance between our models and two well-known ACS risk score tools, i.e., GRACE and TIMI, our learned models obtain better performances with a significant margin. Conclusions: Experimental results reveal that our approach can obtain competitive performance in MACE prediction. The comparison of classifiers indicates the proposed approach has a competitive generality with datasets extracted by different feature extraction methods. Furthermore, our MACE prediction model obtained a significant improvement by comparison with both GRACE and TIMI. It indicates that using admission records can effectively provide MACE prediction service for ACS patients at the early stage of their hospitalizations. PMID:27649220

Validation of a Delirium Risk Assessment Using Electronic Medical Record Information.

PubMed

Rudolph, James L; Doherty, Kelly; Kelly, Brittany; Driver, Jane A; Archambault, Elizabeth

2016-03-01

Identifying patients at risk for delirium allows prompt application of prevention, diagnostic, and treatment strategies; but is rarely done. Once delirium develops, patients are more likely to need posthospitalization skilled care. This study developed an a priori electronic prediction rule using independent risk factors identified in a National Center of Clinical Excellence meta-analysis and validated the ability to predict delirium in 2 cohorts. Retrospective analysis followed by prospective validation. Tertiary VA Hospital in New England. A total of 27,625 medical records of hospitalized patients and 246 prospectively enrolled patients admitted to the hospital. The electronic delirium risk prediction rule was created using data obtained from the patient electronic medical record (EMR). The primary outcome, delirium, was identified 2 ways: (1) from the EMR (retrospective cohort) and (2) clinical assessment on enrollment and daily thereafter (prospective participants). We assessed discrimination of the delirium prediction rule with the C-statistic. Secondary outcomes were length of stay and discharge to rehabilitation. Retrospectively, delirium was identified in 8% of medical records (n = 2343); prospectively, delirium during hospitalization was present in 26% of participants (n = 64). In the retrospective cohort, medical record delirium was identified in 2%, 3%, 11%, and 38% of the low, intermediate, high, and very high-risk groups, respectively (C-statistic = 0.81; 95% confidence interval 0.80-0.82). Prospectively, the electronic prediction rule identified delirium in 15%, 18%, 31%, and 55% of these groups (C-statistic = 0.69; 95% confidence interval 0.61-0.77). Compared with low-risk patients, those at high- or very high delirium risk had increased length of stay (5.7 ± 5.6 vs 3.7 ± 2.7 days; P = .001) and higher rates of discharge to rehabilitation (8.9% vs 20.8%; P = .02). Automatic calculation of delirium risk using an EMR algorithm identifies patients at risk for delirium, which creates a critical opportunity for gaining clinical efficiencies and improving delirium identification, including those needing skilled care. Published by Elsevier Inc.

Use of Anecdotal Occurrence Data in Species Distribution Models: An Example Based on the White-Nosed Coati (Nasua narica) in the American Southwest

PubMed Central

Frey, Jennifer K.; Lewis, Jeremy C.; Guy, Rachel K.; Stuart, James N.

2013-01-01

Simple Summary We evaluated the influence of occurrence records with different reliability on predicted distribution of a unique, rare mammal in the American Southwest, the white-nosed coati (Nasua narica). We concluded that occurrence datasets that include anecdotal records can be used to infer species distributions, providing such data are used only for easily-identifiable species and based on robust modeling methods such as maximum entropy. Use of a reliability rating system is critical for using anecdotal data. Abstract Species distributions are usually inferred from occurrence records. However, these records are prone to errors in spatial precision and reliability. Although influence of spatial errors has been fairly well studied, there is little information on impacts of poor reliability. Reliability of an occurrence record can be influenced by characteristics of the species, conditions during the observation, and observer’s knowledge. Some studies have advocated use of anecdotal data, while others have advocated more stringent evidentiary standards such as only accepting records verified by physical evidence, at least for rare or elusive species. Our goal was to evaluate the influence of occurrence records with different reliability on species distribution models (SDMs) of a unique mammal, the white-nosed coati (Nasua narica) in the American Southwest. We compared SDMs developed using maximum entropy analysis of combined bioclimatic and biophysical variables and based on seven subsets of occurrence records that varied in reliability and spatial precision. We found that the predicted distribution of the coati based on datasets that included anecdotal occurrence records were similar to those based on datasets that only included physical evidence. Coati distribution in the American Southwest was predicted to occur in southwestern New Mexico and southeastern Arizona and was defined primarily by evenness of climate and Madrean woodland and chaparral land-cover types. Coati distribution patterns in this region suggest a good model for understanding the biogeographic structure of range margins. We concluded that occurrence datasets that include anecdotal records can be used to infer species distributions, providing such data are used only for easily-identifiable species and based on robust modeling methods such as maximum entropy. Use of a reliability rating system is critical for using anecdotal data. PMID:26487405

High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays.

PubMed

Kaiju, Taro; Doi, Keiichi; Yokota, Masashi; Watanabe, Kei; Inoue, Masato; Ando, Hiroshi; Takahashi, Kazutaka; Yoshida, Fumiaki; Hirata, Masayuki; Suzuki, Takafumi

2017-01-01

Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm 2 . The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control.

High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays

PubMed Central

Kaiju, Taro; Doi, Keiichi; Yokota, Masashi; Watanabe, Kei; Inoue, Masato; Ando, Hiroshi; Takahashi, Kazutaka; Yoshida, Fumiaki; Hirata, Masayuki; Suzuki, Takafumi

2017-01-01

Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm2. The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control. PMID:28442997

How to improve breeding value prediction for feed conversion ratio in the case of incomplete longitudinal body weights.

PubMed

Tran, V H Huynh; Gilbert, H; David, I

2017-01-01

With the development of automatic self-feeders, repeated measurements of feed intake are becoming easier in an increasing number of species. However, the corresponding BW are not always recorded, and these missing values complicate the longitudinal analysis of the feed conversion ratio (FCR). Our aim was to evaluate the impact of missing BW data on estimations of the genetic parameters of FCR and ways to improve the estimations. On the basis of the missing BW profile in French Large White pigs (male pigs weighed weekly, females and castrated males weighed monthly), we compared 2 different ways of predicting missing BW, 1 using a Gompertz model and 1 using a linear interpolation. For the first part of the study, we used 17,398 weekly records of BW and feed intake recorded over 16 consecutive weeks in 1,222 growing male pigs. We performed a simulation study on this data set to mimic missing BW values according to the pattern of weekly proportions of incomplete BW data in females and castrated males. The FCR was then computed for each week using observed data (obser_FCR), data with missing BW (miss_FCR), data with BW predicted using a Gompertz model (Gomp_FCR), and data with BW predicted by linear interpolation (interp_FCR). Heritability (h) was estimated, and the EBV was predicted for each repeated FCR using a random regression model. In the second part of the study, the full data set (males with their complete BW records, castrated males and females with missing BW) was analyzed using the same methods (miss_FCR, Gomp_FCR, and interp_FCR). Results of the simulation study showed that h were overestimated in the case of missing BW and that predicting BW using a linear interpolation provided a more accurate estimation of h and of EBV than a Gompertz model. Over 100 simulations, the correlation between obser_EBV and interp_EBV, Gomp_EBV, and miss_EBV was 0.93 ± 0.02, 0.91 ± 0.01, and 0.79 ± 0.04, respectively. The heritabilities obtained with the full data set were quite similar for miss_FCR, Gomp_FCR, and interp_FCR. In conclusion, when the proportion of missing BW is high, genetic parameters of FCR are not well estimated. In French Large White pigs, in the growing period extending from d 65 to 168, prediction of missing BW using a Gompertz growth model slightly improved the estimations, but the linear interpolation improved the estimation to a greater extent. This result is due to the linear rather than sigmoidal increase in BW over the study period.

A Longitudinal Analysis of Risk Factors for Child Maltreatment: Findings of a 17-Year Prospective Study of Officially Recorded and Self-Reported Child Abuse and Neglect.

ERIC Educational Resources Information Center

Brown, Jocelyn; Cohen, Patricia; Johnson, Jeffrey G.; Salzinger, Suzanne

1998-01-01

Repeated surveys assessing demographic variables, family relationships, parental behavior, and parent/child characteristics were administered to 664 families and compared with child abuse and neglect data from state records and retrospective self-reports. Analysis found maternal youth and sociopathy predicted general child maltreatment, but…

Predictive Validity and Impact of CAEP Standard 3.2: Results from One Master's-Level Teacher Preparation Program

ERIC Educational Resources Information Center

Evans, Carla M.

2017-01-01

This study investigates the predictive validity and policy impact of Council for Accreditation of Educator Preparation minimum admission requirements in Standard 3.2 on teacher preparation programs (TPPs), their applicants, and the broader field of educator preparation. Undergraduate grade point average (GPA) and Graduate Record Examination (GRE)…

EEG Estimates of Cognitive Workload and Engagement Predict Math Problem Solving Outcomes

ERIC Educational Resources Information Center

Beal, Carole R.; Galan, Federico Cirett

2012-01-01

In the present study, the authors focused on the use of electroencephalography (EEG) data about cognitive workload and sustained attention to predict math problem solving outcomes. EEG data were recorded as students solved a series of easy and difficult math problems. Sequences of attention and cognitive workload estimates derived from the EEG…

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing.

PubMed

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis G; Atkins, David C; Narayanan, Shrikanth S

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy-observational coding-has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies.

Real-time control of walking using recordings from dorsal root ganglia

NASA Astrophysics Data System (ADS)

Holinski, B. J.; Everaert, D. G.; Mushahwar, V. K.; Stein, R. B.

2013-10-01

Objective. The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. Approach. In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback. Main results. Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. Significance. Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.

Real-time control of walking using recordings from dorsal root ganglia

PubMed Central

Holinski, B J; Everaert, D G; Mushahwar, V K; Stein, R B

2013-01-01

Objective The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. Approach In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the dorsal root ganglia. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modeled from recorded neural firing rates. These models were then used for closed-loop feedback. Main Results Overall, firing-rate based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48±13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. Significance Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development. PMID:23928579

Linear genetic programming application for successive-station monthly streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

2014-09-01

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

Prior nonhip limb fracture predicts subsequent hip fracture in institutionalized elderly people.

PubMed

Nakamura, K; Takahashi, S; Oyama, M; Oshiki, R; Kobayashi, R; Saito, T; Yoshizawa, Y; Tsuchiya, Y

2010-08-01

This 1-year cohort study of nursing home residents revealed that historical fractures of upper limbs or nonhip lower limbs were associated with hip fracture (hazard ratio = 2.14), independent of activities of daily living (ADL), mobility, dementia, weight, and type of nursing home. Prior nonhip fractures are useful for predicting of hip fracture in institutional settings. The aim of this study was to evaluate the utility of fracture history for the prediction of hip fracture in nursing home residents. This was a cohort study with a 1-year follow-up. Subjects were 8,905 residents of nursing homes in Niigata, Japan (mean age, 84.3 years). Fracture histories were obtained from nursing home medical records. ADL levels were assessed by caregivers. Hip fracture diagnosis was based on hospital medical records. Subjects had fracture histories of upper limbs (5.0%), hip (14.0%), and nonhip lower limbs (4.6%). Among historical single fractures, only prior nonhip lower limbs significantly predicted subsequent fracture (adjusted hazard ratio, 2.43; 95% confidence interval (CI), 1.30-4.57). The stepwise method selected the best model, in which a combined historical fracture at upper limbs or nonhip lower limbs (adjusted hazard ratio, 2.14; 95% CI, 1.30-3.52), dependence, ADL levels, mobility, dementia, weight, and type of nursing home independently predicted subsequent hip fracture. A fracture history at upper or nonhip lower limbs, in combination with other known risk factors, is useful for the prediction of future hip fracture in institutional settings.

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

NASA Astrophysics Data System (ADS)

Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

2017-07-01

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

Continuously growing rodent molars result from a predictable quantitative evolutionary change over 50 million years

PubMed Central

Mushegyan, Vagan; Eronen, Jussi T.; Lawing, A. Michelle; Sharir, Amnon; Janis, Christine; Jernvall, Jukka; Klein, Ophir D.

2015-01-01

Summary The fossil record is widely informative about evolution, but fossils are not systematically used to study the evolution of stem cell-driven renewal. Here, we examined evolution of the continuous growth (hypselodonty) of rodent molar teeth, which is fuelled by the presence of dental stem cells. We studied occurrences of 3500 North American rodent fossils, ranging from 50 million years ago (mya) to 2 mya. We examined changes in molar height to determine if evolution of hypselodonty shows distinct patterns in the fossil record, and we found that hypselodont taxa emerged through intermediate forms of increasing crown height. Next, we designed a Markov simulation model, which replicated molar height increases throughout the Cenozoic, and, moreover, evolution of hypselodonty. Thus, by extension, the retention of the adult stem-cell niche appears to be a predictable quantitative rather than a stochastic qualitative process. Our analyses predict that hypselodonty will eventually become the dominant phenotype. PMID:25921530

Validation of asthma recording in electronic health records: protocol for a systematic review.

PubMed

Nissen, Francis; Quint, Jennifer K; Wilkinson, Samantha; Mullerova, Hana; Smeeth, Liam; Douglas, Ian J

2017-05-29

Asthma is a common, heterogeneous disease with significant morbidity and mortality worldwide. It can be difficult to define in epidemiological studies using electronic health records as the diagnosis is based on non-specific respiratory symptoms and spirometry, neither of which are routinely registered. Electronic health records can nonetheless be valuable to study the epidemiology, management, healthcare use and control of asthma. For health databases to be useful sources of information, asthma diagnoses should ideally be validated. The primary objectives are to provide an overview of the methods used to validate asthma diagnoses in electronic health records and summarise the results of the validation studies. EMBASE and MEDLINE will be systematically searched for appropriate search terms. The searches will cover all studies in these databases up to October 2016 with no start date and will yield studies that have validated algorithms or codes for the diagnosis of asthma in electronic health records. At least one test validation measure (sensitivity, specificity, positive predictive value, negative predictive value or other) is necessary for inclusion. In addition, we require the validated algorithms to be compared with an external golden standard, such as a manual review, a questionnaire or an independent second database. We will summarise key data including author, year of publication, country, time period, date, data source, population, case characteristics, clinical events, algorithms, gold standard and validation statistics in a uniform table. This study is a synthesis of previously published studies and, therefore, no ethical approval is required. The results will be submitted to a peer-reviewed journal for publication. Results from this systematic review can be used to study outcome research on asthma and can be used to identify case definitions for asthma. CRD42016041798. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

PubMed

Wolfson, Julian; Bandyopadhyay, Sunayan; Elidrisi, Mohamed; Vazquez-Benitez, Gabriela; Vock, David M; Musgrove, Donald; Adomavicius, Gediminas; Johnson, Paul E; O'Connor, Patrick J

2015-09-20

Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system. Copyright © 2015 John Wiley & Sons, Ltd.

Seasonality of Human Leptospirosis in Reunion Island (Indian Ocean) and Its Association with Meteorological Data

PubMed Central

Desvars, Amélie; Jégo, Sylvaine; Chiroleu, Frédéric; Bourhy, Pascale; Cardinale, Eric; Michault, Alain

2011-01-01

Background Leptospirosis is a disease which occurs worldwide but particularly affects tropical areas. Transmission of the disease is dependent on its excretion by reservoir animals and the presence of moist environment which allows the survival of the bacteria. Methods and Findings A retrospective study was undertaken to describe seasonal patterns of human leptospirosis cases reported by the Centre National de Références des Leptospiroses (CNRL, Pasteur Institute, Paris) between 1998 and 2008, to determine if there was an association between the occurrence of diagnosed cases and rainfall, temperature and global solar radiation (GSR). Meteorological data were recorded in the town of Saint-Benoît (Météo France “Beaufonds-Miria” station), located on the windward (East) coast. Time-series analysis was used to identify the variables that best described and predicted the occurrence of cases of leptospirosis on the island. Six hundred and thirteen cases were reported during the 11-year study period, and 359 cases (58.56%) were diagnosed between February and May. A significant correlation was identified between the number of cases in a given month and the associated cumulated rainfall as well as the mean monthly temperature recorded 2 months prior to diagnosis (r = 0.28 and r = 0.23 respectively). The predictive model includes the number of cases of leptospirosis recorded 1 month prior to diagnosis (b = 0.193), the cumulated monthly rainfall recorded 2 months prior to diagnosis (b = 0.145), the average monthly temperature recorded 0 month prior to diagnosis (b = 3.836), and the average monthly GSR recorded 0 month prior to diagnosis (b = −1.293). Conclusions Leptospirosis has a seasonal distribution in Reunion Island. Meteorological data can be used to predict the occurrence of the disease and our statistical model can help to implement seasonal prevention measures. PMID:21655257

Prediction using patient comparison vs. modeling: a case study for mortality prediction.

PubMed

Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter

2016-08-01

Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.

Evoked potentials recorded during routine EEG predict outcome after perinatal asphyxia.

PubMed

Nevalainen, Päivi; Marchi, Viviana; Metsäranta, Marjo; Lönnqvist, Tuula; Toiviainen-Salo, Sanna; Vanhatalo, Sampsa; Lauronen, Leena

2017-07-01

To evaluate the added value of somatosensory (SEPs) and visual evoked potentials (VEPs) recorded simultaneously with routine EEG in early outcome prediction of newborns with hypoxic-ischemic encephalopathy under modern intensive care. We simultaneously recorded multichannel EEG, median nerve SEPs, and flash VEPs during the first few postnatal days in 50 term newborns with hypoxic-ischemic encephalopathy. EEG background was scored into five grades and the worst two grades were considered to indicate poor cerebral recovery. Evoked potentials were classified as absent or present. Clinical outcome was determined from the medical records at a median age of 21months. Unfavorable outcome included cerebral palsy, severe mental retardation, severe epilepsy, or death. The accuracy of outcome prediction was 98% with SEPs compared to 90% with EEG. EEG alone always predicted unfavorable outcome when it was inactive (n=9), and favorable outcome when it was normal or only mildly abnormal (n=17). However, newborns with moderate or severe EEG background abnormality could have either favorable or unfavorable outcome, which was correctly predicted by SEP in all but one newborn (accuracy in this subgroup 96%). Absent VEPs were always associated with an inactive EEG, and an unfavorable outcome. However, presence of VEPs did not guarantee a favorable outcome. SEPs accurately predict clinical outcomes in newborns with hypoxic-ischemic encephalopathy and improve the EEG-based prediction particularly in those newborns with severely or moderately abnormal EEG findings. SEPs should be added to routine EEG recordings for early bedside assessment of newborns with hypoxic-ischemic encephalopathy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

A strong-motion database from the Central American subduction zone

NASA Astrophysics Data System (ADS)

Arango, Maria Cristina; Strasser, Fleur O.; Bommer, Julian J.; Hernández, Douglas A.; Cepeda, Jose M.

2011-04-01

Subduction earthquakes along the Pacific Coast of Central America generate considerable seismic risk in the region. The quantification of the hazard due to these events requires the development of appropriate ground-motion prediction equations, for which purpose a database of recordings from subduction events in the region is indispensable. This paper describes the compilation of a comprehensive database of strong ground-motion recordings obtained during subduction-zone events in Central America, focusing on the region from 8 to 14° N and 83 to 92° W, including Guatemala, El Salvador, Nicaragua and Costa Rica. More than 400 accelerograms recorded by the networks operating across Central America during the last decades have been added to data collected by NORSAR in two regional projects for the reduction of natural disasters. The final database consists of 554 triaxial ground-motion recordings from events of moment magnitudes between 5.0 and 7.7, including 22 interface and 58 intraslab-type events for the time period 1976-2006. Although the database presented in this study is not sufficiently complete in terms of magnitude-distance distribution to serve as a basis for the derivation of predictive equations for interface and intraslab events in Central America, it considerably expands the Central American subduction data compiled in previous studies and used in early ground-motion modelling studies for subduction events in this region. Additionally, the compiled database will allow the assessment of the existing predictive models for subduction-type events in terms of their applicability for the Central American region, which is essential for an adequate estimation of the hazard due to subduction earthquakes in this region.

Usefulness of bowel sound auscultation: a prospective evaluation.

PubMed

Felder, Seth; Margel, David; Murrell, Zuri; Fleshner, Phillip

2014-01-01

Although the auscultation of bowel sounds is considered an essential component of an adequate physical examination, its clinical value remains largely unstudied and subjective. The aim of this study was to determine whether an accurate diagnosis of normal controls, mechanical small bowel obstruction (SBO), or postoperative ileus (POI) is possible based on bowel sound characteristics. Prospectively collected recordings of bowel sounds from patients with normal gastrointestinal motility, SBO diagnosed by computed tomography and confirmed at surgery, and POI diagnosed by clinical symptoms and a computed tomography without a transition point. Study clinicians were instructed to categorize the patient recording as normal, obstructed, ileus, or not sure. Using an electronic stethoscope, bowel sounds of healthy volunteers (n = 177), patients with SBO (n = 19), and patients with POI (n = 15) were recorded. A total of 10 recordings randomly selected from each category were replayed through speakers, with 15 of the recordings duplicated to surgical and internal medicine clinicians (n = 41) blinded to the clinical scenario. The sensitivity, positive predictive value, and intra-rater variability were determined based on the clinician's ability to properly categorize the bowel sound recording when blinded to additional clinical information. Secondary outcomes were the clinician's perceived level of expertise in interpreting bowel sounds. The overall sensitivity for normal, SBO, and POI recordings was 32%, 22%, and 22%, respectively. The positive predictive value of normal, SBO, and POI recordings was 23%, 28%, and 44%, respectively. Intra-rater reliability of duplicated recordings was 59%, 52%, and 53% for normal, SBO, and POI, respectively. No statistically significant differences were found between the surgical and internal medicine clinicians for sensitivity, positive predictive value, or intra-rater variability. Overall, 44% of clinicians reported that they rarely listened to bowel sounds, whereas 17% reported that they always listened. Auscultation of bowel sounds is not a useful clinical practice when differentiating patients with normal versus pathologic bowel sounds. The listener frequently arrives at an incorrect diagnosis. If routine abdominal auscultation is to be continued, our findings emphasize the need for improvements in training and education as well as advancements in the understanding of the objective acoustical properties of bowel sounds. Copyright © 2014 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

An empirical approach to predicting long term behavior of metal particle based recording media

NASA Technical Reports Server (NTRS)

Hadad, Allan S.

1992-01-01

Alpha iron particles used for magnetic recording are prepared through a series of dehydration and reduction steps of alpha-Fe2O3-H2O resulting in acicular, polycrystalline, body centered cubic (bcc) alpha-Fe particles that are single magnetic domains. Since fine iron particles are pyrophoric by nature, stabilization processes had to be developed in order for iron particles to be considered as a viable recording medium for long term archival (i.e., 25+ years) information storage. The primary means of establishing stability is through passivation or controlled oxidation of the iron particle's surface. A study was undertaken to examine the degradation in magnetic properties as a function of both temperature and humidity on silicon-containing iron particles between 50-120 C and 3-89 percent relative humidity. The methodology to which experimental data was collected and analyzed leading to predictive capability is discussed.

Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study

PubMed Central

Kohane, Isaac S; Mandl, Kenneth D

2009-01-01

Objective To determine whether longitudinal data in patients’ historical records, commonly available in electronic health record systems, can be used to predict a patient’s future risk of receiving a diagnosis of domestic abuse. Design Bayesian models, known as intelligent histories, used to predict a patient’s risk of receiving a future diagnosis of abuse, based on the patient’s diagnostic history. Retrospective evaluation of the model’s predictions using an independent testing set. Setting A state-wide claims database covering six years of inpatient admissions to hospital, admissions for observation, and encounters in emergency departments. Population All patients aged over 18 who had at least four years between their earliest and latest visits recorded in the database (561 216 patients). Main outcome measures Timeliness of detection, sensitivity, specificity, positive predictive values, and area under the ROC curve. Results 1.04% (5829) of the patients met the narrow case definition for abuse, while 3.44% (19 303) met the broader case definition for abuse. The model achieved sensitive, specific (area under the ROC curve of 0.88), and early (10-30 months in advance, on average) prediction of patients’ future risk of receiving a diagnosis of abuse. Analysis of model parameters showed important differences between sexes in the risks associated with certain diagnoses. Conclusions Commonly available longitudinal diagnostic data can be useful for predicting a patient’s future risk of receiving a diagnosis of abuse. This modelling approach could serve as the basis for an early warning system to help doctors identify high risk patients for further screening. PMID:19789406

Optimizing research in symptomatic uterine fibroids with development of a computable phenotype for use with electronic health records.

PubMed

Hoffman, Sarah R; Vines, Anissa I; Halladay, Jacqueline R; Pfaff, Emily; Schiff, Lauren; Westreich, Daniel; Sundaresan, Aditi; Johnson, La-Shell; Nicholson, Wanda K

2018-06-01

Women with symptomatic uterine fibroids can report a myriad of symptoms, including pain, bleeding, infertility, and psychosocial sequelae. Optimizing fibroid research requires the ability to enroll populations of women with image-confirmed symptomatic uterine fibroids. Our objective was to develop an electronic health record-based algorithm to identify women with symptomatic uterine fibroids for a comparative effectiveness study of medical or surgical treatments on quality-of-life measures. Using an iterative process and text-mining techniques, an effective computable phenotype algorithm, composed of demographics, and clinical and laboratory characteristics, was developed with reasonable performance. Such algorithms provide a feasible, efficient way to identify populations of women with symptomatic uterine fibroids for the conduct of large traditional or pragmatic trials and observational comparative effectiveness studies. Symptomatic uterine fibroids, due to menorrhagia, pelvic pain, bulk symptoms, or infertility, are a source of substantial morbidity for reproductive-age women. Comparing Treatment Options for Uterine Fibroids is a multisite registry study to compare the effectiveness of hormonal or surgical fibroid treatments on women's perceptions of their quality of life. Electronic health record-based algorithms are able to identify large numbers of women with fibroids, but additional work is needed to develop electronic health record algorithms that can identify women with symptomatic fibroids to optimize fibroid research. We sought to develop an efficient electronic health record-based algorithm that can identify women with symptomatic uterine fibroids in a large health care system for recruitment into large-scale observational and interventional research in fibroid management. We developed and assessed the accuracy of 3 algorithms to identify patients with symptomatic fibroids using an iterative approach. The data source was the Carolina Data Warehouse for Health, a repository for the health system's electronic health record data. In addition to International Classification of Diseases, Ninth Revision diagnosis and procedure codes and clinical characteristics, text data-mining software was used to derive information from imaging reports to confirm the presence of uterine fibroids. Results of each algorithm were compared with expert manual review to calculate the positive predictive values for each algorithm. Algorithm 1 was composed of the following criteria: (1) age 18-54 years; (2) either ≥1 International Classification of Diseases, Ninth Revision diagnosis codes for uterine fibroids or mention of fibroids using text-mined key words in imaging records or documents; and (3) no International Classification of Diseases, Ninth Revision or Current Procedural Terminology codes for hysterectomy and no reported history of hysterectomy. The positive predictive value was 47% (95% confidence interval 39-56%). Algorithm 2 required ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids and positive text-mined key words and had a positive predictive value of 65% (95% confidence interval 50-79%). In algorithm 3, further refinements included ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids on separate outpatient visit dates, the exclusion of women who had a positive pregnancy test within 3 months of their fibroid-related visit, and exclusion of incidentally detected fibroids during prenatal or emergency department visits. Algorithm 3 achieved a positive predictive value of 76% (95% confidence interval 71-81%). An electronic health record-based algorithm is capable of identifying cases of symptomatic uterine fibroids with moderate positive predictive value and may be an efficient approach for large-scale study recruitment. Copyright © 2018 Elsevier Inc. All rights reserved.

Machine learning of swimming data via wisdom of crowd and regression analysis.

PubMed

Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing

2017-04-01

Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.

Validity of International Classification of Diseases (ICD) coding for dengue infections in hospital discharge records in Malaysia.

PubMed

Woon, Yuan-Liang; Lee, Keng-Yee; Mohd Anuar, Siti Fatimah Zahra; Goh, Pik-Pin; Lim, Teck-Onn

2018-04-20

Hospitalization due to dengue illness is an important measure of dengue morbidity. However, limited studies are based on administrative database because the validity of the diagnosis codes is unknown. We validated the International Classification of Diseases, 10th revision (ICD) diagnosis coding for dengue infections in the Malaysian Ministry of Health's (MOH) hospital discharge database. This validation study involves retrospective review of available hospital discharge records and hand-search medical records for years 2010 and 2013. We randomly selected 3219 hospital discharge records coded with dengue and non-dengue infections as their discharge diagnoses from the national hospital discharge database. We then randomly sampled 216 and 144 records for patients with and without codes for dengue respectively, in keeping with their relative frequency in the MOH database, for chart review. The ICD codes for dengue were validated against lab-based diagnostic standard (NS1 or IgM). The ICD-10-CM codes for dengue had a sensitivity of 94%, modest specificity of 83%, positive predictive value of 87% and negative predictive value 92%. These results were stable between 2010 and 2013. However, its specificity decreased substantially when patients manifested with bleeding or low platelet count. The diagnostic performance of the ICD codes for dengue in the MOH's hospital discharge database is adequate for use in health services research on dengue.

Choice of Variables and Gender Differentiated Prediction within Selected Academic Programs. Research Report #105.

ERIC Educational Resources Information Center

Gamache, LeAnn M.; Novick, Melvin R.

The existence of differential prediction of two-year grade point average is reported for gender groups within programs of study at the University of Iowa. Academic records of all freshmen entering the University in 1978 in the fields of Business, Liberal Arts, Pre-Medicine, and those undecided as to major were analyzed with respect to American…

The habitual female offender inside: How psychopathic traits predict chronic prison violence.

PubMed

Thomson, Nicholas D; Towl, Graham J; Centifanti, Luna C M

2016-06-01

Psychopathy is considered one of the best predictors of violence and prison misconducts and is arguably an important clinical construct in the correctional setting. However, we tested whether psychopathy can be used to predict misconducts in prison environments for women as has been done for men. To date, few studies exist that examine and validate this association in female offender samples. The present study included 182 ethnically diverse female offenders. The aim was to prospectively predict violent and nonviolent misconducts over a 9-month period using official records of prior violent criminal history (e.g., homicide, manslaughter, assault), and self-report measures of psychopathy, impulsivity, and empathy. Using negative binomial regression, we found that past violent criminal history, and callous and antisocial psychopathic traits were predictors of violent misconducts, whereas antisocial psychopathic traits and impulsivity best predicted nonviolent misconducts. Although empathy was negatively associated with psychopathy it was not a significant predictor of violent or nonviolent misconducts. Statistical models, which included impulsivity, were considered the most parsimonious at predicting misconducts. Our findings demonstrate how risk-factors found to be reliable in male offender samples, such as psychopathic traits, impulsivity, and past violent criminal history, generalize to female offenders for predicting nonviolent and violent misconducts. One notable difference is the importance of callous psychopathic traits when predicting chronic violent misconducts by female offenders. In sum, there are more similarities in psychopathy and impulsivity than differences in the prediction of misconducts among men and women. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Flight assessment in patients with respiratory disease: hypoxic challenge testing vs. predictive equations.

PubMed

Martin, S E; Bradley, J M; Buick, J B; Bradbury, I; Elborn, J S

2007-06-01

Predictive equations have been proposed as a simpler alternative to hypoxic challenge testing (HCT) for determining the risk of in-flight hypoxia. To assess agreement between hypoxic challenge testing (HCT) and predictive equations for assessment of in-flight hypoxia. Retrospective study. Patients with chronic obstructive pulmonary disease (COPD) (n = 15), interstitial lung disease (ILD) (n = 15) and cystic fibrosis (CF) (n = 15) were studied. Spirometry was recorded prior to hypoxic inhalation and oxygen saturations (SpO2) were recorded before, after and during hypoxic inhalation. Blood gases were analysed before and after hypoxic inhalation and when SpO2 = 85%. An HCT was performed using the Ventimask method. The PaO2 at altitude was estimated for each group using four published predictive equations, which use values of PaO2 (ground) and lung function measurements to predict altitude PaO2. Results were interpreted using the BTS recommendations for prescription of in-flight oxygen post HCT. The Stuart Maxwell test of overall homogeneity was used to assess agreement between HCT results and each of the predictive equations. Ground PaO2 was significantly greater in patients with CF than either ILD or COPD (p < 0.05). PaO2 in all three groups significantly decreased following HCT. With the exception of equation 3, significantly fewer patients in each group would require in-flight O2 if prescription was based on HCT, compared to predictive equations (p < 0.05). Predictive equations considerably overestimate the need for in-flight O2, compared to HCT.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

PubMed Central

Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients. PMID:29904574

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

PubMed

Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

Predicting changes in hypertension control using electronic health records from a chronic disease management program

PubMed Central

Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

2014-01-01

Objective Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. Method In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. Results The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). Conclusions This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans. PMID:24045907

Predicting changes in hypertension control using electronic health records from a chronic disease management program.

PubMed

Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

2014-01-01

Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans.

Variability in wood-frame building damage using broad-band synthetic ground motions: a comparative numerical study with recorded motions

USGS Publications Warehouse

Pei, Shiling; van de Lindt, John W.; Hartzell, Stephen; Luco, Nicolas

2014-01-01

Earthquake damage to light-frame wood buildings is a major concern for North America because of the volume of this construction type. In order to estimate wood building damage using synthetic ground motions, we need to verify the ability of synthetically generated ground motions to simulate realistic damage for this structure type. Through a calibrated damage potential indicator, four different synthetic ground motion models are compared with the historically recorded ground motions at corresponding sites. We conclude that damage for sites farther from the fault (>20 km) is under-predicted on average and damage at closer sites is sometimes over-predicted.

Neonatal Nutrition Predicts Energy Balance in Young Adults Born Preterm at Very Low Birth Weight

PubMed Central

Matinolli, Hanna-Maria; Hovi, Petteri; Levälahti, Esko; Kaseva, Nina; Silveira, Patricia P.; Hemiö, Katri; Järvenpää, Anna-Liisa; Eriksson, Johan G.; Andersson, Sture; Lindström, Jaana; Männistö, Satu; Kajantie, Eero

2017-01-01

Epidemiological studies and animal models suggest that early postnatal nutrition and growth can influence adult health. However, few human studies have objective recordings of early nutrient intake. We studied whether nutrient intake and growth during the first 9 weeks after preterm birth with very low birth weight (VLBW, <1500 g) predict total energy intake, resting energy expenditure (REE), physical activity and food preferences in young adulthood. We collected daily nutritional intakes and weights during the initial hospital stay from hospital records for 127 unimpaired VLBW participants. At an average age 22.5 years, they completed a three-day food record and a physical activity questionnaire and underwent measurements of body composition (dual X-ray absorptiometry; n = 115 with adequate data) and REE (n = 92 with adequate data). We used linear regression and path analysis to investigate associations between neonatal nutrient intake and adult outcomes. Higher energy, protein and fat intakes during the first three weeks of life predicted lower relative (=per unit lean body mass) energy intake and relative REE in adulthood, independent of other pre- and neonatal factors. In path analysis, total effects of early nutrition and growth on relative energy intake were mostly explained by direct effects of early life nutrition. A path mediated by early growth reached statistical significance only for protein intake. There were no associations of neonatal intakes with physical activity or food preferences in adulthood. As a conclusion, higher intake of energy and nutrients during first three weeks of life of VLBW infants predicts energy balance after 20 years. This association is partly mediated through postnatal growth. PMID:29186804

Stochastic ground motion simulation

USGS Publications Warehouse

Rezaeian, Sanaz; Xiaodan, Sun; Beer, Michael; Kougioumtzoglou, Ioannis A.; Patelli, Edoardo; Siu-Kui Au, Ivan

2014-01-01

Strong earthquake ground motion records are fundamental in engineering applications. Ground motion time series are used in response-history dynamic analysis of structural or geotechnical systems. In such analysis, the validity of predicted responses depends on the validity of the input excitations. Ground motion records are also used to develop ground motion prediction equations(GMPEs) for intensity measures such as spectral accelerations that are used in response-spectrum dynamic analysis. Despite the thousands of available strong ground motion records, there remains a shortage of records for large-magnitude earthquakes at short distances or in specific regions, as well as records that sample specific combinations of source, path, and site characteristics.

User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

PubMed

Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung C

2018-01-01

Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation ( r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

Individual to Community-Level Faunal Responses to Environmental Change from a Marine Fossil Record of Early Miocene Global Warming

PubMed Central

Belanger, Christina L.

2012-01-01

Modern climate change has a strong potential to shift earth systems and biological communities into novel states that have no present-day analog, leaving ecologists with no observational basis to predict the likely biotic effects. Fossil records contain long time-series of past environmental changes outside the range of modern observation, which are vital for predicting future ecological responses, and are capable of (a) providing detailed information on rates of ecological change, (b) illuminating the environmental drivers of those changes, and (c) recording the effects of environmental change on individual physiological rates. Outcrops of Early Miocene Newport Member of the Astoria Formation (Oregon) provide one such time series. This record of benthic foraminiferal and molluscan community change from continental shelf depths spans a past interval environmental change (∼20.3-16.7 mya) during which the region warmed 2.1–4.5°C, surface productivity and benthic organic carbon flux increased, and benthic oxygenation decreased, perhaps driven by intensified upwelling as on the modern Oregon coast. The Newport Member record shows that (a) ecological responses to natural environmental change can be abrupt, (b) productivity can be the primary driver of faunal change during global warming, (c) molluscs had a threshold response to productivity change while foraminifera changed gradually, and (d) changes in bivalve body size and growth rates parallel changes in taxonomic composition at the community level, indicating that, either directly or indirectly through some other biological parameter, the physiological tolerances of species do influence community change. Ecological studies in modern and fossil records that consider multiple ecological levels, environmental parameters, and taxonomic groups can provide critical information for predicting future ecological change and evaluating species vulnerability. PMID:22558424

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data

PubMed Central

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2013-01-01

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. PMID:25309815

Confronting uncertainty in flood damage predictions

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Vogel, Kristin; Merz, Bruno

2015-04-01

Reliable flood damage models are a prerequisite for the practical usefulness of the model results. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005 and 2006, in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

Positive predictive value of infective endocarditis in the Danish National Patient Registry: a validation study.

PubMed

Østergaard, Lauge; Adelborg, Kasper; Sundbøll, Jens; Pedersen, Lars; Loldrup Fosbøl, Emil; Schmidt, Morten

2018-05-30

The positive predictive value of an infective endocarditis diagnosis is approximately 80% in the Danish National Patient Registry. However, since infective endocarditis is a heterogeneous disease implying long-term intravenous treatment, we hypothesiszed that the positive predictive value varies by length of hospital stay. A total of 100 patients with first-time infective endocarditis in the Danish National Patient Registry were identified from January 2010 - December 2012 at the University hospital of Aarhus and regional hospitals of Herning and Randers. Medical records were reviewed. We calculated the positive predictive value according to admission length, and separately for patients with a cardiac implantable electronic device and a prosthetic heart valve using the Wilson score method. Among the 92 medical records available for review, the majority of the patients had admission length ⩾2 weeks. The positive predictive value increased with length of admission. In patients with admission length <2 weeks the positive predictive value was 65% while it was 90% for admission length ⩾2 weeks. The positive predictive value was 81% for patients with a cardiac implantable electronic device and 87% for patients with a prosthetic valve. The positive predictive value of the infective endocarditis diagnosis in the Danish National Patient Registry is high for patients with admission length ⩾2 weeks. Using this algorithm, the Danish National Patient Registry provides a valid source for identifying infective endocarditis for research.

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing

PubMed Central

Xiao, Bo; Imel, Zac E.; Georgiou, Panayiotis G.; Atkins, David C.; Narayanan, Shrikanth S.

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy–observational coding–has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies. PMID:26630392

Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study.

PubMed

Oh, Ein; Yoo, Tae Keun; Park, Eun-Cheol

2013-09-13

Blindness due to diabetic retinopathy (DR) is the major disability in diabetic patients. Although early management has shown to prevent vision loss, diabetic patients have a low rate of routine ophthalmologic examination. Hence, we developed and validated sparse learning models with the aim of identifying the risk of DR in diabetic patients. Health records from the Korea National Health and Nutrition Examination Surveys (KNHANES) V-1 were used. The prediction models for DR were constructed using data from 327 diabetic patients, and were validated internally on 163 patients in the KNHANES V-1. External validation was performed using 562 diabetic patients in the KNHANES V-2. The learning models, including ridge, elastic net, and LASSO, were compared to the traditional indicators of DR. Considering the Bayesian information criterion, LASSO predicted DR most efficiently. In the internal and external validation, LASSO was significantly superior to the traditional indicators by calculating the area under the curve (AUC) of the receiver operating characteristic. LASSO showed an AUC of 0.81 and an accuracy of 73.6% in the internal validation, and an AUC of 0.82 and an accuracy of 75.2% in the external validation. The sparse learning model using LASSO was effective in analyzing the epidemiological underlying patterns of DR. This is the first study to develop a machine learning model to predict DR risk using health records. LASSO can be an excellent choice when both discriminative power and variable selection are important in the analysis of high-dimensional electronic health records.

Comparison of Medicare Claims vs. Physician Adjudication for Identifying Stroke Outcomes in the Women’s Health Initiative

PubMed Central

Lakshminarayan, Kamakshi; Larson, Joseph C.; Virnig, Beth; Fuller, Candace; Allen, Norrina Bai; Limacher, Marian; Winkelmayer, Wolfgang C.; Safford, Monika M.; Burwen, Dale R.

2014-01-01

Background and Purpose Many studies use medical record review for ascertaining outcomes. One large, longitudinal study, the Women’s Health Initiative (WHI) ascertains strokes using participant self-report and subsequent physician review of medical records. This is resource-intensive. Herein, we assess whether Medicare data can reliably assess stroke events in the WHI. Methods Subjects were WHI participants with fee-for-service Medicare. Four stroke definitions were created for Medicare data using discharge diagnoses in hospitalization claims. Definition 1: stroke codes in any position; Definition 2: primary position stroke codes; Definitions 3 & 4: hemorrhagic and ischemic stroke codes respectively. WHI data were randomly split into training (50%) and test sets. A concordance matrix was used to examine agreement between WHI and Medicare stroke diagnosis. A WHI stroke and a Medicare stroke were considered a match if they occurred within +/− 7 days of each other. Refined analyses excluded Medicare events where medical records were unavailable for comparison. Results Training data (n=24,428): There were 577 WHI strokes and 557 Medicare strokes using definition 1. Of these, 478 were a match. Algorithm performance: Specificity 99.7%; Negative Predictive Value 99.7%; Sensitivity 82.8%; Positive Predictive Value 85.8%; kappa 0.84. Performance was similar for test data. While specificity and negative predictive value exceeded 99%, sensitivity ranged from 75 to 88% and positive predictive value ranged from 80 to 90% across stroke definitions. Conclusion Medicare data appear useful for population-based stroke research; however the performance characteristics depend on the definition selected. PMID:24525955

Effect of soil conditions on predicted ground motion: Case study from Western Anatolia, Turkey

NASA Astrophysics Data System (ADS)

Gok, Elcin; Chávez-García, Francisco J.; Polat, Orhan

2014-04-01

We present a site effect study for the city of Izmir, Western Anatolia, Turkey. Local amplification was evaluated using state-of-practice tools. Ten earthquakes recorded at 16 sites were analysed using spectral ratios relative to a reference site, horizontal-to-vertical spectral ratios, and an inversion scheme of the Fourier amplitude spectra of the recorded S-waves. Seismic noise records were also used to estimate site effects. The different estimates are in good agreement among them, although a basic uncertainty of a factor of 2 seems difficult to decrease. We used our site effect estimates to predict ground motion in Izmir for a possible M6.5 earthquake close to the city using stochastic modelling. Site effects have a large impact on PSV (pseudospectral velocity), where local amplification increases amplitudes by almost a factor of 9 at 1 Hz relative to the firm ground condition. Our results allow identifying the neighbourhoods of Izmir where hazard mitigation measurements are a priority task and will also be useful for planning urban development.

Predicting Human Mobility Patterns in Marine Ecosystems: Entropy and Home Range Calculations Based on High-Resolution Fishing Vessel Tracking Data

NASA Astrophysics Data System (ADS)

Murawski, S. A.

2016-02-01

A number of recent studies have developed metrics of human mobility patterns based on georeferenced cell phone records. The studies generally indicate a high degree of predictability in human location and relatively narrow home ranges for most people. In marine ecosystems there are a number of important uses for such calculations including marine spatial planning and predicting the impacts of marine management options such as establishing marine protected areas (MPAs). In this study we use individual fishing vessel satellite tracking (VMS) records ( 30 million records) obtained from commercial reef fish fishing vessels in the Gulf of Mexico during 2006-2014. This period witnessed the establishment of a variety of new regulations including individual fishing quotas (IFQs) for snapper, grouper, and tilefish, establishment of spatial-area closures, and the temporary closure of as much as 85,000 nautical miles of productive fishing grounds associated with the Deepwater Horizon oil spill accident. Vessel positions were obtained, with a location frequency of one hour. From these VMS data we calculated three measures of entropy (degree of repeatability in spatial use), as well as calculated the axis of gyration (home range) for each vessel in the data set. These calculations were related to a variety of descriptor variables including vessel size, distance from home port to predominant fishing grounds, revenue generated on fishing trips, and fishing regulations. The applicability of these calculations to marine resource management applications is discussed.

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data.

PubMed

Kim, Jungmin; Park, Juyong; Lee, Wonjae

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.

Pile Driving

NASA Technical Reports Server (NTRS)

1987-01-01

Machine-oriented structural engineering firm TERA, Inc. is engaged in a project to evaluate the reliability of offshore pile driving prediction methods to eventually predict the best pile driving technique for each new offshore oil platform. Phase I Pile driving records of 48 offshore platforms including such information as blow counts, soil composition and pertinent construction details were digitized. In Phase II, pile driving records were statistically compared with current methods of prediction. Result was development of modular software, the CRIPS80 Software Design Analyzer System, that companies can use to evaluate other prediction procedures or other data bases.

Predicting healthcare trajectories from medical records: A deep learning approach.

PubMed

Pham, Trang; Tran, Truyen; Phung, Dinh; Venkatesh, Svetha

2017-05-01

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

The Role of Racial Identity, Academic Self-Concept, and Self-Esteem in the Prediction of Academic Outcomes for African American Students

ERIC Educational Resources Information Center

Awad, Germine H.

2007-01-01

The purpose of the present study was to examine the extent to which racial identity, academic self-concept, and self-esteem predict two types of academic outcomes, grade point average (GPA), and verbal Graduate Record Examination scores. Although grades and standardized test performance are often collapsed under the category of academic…

Non-linear transfer characteristics of stimulation and recording hardware account for spurious low-frequency artifacts during amplitude modulated transcranial alternating current stimulation (AM-tACS).

PubMed

Kasten, Florian H; Negahbani, Ehsan; Fröhlich, Flavio; Herrmann, Christoph S

2018-05-31

Amplitude modulated transcranial alternating current stimulation (AM-tACS) has been recently proposed as a possible solution to overcome the pronounced stimulation artifact encountered when recording brain activity during tACS. In theory, AM-tACS does not entail power at its modulating frequency, thus avoiding the problem of spectral overlap between brain signal of interest and stimulation artifact. However, the current study demonstrates how weak non-linear transfer characteristics inherent to stimulation and recording hardware can reintroduce spurious artifacts at the modulation frequency. The input-output transfer functions (TFs) of different stimulation setups were measured. Setups included recordings of signal-generator and stimulator outputs and M/EEG phantom measurements. 6 th -degree polynomial regression models were fitted to model the input-output TFs of each setup. The resulting TF models were applied to digitally generated AM-tACS signals to predict the frequency of spurious artifacts in the spectrum. All four setups measured for the study exhibited low-frequency artifacts at the modulation frequency and its harmonics when recording AM-tACS. Fitted TF models showed non-linear contributions significantly different from zero (all p < .05) and successfully predicted the frequency of artifacts observed in AM-signal recordings. Results suggest that even weak non-linearities of stimulation and recording hardware can lead to spurious artifacts at the modulation frequency and its harmonics. These artifacts were substantially larger than alpha-oscillations of a human subject in the MEG. Findings emphasize the need for more linear stimulation devices for AM-tACS and careful analysis procedures, taking into account low-frequency artifacts to avoid confusion with effects of AM-tACS on the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

PubMed

Afshar, Majid; Press, Valerie G; Robison, Rachel G; Kho, Abel N; Bandi, Sindhura; Biswas, Ashvini; Avila, Pedro C; Kumar, Harsha Vardhan Madan; Yu, Byung; Naureckas, Edward T; Nyenhuis, Sharmilee M; Codispoti, Christopher D

2017-10-13

Comprehensive, rapid, and accurate identification of patients with asthma for clinical care and engagement in research efforts is needed. The original development and validation of a computable phenotype for asthma case identification occurred at a single institution in Chicago and demonstrated excellent test characteristics. However, its application in a diverse payer mix, across different health systems and multiple electronic health record vendors, and in both children and adults was not examined. The objective of this study is to externally validate the computable phenotype across diverse Chicago institutions to accurately identify pediatric and adult patients with asthma. A cohort of 900 asthma and control patients was identified from the electronic health record between January 1, 2012 and November 30, 2014. Two physicians at each site independently reviewed the patient chart to annotate cases. The inter-observer reliability between the physician reviewers had a κ-coefficient of 0.95 (95% CI 0.93-0.97). The accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the computable phenotype were all above 94% in the full cohort. The excellent positive and negative predictive values in this multi-center external validation study establish a useful tool to identify asthma cases in in the electronic health record for research and care. This computable phenotype could be used in large-scale comparative-effectiveness trials.

How valid are self-reports of illness-related absence? Evidence from a university employee health management program.

PubMed

Jenkins, Kristi Rahrig

2014-08-01

The present study uses a focused approach to compare self-reported versus administratively recorded measures of absences related to health or illness. To date, the few studies that focus on this topic produced mixed results. To help shed light on this issue, the present research has 2 related objectives: (1) examine how highly correlated self-reported and administratively recorded measures of absences related to health or illness might be, and (2) how each measure predicts various aspects of health. Using data from the 2012 StayWell® Health Management health risk appraisal (HRA) and 1 year (2011) of administratively recorded timekeeping data, bivariate analyses for continuous variables and generalized linear modeling for variables with greater than 2 response categories were used. For the multivariate analyses, linear regression models controlling for sex, age, race, income, job status, and campus location were calculated for the continuous outcomes (ie, self-rated health and chronic conditions). Results indicate that self-reported and administratively recorded absences related to health or illness were moderately correlated (correlation coefficient of 0.47). In addition, each measure functioned similarly (in direction and magnitude) to predict health outcomes. Both greater self-reported and recorded illness-related absenteeism was associated with poorer self-rated health and greater numbers of chronic conditions. These results suggest that self-rated illness-related absenteeism may be a reasonable way to assess various program outcomes meaningful to employers, particularly if administratively recorded measures are unavailable or too time consuming or expensive to analyze.

Predictive modeling of structured electronic health records for adverse drug event detection.

PubMed

Zhao, Jing; Henriksson, Aron; Asker, Lars; Boström, Henrik

2015-01-01

The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and combined. We have demonstrated how machine learning can be applied to electronic health records for the purpose of detecting adverse drug events and proposed solutions to some of the challenges this presents, including how to represent the various data types. Overall, clinical codes are more useful than measurements and, in specific cases, it is beneficial to combine the two.

Predictive modeling of structured electronic health records for adverse drug event detection

PubMed Central

2015-01-01

Background The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Methods Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Results Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and combined. Conclusions We have demonstrated how machine learning can be applied to electronic health records for the purpose of detecting adverse drug events and proposed solutions to some of the challenges this presents, including how to represent the various data types. Overall, clinical codes are more useful than measurements and, in specific cases, it is beneficial to combine the two. PMID:26606038

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

Post, Wilfred M; King, Anthony Wayne; Dragoni, Danilo

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties aremore » then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.« less

Sensitivity, specificity and predictive values of anterior chamber tap in cases of bacterial endophthalmitis.

PubMed

Sjoholm-Gomez de Liano, Carl; Soberon-Ventura, Vidal F; Salcedo-Villanueva, Guillermo; Santos-Palacios, Abril; Guerrero-Naranjo, Jose Luis; Fromow-Guerra, Jans; García-Aguirre, Gerardo; Morales-Canton, Virgilio; Velez-Montoya, Raul

2017-01-01

To assess the sensitivity, specificity, positive predictive value and negative predictive value of anterior chamber tap for the diagnosis of bacterial endophthalmitis on a population with high prevalence. Retrospective, single centre, case series study. We reviewed all medical records with clinical diagnosis of bacterial endophthalmitis in our hospital from January 1st, 2000 to December 31st 2014. From each record, we documented general demographic data, best corrected visual acuity and vitreous and aqueous tap microbiological results. All cases were further divided according to the endophthalmitis aetiology to perform individual calculations of sensitivity, specificity, positive predictive value, negative predictive value, accuracy and prevalence. We used the results of the vitreous tap as the gold standard for diagnosis of bacterial endophthalmitis. We excluded those records in which the aqueous and vitreous samples were not taken simultaneously or had an incomplete microbiological report. Significance were assessed with chi squared statistics, with an alpha value of 0.05 for statistical significance. A total of 190 cases fulfilled the inclusion/exclusion criteria. Positive culture rate from vitreous samples was 64.74%. Positive culture rate from aqueous sample was 32.11%. Bacteria isolated from aqueous samples matched those isolated from vitreous samples 78.68% of the time. The overall sensitivity was 38.21%, specificity: 75.51%, positive predictive value: 79.66%, negative predictive value: 32.74% ( p  = 0.08). Subgroup analysis showed that anterior chamber taps in cases of post-surgical endophthalmitis had a moderate to low sensitivity (37.73%), high specificity (93%) and high positive predictive value (95%) ( p  < 0.04). The sensitivity and specificity of anterior chamber tap are low and should not be used for critical therapeutic decisions in patients with suspected bacterial endophthalmitis. In cases of post-surgical endophthalmitis, the result of an anterior chamber tap could be used for therapeutic guidance, but only in conjunction with clinical presentation and in the absence of a better method for diagnosis.

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record.

PubMed

Harvey, H Benjamin; Liu, Catherine; Ai, Jing; Jaworsky, Cristina; Guerrier, Claude Emmanuel; Flores, Efren; Pianykh, Oleg

2017-10-01

To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination. Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve. Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination (P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality. Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Diagnosis and prediction of neuroendocrine liver metastases: a protocol of six systematic reviews.

PubMed

Arigoni, Stephan; Ignjatovic, Stefan; Sager, Patrizia; Betschart, Jonas; Buerge, Tobias; Wachtl, Josephine; Tschuor, Christoph; Limani, Perparim; Puhan, Milo A; Lesurtel, Mickael; Raptis, Dimitri A; Breitenstein, Stefan

2013-12-23

Patients with hepatic metastases from neuroendocrine tumors (NETs) benefit from an early diagnosis, which is crucial for the optimal therapy and management. Diagnostic procedures include morphological and functional imaging, identification of biomarkers, and biopsy. The aim of six systematic reviews discussed in this study is to assess the predictive value of Ki67 index and other biomarkers, to compare the diagnostic accuracy of morphological and functional imaging, and to define the role of biopsy in the diagnosis and prediction of neuroendocrine tumor liver metastases. An objective group of librarians will provide an electronic search strategy to examine the following databases: MEDLINE, EMBASE and The Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL), Database of Abstracts of Reviews of Effects). There will be no restriction concerning language and publication date. The qualitative and quantitative synthesis of the systematic review will be conducted with randomized controlled trials (RCT), prospective and retrospective comparative cohort studies, and case-control studies. Case series will be collected in a separate database and only used for descriptive purposes. This study is ongoing and presents a protocol of six systematic reviews to elucidate the role of histopathological and biochemical markers, biopsies of the primary tumor and the metastases as well as morphological and functional imaging modalities for the diagnosis and prediction of neuroendocrine liver metastases. These systematic reviews will assess the value and accuracy of several diagnostic modalities in patients with NET liver metastases, and will provide a basis for the development of clinical practice guidelines. The systematic reviews have been prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO): CRD42012002644; http://www.metaxis.com/prospero/full_doc.asp?RecordID=2644 (Archived by WebCite at http://www.webcitation.org/6LzCLd5sF), CRD42012002647; http://www.metaxis.com/prospero/full_doc.asp?RecordID=2647 (Archived by WebCite at http://www.webcitation.org/6LzCRnZnO), CRD42012002648; http://www.metaxis.com/prospero/full_doc.asp?RecordID=2648 (Archived by WebCite at http://www.webcitation.org/6LzCVeuVR), CRD42012002649; http://www.metaxis.com/prospero/full_doc.asp?RecordID=2649 (Archived by WebCite at http://www.webcitation.org/6LzCZzZWU), CRD42012002650; http://www.metaxis.com/prospero/full_doc.asp?RecordID=2650 (Archived by WebCite at http://www.webcitation.org/6LzDPhGb8), CRD42012002651; http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012002651#.UrMglPRDuVo (Archived by WebCite at http://www.webcitation.org/6LzClCNff).

Linking melodic expectation to expressive performance timing and perceived musical tension.

PubMed

Gingras, Bruno; Pearce, Marcus T; Goodchild, Meghan; Dean, Roger T; Wiggins, Geraint; McAdams, Stephen

2016-04-01

This research explored the relations between the predictability of musical structure, expressive timing in performance, and listeners' perceived musical tension. Studies analyzing the influence of expressive timing on listeners' affective responses have been constrained by the fact that, in most pieces, the notated durations limit performers' interpretive freedom. To circumvent this issue, we focused on the unmeasured prelude, a semi-improvisatory genre without notated durations. In Experiment 1, 12 professional harpsichordists recorded an unmeasured prelude on a harpsichord equipped with a MIDI console. Melodic expectation was assessed using a probabilistic model (IDyOM [Information Dynamics of Music]) whose expectations have been previously shown to match closely those of human listeners. Performance timing information was extracted from the MIDI data using a score-performance matching algorithm. Time-series analyses showed that, in a piece with unspecified note durations, the predictability of melodic structure measurably influenced tempo fluctuations in performance. In Experiment 2, another 10 harpsichordists, 20 nonharpsichordist musicians, and 20 nonmusicians listened to the recordings from Experiment 1 and rated the perceived tension continuously. Granger causality analyses were conducted to investigate predictive relations among melodic expectation, expressive timing, and perceived tension. Although melodic expectation, as modeled by IDyOM, modestly predicted perceived tension for all participant groups, neither of its components, information content or entropy, was Granger causal. In contrast, expressive timing was a strong predictor and was Granger causal. However, because melodic expectation was also predictive of expressive timing, our results outline a complete chain of influence from predictability of melodic structure via expressive performance timing to perceived musical tension. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

An empirical approach to improving tidal predictions using recent real-time tide gauge data

NASA Astrophysics Data System (ADS)

Hibbert, Angela; Royston, Samantha; Horsburgh, Kevin J.; Leach, Harry

2014-05-01

Classical harmonic methods of tidal prediction are often problematic in estuarine environments due to the distortion of tidal fluctuations in shallow water, which results in a disparity between predicted and observed sea levels. This is of particular concern in the Bristol Channel, where the error associated with tidal predictions is potentially greater due to an unusually large tidal range of around 12m. As such predictions are fundamental to the short-term forecasting of High Water (HW) extremes, it is vital that alternative solutions are found. In a pilot study, using a year-long observational sea level record from the Port of Avonmouth in the Bristol Channel, the UK National Tidal and Sea Level Facility (NTSLF) tested the potential for reducing tidal prediction errors, using three alternatives to the Harmonic Method of tidal prediction. The three methods evaluated were (1) the use of Artificial Neural Network (ANN) models, (2) the Species Concordance technique and (3) a simple empirical procedure for correcting Harmonic Method High Water predictions based upon a few recent observations (referred to as the Empirical Correction Method). This latter method was then successfully applied to sea level records from an additional 42 of the 45 tide gauges that comprise the UK Tide Gauge Network. Consequently, it is to be incorporated into the operational systems of the UK Coastal Monitoring and Forecasting Partnership in order to improve short-term sea level predictions for the UK and in particular, the accurate estimation of HW extremes.

Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador.

PubMed

Lowe, Rachel; Stewart-Ibarra, Anna M; Petrova, Desislava; García-Díez, Markel; Borbor-Cordova, Mercy J; Mejía, Raúl; Regato, Mary; Rodó, Xavier

2017-07-01

El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record. We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information. The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016. This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. European Union FP7, Royal Society, and National Science Foundation. Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Prediction processes during multiple object tracking (MOT): involvement of dorsal and ventral premotor cortices

PubMed Central

Atmaca, Silke; Stadler, Waltraud; Keitel, Anne; Ott, Derek V M; Lepsien, Jöran; Prinz, Wolfgang

2013-01-01

Background The multiple object tracking (MOT) paradigm is a cognitive task that requires parallel tracking of several identical, moving objects following nongoal-directed, arbitrary motion trajectories. Aims The current study aimed to investigate the employment of prediction processes during MOT. As an indicator for the involvement of prediction processes, we targeted the human premotor cortex (PM). The PM has been repeatedly implicated to serve the internal modeling of future actions and action effects, as well as purely perceptual events, by means of predictive feedforward functions. Materials and methods Using functional magnetic resonance imaging (fMRI), BOLD activations recorded during MOT were contrasted with those recorded during the execution of a cognitive control task that used an identical stimulus display and demanded similar attentional load. A particular effort was made to identify and exclude previously found activation in the PM-adjacent frontal eye fields (FEF). Results We replicated prior results, revealing occipitotemporal, parietal, and frontal areas to be engaged in MOT. Discussion The activation in frontal areas is interpreted to originate from dorsal and ventral premotor cortices. The results are discussed in light of our assumption that MOT engages prediction processes. Conclusion We propose that our results provide first clues that MOT does not only involve visuospatial perception and attention processes, but prediction processes as well. PMID:24363971

Detection of physiological noise in resting state fMRI using machine learning.

PubMed

Ash, Tom; Suckling, John; Walter, Martin; Ooi, Cinly; Tempelmann, Claus; Carpenter, Adrian; Williams, Guy

2013-04-01

We present a technique for predicting cardiac and respiratory phase on a time point by time point basis, from fMRI image data. These predictions have utility in attempts to detrend effects of the physiological cycles from fMRI image data. We demonstrate the technique both in the case where it can be trained on a subject's own data, and when it cannot. The prediction scheme uses a multiclass support vector machine algorithm. Predictions are demonstrated to have a close fit to recorded physiological phase, with median Pearson correlation scores between recorded and predicted values of 0.99 for the best case scenario (cardiac cycle trained on a subject's own data) down to 0.83 for the worst case scenario (respiratory predictions trained on group data), as compared to random chance correlation score of 0.70. When predictions were used with RETROICOR--a popular physiological noise removal tool--the effects are compared to using recorded phase values. Using Fourier transforms and seed based correlation analysis, RETROICOR is shown to produce similar effects whether recorded physiological phase values are used, or they are predicted using this technique. This was seen by similar levels of noise reduction noise in the same regions of the Fourier spectra, and changes in seed based correlation scores in similar regions of the brain. This technique has a use in situations where data from direct monitoring of the cardiac and respiratory cycles are incomplete or absent, but researchers still wish to reduce this source of noise in the image data. Copyright © 2011 Wiley Periodicals, Inc.

The Cost of Doing Business: Cost Structure of Electronic Immunization Registries

PubMed Central

Fontanesi, John M; Flesher, Don S; De Guire, Michelle; Lieberthal, Allan; Holcomb, Kathy

2002-01-01

Objective To predict the true cost of developing and maintaining an electronic immunization registry, and to set the framework for developing future cost-effective and cost-benefit analysis. Data Sources/Study Setting Primary data collected at three immunization registries located in California, accounting for 90 percent of all immunization records in registries in the state during the study period. Study Design A parametric cost analysis compared registry development and maintenance expenditures to registry performance requirements. Data Collection/Extraction Methods Data were collected at each registry through interviews, reviews of expenditure records, technical accomplishments development schedules, and immunization coverage rates. Principal Findings The cost of building immunization registries is predictable and independent of the hardware/software combination employed. The effort requires four man-years of technical effort or approximately $250,000 in 1998 dollars. Costs for maintaining a registry were approximately $5,100 per end user per three-year period. Conclusions There is a predictable cost structure for both developing and maintaining immunization registries. The cost structure can be used as a framework for examining the cost-effectiveness and cost-benefits of registries. The greatest factor effecting improvement in coverage rates was ongoing, user-based administrative investment. PMID:12479497

Effect of analgesia on the changes in respiratory parameters in blunt chest injury with multiple rib fractures.

PubMed

Ekpe, Eyo Effiong; Eyo, Catherine

2017-01-01

Blunt chest injury with multiple rib fractures can result in such complications as pneumonia, atelectasis, bronchiectasis, empyema thoracis, acute respiratory distress syndrome, and prolonged Intensive Care Unit and hospital stay, with its concomitant mortality. These may be prevented or reduced by good analgesic therapy which is the subject of this study. This was a prospective study of effects of analgesia on changes in pulmonary functions of patients with traumatic multiple rib fractures resulting from blunt chest injury. There were 64 adult patients who were studied with multiple rib fractures caused by blunt chest trauma. Of these patients, 54 (84.4%) were male and 10 (15.6%) were female. Motorcycle (popularly known as "okada") and tricycle (popularly known as keke napep) accidents significantly accounted for the majority of the multiple rib fractures, that is, in 50 (78.1%) of the patients. Before analgesic administration, no patient had a normal respiratory rate, but at 1 h following the administration of analgesic, 21 (32.8%) of patients recorded normal respiratory rates and there was a significant reduction in the number (10.9% vs. 39.1%) of patients with respiratory rates> 30 breaths/min. Before commencement of analgesic, no patient recorded up to 99% of oxygen saturation (SpO2) as measured by pulse oximeter, while 43.8% recorded SpO2of 96%. This improved after 1 h of administration of analgesics to SpO2of 100% in 18.8% of patients and 99% in 31.3% of patients and none recording SpO2of < 97% (P = 0.006). Before analgesia, no patient was able to achieve peak expiratory flow rate (PEFR) value> 100% of predicted while only 9 (14.1%) patients were able to achieve a PEFR value in the range of 91%-100% of predicted value. One hour after analgesia, a total of 6 (9.4%) patients were able to achieve PEFR values> 100% predicted, while 35 (54.7%) patients achieved PEFR values in the range of 91%-100% predicted. Adequate analgesia is capable of reversing the negative effects of chest pain of traumatic multiple rib fractures on pulmonary function parameters through improvement in respiratory mechanics.

What do we gain with Probabilistic Flood Loss Models?

NASA Astrophysics Data System (ADS)

Schroeter, K.; Kreibich, H.; Vogel, K.; Merz, B.; Lüdtke, S.

2015-12-01

The reliability of flood loss models is a prerequisite for their practical usefulness. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks and traditional stage damage functions which are cast in a probabilistic framework. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005, 2006 and 2013 in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

Evaluation of Voice Acoustics as Predictors of Clinical Depression Scores.

PubMed

Hashim, Nik Wahidah; Wilkes, Mitch; Salomon, Ronald; Meggs, Jared; France, Daniel J

2017-03-01

The aim of the present study was to determine if acoustic measures of voice, characterizing specific spectral and timing properties, predict clinical ratings of depression severity measured in a sample of patients using the Hamilton Depression Rating Scale (HAMD) and Beck Depression Inventory (BDI-II). This is a prospective study. Voice samples and clinical depression scores were collected prospectively from consenting adult patients who were referred to psychiatry from the adult emergency department or primary care clinics. The patients were audio-recorded as they read a standardized passage in a nearly closed-room environment. Mean Absolute Error (MAE) between actual and predicted depression scores was used as the primary outcome measure. The average MAE between predicted and actual HAMD scores was approximately two scores for both men and women, and the MAE for the BDI-II scores was approximately one score for men and eight scores for women. Timing features were predictive of HAMD scores in female patients while a combination of timing features and spectral features was predictive of scores in male patients. Timing features were predictive of BDI-II scores in male patients. Voice acoustic features extracted from read speech demonstrated variable effectiveness in predicting clinical depression scores in men and women. Voice features were highly predictive of HAMD scores in men and women, and BDI-II scores in men, respectively. The methodology is feasible for diagnostic applications in diverse clinical settings as it can be implemented during a standard clinical interview in a normal closed room and without strict control on the recording environment. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Pleasure to play, arousal to stay: the effect of player emotions on digital game preferences and playing time.

PubMed

Poels, Karolien; van den Hoogen, Wouter; Ijsselsteijn, Wijnand; de Kort, Yvonne

2012-01-01

This study investigated how player emotions during game-play, measured through self-report and physiological recordings, predict playing time and game preferences. We distinguished between short-term (immediately after game-play) and long-term (after 3 weeks) playing time and game preferences. While pleasure was most predictive for short-term playing time and game preferences, arousal, particularly for game preferences, was most predictive on the longer term. This result was found through both self-report and physiological emotion measures. This study initiates theorizing about digital gaming as a hedonic consumer product and sketches future research endeavors of this topic.

Evaluation of a method of estimating low-flow frequencies from base-flow measurements at Indiana streams

USGS Publications Warehouse

Wilson, John Thomas

2000-01-01

A mathematical technique of estimating low-flow frequencies from base-flow measurements was evaluated by using data for streams in Indiana. Low-flow frequencies at low- flow partial-record stations were estimated by relating base-flow measurements to concurrent daily flows at nearby streamflow-gaging stations (index stations) for which low-flowfrequency curves had been developed. A network of long-term streamflow-gaging stations in Indiana provided a sample of sites with observed low-flow frequencies. Observed values of 7-day, 10-year low flow and 7-day, 2-year low flow were compared to predicted values to evaluate the accuracy of the method. Five test cases were used to evaluate the method under a variety of conditions in which the location of the index station and its drainage area varied relative to the partial-record station. A total of 141 pairs of streamflow-gaging stations were used in the five test cases. Four of the test cases used one index station, the fifth test case used two index stations. The number of base-flow measurements was varied for each test case to see if the accuracy of the method was affected by the number of measurements used. The most accurate and least variable results were produced when two index stations on the same stream or tributaries of the partial-record station were used. All but one value of the predicted 7-day, 10-year low flow were within 15 percent of the values observed for the long-term continuous record, and all of the predicted values of the 7-day, 2-year lowflow were within 15 percent of the observed values. This apparent accuracy, to some extent, may be a result of the small sample set of 15. Of the four test cases that used one index station, the most accurate and least variable results were produced in the test case where the index station and partial-record station were on the same stream or on streams tributary to each other and where the index station had a larger drainage area than the partial-record station. In that test case, the method tended to over predict, based on the median relative error. In 23 of 28 test pairs, the predicted 7-day, 10-year low flow was within 15 percent of the observed value; in 26 of 28 test pairs, the predicted 7-day, 2-year low flow was within 15 percent of the observed value. When the index station and partial-record station were on the same stream or streams tributary to each other and the index station had a smaller drainage area than the partial-record station, the method tended to under predict the low-flow frequencies. Nineteen of 28 predicted values of the 7-day, 10-year low flow were within 15 percent of the observed values. Twenty-five of 28 predicted values of the 7-day, 2-year low flow were within 15 percent of the observed values. When the index station and the partial-record station were on different streams, the method tended to under predict regardless of whether the index station had a larger or smaller drainage area than that of the partial-record station. Also, the variability of the relative error of estimate was greatest for the test cases that used index stations and partial-record stations from different streams. This variability, in part, may be caused by using more streamflow-gaging stations with small low-flow frequencies in these test cases. A small difference in the predicted and observed values can equate to a large relative error when dealing with stations that have small low-flow frequencies. In the test cases that used one index station, the method tended to predict smaller low-flow frequencies as the number of base-flow measurements was reduced from 20 to 5. Overall, the average relative error of estimate and the variability of the predicted values increased as the number of base-flow measurements was reduced.

Compliance With Electronic Medical Records Privacy Policy: An Empirical Investigation of Hospital Information Technology Staff

PubMed Central

Sher, Ming-Ling; Talley, Paul C.; Yang, Ching-Wen; Kuo, Kuang-Ming

2017-01-01

The employment of Electronic Medical Records is expected to better enhance health care quality and to relieve increased financial pressure. Electronic Medical Records are, however, potentially vulnerable to security breaches that may result in a rise of patients’ privacy concerns. The purpose of our study was to explore the factors that motivate hospital information technology staff’s compliance with Electronic Medical Records privacy policy from the theoretical lenses of protection motivation theory and the theory of reasoned action. The study collected data using survey methodology. A total of 310 responses from information technology staff of 7 medical centers in Taiwan was analyzed using the Structural Equation Modeling technique. The results revealed that perceived vulnerability and perceived severity of threats from Electronic Medical Records breaches may be used to predict the information technology staff’s fear arousal level. And factors including fear arousal, response efficacy, self-efficacy, and subjective norm, in their turn, significantly predicted IT staff’s behavioral intention to comply with privacy policy. Response cost was not found to have any relationship with behavioral intention. Based on the findings, we suggest that hospitals could plan and design effective strategies such as initiating privacy-protection awareness and skills training programs to improve information technology staff member’s adherence to privacy policy. Furthermore, enhancing the privacy-protection climate in hospitals is also a viable means to the end. Further practical and research implications are also discussed.

Can outcome of pancreatic pseudocysts be predicted? Proposal for a new scoring system.

PubMed

Şenol, Kazım; Akgül, Özgür; Gündoğdu, Salih Burak; Aydoğan, İhsan; Tez, Mesut; Coşkun, Faruk; Tihan, Deniz Necdet

2016-03-01

The spontaneous resolution rate of pancreatic pseudocysts (PPs) is 86%, and the serious complication rate is 3-9%. The aim of the present study was to develop a scoring system that would predict spontaneous resolution of PPs. Medical records of 70 patients were retrospectively reviewed. Two patients were excluded. Demographic data and laboratory measurements were obtained from patient records. Mean age of the 68 patients included was 56.6 years. Female:male ratio was 1.34:1. Causes of pancreatitis were stones (48.5%), alcohol consumption (26.5%), and unknown etiology (25%). Mean size of PP was 71 mm. Pseudocysts disappeared in 32 patients (47.1%). With univariate analysis, serum direct bilirubin level (>0.95 mg/dL), cyst carcinoembryonic antigen (CEA) level (>1.5), and cyst diameter (>55 mm) were found to be significantly different between patients with and without spontaneous resolution. In multivariate analysis, these variables were statistically significant. Scores were calculated with points assigned to each variable. Final scores predicted spontaneous resolution in approximately 80% of patients. The scoring system developed to predict resolution of PPs is simple and useful, but requires validation.

Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering.

PubMed

Manoharan, Sujatha C; Ramakrishnan, Swaminathan

2009-10-01

In this work, prediction of forced expiratory volume in pulmonary function test, carried out using spirometry and neural networks is presented. The pulmonary function data were recorded from volunteers using commercial available flow volume spirometer in standard acquisition protocol. The Radial Basis Function neural networks were used to predict forced expiratory volume in 1 s (FEV1) from the recorded flow volume curves. The optimal centres of the hidden layer of radial basis function were determined by k-means clustering algorithm. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal, restrictive and obstructive cases. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases. Prediction accuracy was more in obstructive abnormality when compared to restrictive cases. It appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

PubMed

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

NASA Astrophysics Data System (ADS)

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Near-inertial Wave Studies Using Historical Mooring Records and a High-Resolution General Circulation Model

DTIC Science & Technology

2009-09-30

Mooring Records and a High- Resolution General Circulation Model Harper Simmons School of Fisheries and Ocean Sciences 903 Koyukuk Drive Fairbanks AK...oceanographic community has been to develop a global internal wave prediction system analogous to those already in place for surface waves. Early steps have... Fisheries and Ocean Sciences,903 Koyukuk Drive,Fairbanks,AK,99775 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND

Expressed Vocational Choices and Later Employment Compared with Vocational Preference Inventory and Kuder Preference Record-Vocational Scores

ERIC Educational Resources Information Center

Wiggins, J. D.; Weslander, Darrell

1977-01-01

Expressed vocational choices were more predictive of employment status four years after high school graduation for males than were scores on either the Vocational Preference Inventory or the Kuder Preference Record--Vocational. Predictions for males were more accurate than for females on all measures. (Author)

Temperature prediction of space flight experiments by computer thermal analysis

NASA Technical Reports Server (NTRS)

Birdsong, M. B.; Luttges, M. W.

1994-01-01

Life sciences experiments are especially sensitive to temperature. A small temperature difference between otherwise identical samples can cause various differences in biological reaction rates. Knowledge of experimental temperatures and temperature histories help to distinguish the effects of microgravity and temperature on spaceflight experiments compared to ground based studies, and allow appropriate controls and sensitivity tests. Up to the present time, the Orbiter (Space Shuttle) has not generally provided temperature measurement instrumentation inside ambient lockers located in the Mid-deck of the Orbiter, or inside similar facilities such as Spacehab and Spacelab, but many pieces of hardware do have temperature recording capability. Most of these temperatures, however, have only been roughly measured or estimated. Such reported experimental temperatures, while accurate within a range of several degrees Celsius, are of limited utility to biological researchers. The temperature controlled lockers used in spaceflight, such as Commerical-Refrigeration Incubation Modules (C-R/IMs), severely reduce the mass and volume available for test samples and do not necessarily provide uniform thermal environments. While these test carriers avoid some of the experimental temperature variations of the ambient lockers, the number of samples which can be accommodated in these temperature controlled units is limited. In the present work, improved models of thermal prediction and control were sought. Temperatures are predicted by thermal analysis software using empirical temperatures recorded during STS-57. These temperatures are compared to data recorded throughout the mission using Ambient Temperature Recorders (ATRs) located within several payload lockers. Additional test cases are undertaken using controlled ground experiments to more precisely determine the reliability of the thermal model. The approach presented should increase the utility of various spaceflight carriers in the support of biological and material science research and ground control studies done in preparation for flight.

Temperature prediction of space flight experiments by computer thermal analysis.

PubMed

Birdsong, M B; Luttges, M W

1995-02-01

Life sciences experiments are especially sensitive to temperature. A small temperature difference between otherwise identical samples can cause various differences in biological reaction rates. Knowledge of experimental temperatures and temperature histories help to distinguish the effects of microgravity and temperature on spaceflight experiments compared to ground based studies, and allow appropriate controls and sensitivity tests. Up to the present time, the Orbiter (Space Shuttle) has not generally provided temperature measurement instrumentation inside ambient lockers located in the Mid-deck of the Orbiter, or inside similar facilities such as Spacehab and Spacelab, but many pieces of hardware do have temperature recording capability. Most of these temperatures, however, have only been roughly measured or estimated. Such reported experimental temperatures, while accurate within a range of several degrees Celsius, are of limited utility to biological researchers. The temperature controlled lockers used in spaceflight, such as Commercial-Refrigeration Incubation Modules (C-R/IMs), severely reduce the mass and volume available for test samples and do not necessarily provide uniform thermal environments. While these test carriers avoid some of the experimental temperature variations of the ambient lockers, the number of samples which can be accommodated in these temperature controlled units is limited. In the present work, improved models of thermal prediction and control were sought. Temperatures are predicted by thermal analysis software using empirical temperatures recorded during STS-57. These temperatures are compared to data recorded throughout the mission using Ambient Temperature Recorders (ATRs) located within several payload lockers. Additional test cases are undertaken using controlled ground experiments to more precisely determine the reliability of the thermal model. The approach presented should increase the utility of various spaceflight carriers in the support of biological and material science research and ground control studies done in preparation for flight.

Chronic disease detection and access: does access improve detection, or does detection make access more difficult?

PubMed

Anwar, Mohammed Saqib; Baker, Richard; Walker, Nicola; Mainous, Arch G; Bankart, M John

2012-05-01

The recorded detection of chronic disease by practices is generally lower than the prevalence predicted by population surveys. To determine whether patient-reported access to general practice predicts the recorded detection rates of chronic diseases in that setting. A cross-sectional study involving 146 general practices in Leicestershire and Rutland, England. The numbers of patients recorded as having chronic disease (coronary heart disease, chronic obstructive pulmonary disease, hypertension, diabetes) were obtained from Quality and Outcomes Framework (QOF) practice disease registers for 2008-2009. Characteristics of practice populations (deprivation, age, sex, ethnicity, proportion reporting poor health, practice turnover, list size) and practice performance (achievement of QOF disease indicators, patient experience of being able to consult a doctor within 2 working days and book an appointment >2 days in advance) were included in regression models. Patient characteristics (deprivation, age, poor health) and practice characteristics (list size, turnover, QOF achievement) were associated with recorded detection of more than one of the chronic diseases. Practices in which patients were more likely to report being able to book appointments had reduced recording rates of chronic disease. Being able to consult a doctor within 2 days was not associated with levels of recorded chronic disease. Practices with high levels of deprivation and older patients have increased rates of recorded chronic disease. As the number of patients recorded with chronic disease increased, the capacity of practices to meet patients' requests for appointments in advance declined. The capacity of some practices to detect and manage chronic disease may need improving.

The chicken or the egg? A meta-analysis of panel studies of the relationship between work-family conflict and strain.

PubMed

Nohe, Christoph; Meier, Laurenz L; Sonntag, Karlheinz; Michel, Alexandra

2015-03-01

Does work-family conflict predict strain, does strain predict work-family conflict, or are they reciprocally related? To answer these questions, we used meta-analytic path analyses on 33 studies that had repeatedly measured work interference with family (WIF) or family interference with work (FIW) and strain. Additionally, this study sheds light on whether relationships between WIF/FIW and work-specific strain support the popular cross-domain perspective or the less popular matching perspective. Results showed reciprocal effects; that is, that WIF predicted strain (β = .08) and strain predicted WIF (β = .08). Similarly, FIW and strain were reciprocally related, such that FIW predicted strain (β = .03) and strain predicted FIW (β = .05). These findings held for both men and women and for different time lags between the 2 measurement waves. WIF had a stronger effect on work-specific strain than did FIW, supporting the matching hypothesis rather than the cross-domain perspective. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Comparing spatial regression to random forests for large ...

EPA Pesticide Factsheets

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

Prediction of earthquake ground motion at rock sites in Japan: evaluation of empirical and stochastic approaches for the PEGASOS Refinement Project

NASA Astrophysics Data System (ADS)

Edwards, Benjamin; Fäh, Donat

2017-11-01

Strong ground-motion databases used to develop ground-motion prediction equations (GMPEs) and calibrate stochastic simulation models generally include relatively few recordings on what can be considered as engineering rock or hard rock. Ground-motion predictions for such sites are therefore susceptible to uncertainty and bias, which can then propagate into site-specific hazard and risk estimates. In order to explore this issue we present a study investigating the prediction of ground motion at rock sites in Japan, where a wide range of recording-site types (from soil to very hard rock) are available for analysis. We employ two approaches: empirical GMPEs and stochastic simulations. The study is undertaken in the context of the PEGASOS Refinement Project (PRP), a Senior Seismic Hazard Analysis Committee (SSHAC) Level 4 probabilistic seismic hazard analysis of Swiss nuclear power plants, commissioned by swissnuclear and running from 2008 to 2013. In order to reduce the impact of site-to-site variability and expand the available data set for rock and hard-rock sites we adjusted Japanese ground-motion data (recorded at sites with 110 m s-1 < Vs30 < 2100 m s-1) to a common hard-rock reference. This was done through deconvolution of: (i) empirically derived amplification functions and (ii) the theoretical 1-D SH amplification between the bedrock and surface. Initial comparison of a Japanese GMPE's predictions with data recorded at rock and hard-rock sites showed systematic overestimation of ground motion. A further investigation of five global GMPEs' prediction residuals as a function of quarter-wavelength velocity showed that they all presented systematic misfit trends, leading to overestimation of median ground motions at rock and hard-rock sites in Japan. In an alternative approach, a stochastic simulation method was tested, allowing the direct incorporation of site-specific Fourier amplification information in forward simulations. We use an adjusted version of the model developed for Switzerland during the PRP. The median simulation prediction at true rock and hard-rock sites (Vs30 > 800 m s-1) was found to be comparable (within expected levels of epistemic uncertainty) to predictions using an empirical GMPE, with reduced residual misfit. As expected, due to including site-specific information in the simulations, the reduction in misfit could be isolated to a reduction in the site-related within-event uncertainty. The results of this study support the use of finite or pseudo-finite fault stochastic simulation methods in estimating strong ground motions in regions of weak and moderate seismicity, such as central and northern Europe. Furthermore, it indicates that weak-motion data has the potential to allow estimation of between- and within-site variability in ground motion, which is a critical issue in site-specific seismic hazard analysis, particularly for safety critical structures.

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

PubMed

Samad, Manar D; Ulloa, Alvaro; Wehner, Gregory J; Jing, Linyuan; Hartzel, Dustin; Good, Christopher W; Williams, Brent A; Haggerty, Christopher M; Fornwalt, Brandon K

2018-06-09

The goal of this study was to use machine learning to more accurately predict survival after echocardiography. Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data. Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. We investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant International Classification of Diseases, Tenth Revision, codes, and age, sex, height, weight, heart rate, blood pressures, low-density lipoprotein, high-density lipoprotein, and smoking; 2) clinical variables plus physician-reported EF; and 3) clinical variables and EF, plus 57 additional echocardiographic measurements. Missing data were imputed with a multivariate imputation by using a chained equations algorithm (MICE). We compared models versus each other and baseline clinical scoring systems by using a mean area under the curve (AUC) over 10 cross-validation folds and across 10 survival durations (6 to 60 months). Machine learning models achieved significantly higher prediction accuracy (all AUC >0.82) over common clinical risk scores (AUC = 0.61 to 0.79), with the nonlinear random forest models outperforming logistic regression (p < 0.01). The random forest model including all echocardiographic measurements yielded the highest prediction accuracy (p < 0.01 across all models and survival durations). Only 10 variables were needed to achieve 96% of the maximum prediction accuracy, with 6 of these variables being derived from echocardiography. Tricuspid regurgitation velocity was more predictive of survival than LVEF. In a subset of studies with complete data for the top 10 variables, multivariate imputation by chained equations yielded slightly reduced predictive accuracies (difference in AUC of 0.003) compared with the original data. Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes.

PubMed

Gabbett, Tim J

2010-10-01

Limited information exists on the training dose-response relationship in elite collision sport athletes. In addition, no study has developed an injury prediction model for collision sport athletes. The purpose of this study was to develop an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. Ninety-one professional rugby league players participated in this 4-year prospective study. This study was conducted in 2 phases. Firstly, training load and injury data were prospectively recorded over 2 competitive seasons in elite collision sport athletes. Training load and injury data were modeled using a logistic regression model with a binomial distribution (injury vs. no injury) and logit link function. Secondly, training load and injury data were prospectively recorded over a further 2 competitive seasons in the same cohort of elite collision sport athletes. An injury prediction model based on planned and actual training loads was developed and implemented to determine if noncontact, soft-tissue injuries could be predicted and therefore prevented in elite collision sport athletes. Players were 50-80% likely to sustain a preseason injury within the training load range of 3,000-5,000 units. These training load 'thresholds' were considerably reduced (1,700-3,000 units) in the late-competition phase of the season. A total of 159 noncontact, soft-tissue injuries were sustained over the latter 2 seasons. The percentage of true positive predictions was 62.3% (n = 121), whereas the total number of false positive and false negative predictions was 20 and 18, respectively. Players that exceeded the training load threshold were 70 times more likely to test positive for noncontact, soft-tissue injury, whereas players that did not exceed the training load threshold were injured 1/10 as often. These findings provide information on the training dose-response relationship and a scientific method of monitoring and regulating training load in elite collision sport athletes.

Roles of Clinician, Patient, and Community Characteristics in the Management of Pediatric Upper Respiratory Tract Infections.

PubMed

Yaeger, Jeffrey P; Temte, Jonathan L; Hanrahan, Lawrence P; Martinez-Donate, P

2015-11-01

Prior studies have evaluated factors predictive of inappropriate antibiotic prescription for upper respiratory tract infections (URIs). Community factors, however, have not been examined. The aim of this study was to evaluate the roles of patient, clinician, and community factors in predicting appropriate management of URIs in children. We used a novel database exchange, linking electronic health record data with community statistics, to identify all patients aged 3 months to 18 years in whom URI was diagnosed in the period from 2007 to 2012. We followed the Healthcare Effectiveness Data and Information Set (HEDIS) quality measurement titled "Appropriate treatment for children with upper respiratory infection" to determine the rate of appropriate management of URIs. We then stratified data across individual and community characteristics and used multiple logistic regression modeling to identify variables that independently predicted antibiotic prescription. Of 20,581 patients, the overall rate for appropriate management for URI was 93.5%. Family medicine clinicians (AOR = 1.5; 95% CI 1.31, 1.71; reference = pediatric clinicians), urgent care clinicians (AOR = 2.23; 95% CI 1.93, 2.57; reference = pediatric clinicians), patients aged 12 to 18 years (AOR = 1.44; 95% CI 1.25, 1.67; reference = age 3 months to 4 years), and patients of white race/ ethnicity (AOR = 1.83; 95% CI 1.41, 2.37; reference = black non-Hispanic) were independently predictive of antibiotic prescription. No community factors were independently predictive of antibiotic prescription. Results correlate with prior studies in which non-pediatric clinicians and white race/ethnicity were predictive of antibiotic prescription, while association with older patient age has not been previously reported. Findings illustrate the promise of linking electronic health records with community data to evaluate health care disparities. © 2015 Annals of Family Medicine, Inc.

Historical citizen science to understand and predict climate-driven trout decline

PubMed Central

Ninyerola, Miquel; Hermoso, Virgilio; Filipe, Ana Filipa; Pla, Magda; Villero, Daniel; Brotons, Lluís; Delibes, Miguel

2017-01-01

Historical species records offer an excellent opportunity to test the predictive ability of range forecasts under climate change, but researchers often consider that historical records are scarce and unreliable, besides the datasets collected by renowned naturalists. Here, we demonstrate the relevance of biodiversity records developed through citizen-science initiatives generated outside the natural sciences academia. We used a Spanish geographical dictionary from the mid-nineteenth century to compile over 10 000 freshwater fish records, including almost 4 000 brown trout (Salmo trutta) citations, and constructed a historical presence–absence dataset covering over 2 000 10 × 10 km cells, which is comparable to present-day data. There has been a clear reduction in trout range in the past 150 years, coinciding with a generalized warming. We show that current trout distribution can be accurately predicted based on historical records and past and present values of three air temperature variables. The models indicate a consistent decline of average suitability of around 25% between 1850s and 2000s, which is expected to surpass 40% by the 2050s. We stress the largely unexplored potential of historical species records from non-academic sources to open new pathways for long-term global change science. PMID:28077766

Predictors of Attrition and Academic Success of Medical Students: A 30-Year Retrospective Study

PubMed Central

Maslov Kruzicevic, Silvija; Barisic, Katarina Josipa; Banozic, Adriana; Esteban, Carlos David; Sapunar, Damir; Puljak, Livia

2012-01-01

Aim To determine attrition and predictors of academic success among medical students at University of Split, Croatia. Methods We analysed academic records of 2054 students enrolled during 1979–2008 period. Results We found that 26% (533/2054) of enrolled students did not graduate. The most common reasons for attrition were ‘personal’ (36.4%), transfer to another medical school (35.6%), and dismissal due to unsatisfactory academic record (21.2%). Grade point average (GPA) and study duration of attrition students were significantly associated with parental education. There were 1126 graduates, 395 men and 731 women. Their average graduation GPA was 3.67±0.53 and study duration 7.6±2.44 years. During 5-year curriculum only 6.4% (42/654) of students graduated in time, and 55% (240/472) of students graduated in time after curriculum was extended to 6 years. Variables predicting whether a student will graduate or not were high school grades, entrance exam score and year of enrollment. Significant predictors of graduation grades were high school grades and entrance exam score. Entrance exam score predicted length of studying. Conclusion Preadmission academic qualifications and year of enrollment predict academic success in medical school. More attention should be devoted to high attrition. PMID:22737228

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks

PubMed Central

Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun

2015-01-01

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction. PMID:25714125

Changing physiological status predicts severe injury and need for specialized trauma center resources.

PubMed

Talbert, Steven

2009-01-01

This study evaluated the association between changing physiological status (delta data) with severe injury (SI) or need for trauma center resources (TCR). Prehospital and emergency department arrival weighted RTS (RTSw) were computed for patients with complete records entered into the registry from 2002 to 2004 (n = 23,753). Physiological change was classified as unchanged, deteriorated, or improved (PreRTSw vs EDRTSw). Performance of delta data was evaluated using standard epidemiological approaches and multiple logistic regression. Deterioration status predicted SI (operating room [OR] = 1.38) and TCR (OR = 2.09). Improved status predicted TCR (OR = 1.27). Delta data independently predicted both SI and TCR.

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values.

PubMed

Přibyl, J; Bauer, J; Čermák, V; Pešek, P; Přibylová, J; Šplíchal, J; Vostrá-Vydrová, H; Vostrý, L; Zavadilová, L

2015-10-01

Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP - Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.

Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models.

PubMed

Ramírez-Albores, Jorge E; Bustamante, Ramiro O; Badano, Ernesto I

2016-01-01

Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of naturally established individuals because this improves the accuracy of predictions about their distribution ranges.

Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models

PubMed Central

Ramírez-Albores, Jorge E.; Bustamante, Ramiro O.

2016-01-01

Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of naturally established individuals because this improves the accuracy of predictions about their distribution ranges. PMID:27195983

Enduring Risk? Old Criminal Records and Predictions of Future Criminal Involvement

ERIC Educational Resources Information Center

Kurlychek, Megan C.; Brame, Robert; Bushway, Shawn D.

2007-01-01

It is well accepted that criminal records impose collateral consequences on offenders. Such records affect access to public housing, student financial aid, welfare benefits, and voting rights. An axiom of these policies is that individuals with criminal records--even old criminal records--exhibit significantly higher risk of future criminal…

Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records

PubMed Central

Weiss, Jeremy C; Page, David; Peissig, Peggy L; Natarajan, Sriraam; McCarty, Catherine

2013-01-01

Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices. PMID:25360347

Near-term prediction of sudden cardiac death in older hemodialysis patients using electronic health records.

PubMed

Goldstein, Benjamin A; Chang, Tara I; Mitani, Aya A; Assimes, Themistocles L; Winkelmayer, Wolfgang C

2014-01-01

Sudden cardiac death is the most common cause of death among individuals undergoing hemodialysis. The epidemiology of sudden cardiac death has been well studied, and efforts are shifting to risk assessment. This study aimed to test whether assessment of acute changes during hemodialysis that are captured in electronic health records improved risk assessment. Data were collected from all hemodialysis sessions of patients 66 years and older receiving hemodialysis from a large national dialysis provider between 2004 and 2008. The primary outcome of interest was sudden cardiac death the day of or day after a dialysis session. This study used data from 2004 to 2006 as the training set and data from 2007 to 2008 as the validation set. The machine learning algorithm, Random Forests, was used to derive the prediction model. In 22 million sessions, 898 people between 2004 and 2006 and 826 people between 2007 and 2008 died on the day of or day after a dialysis session that was serving as a training or test data session, respectively. A reasonably strong predictor was derived using just predialysis information (concordance statistic=0.782), which showed modest but significant improvement after inclusion of postdialysis information (concordance statistic=0.799, P<0.001). However, risk prediction decreased the farther out that it was forecasted (up to 1 year), and postdialytic information became less important. Subtle changes in the experience of hemodialysis aid in the assessment of sudden cardiac death and are captured by modern electronic health records. The collected data are better for the assessment of near-term risk as opposed to longer-term risk.

Reliability of astronomical records in the Nihongi

NASA Astrophysics Data System (ADS)

Kawabata, Kin-Aki; Tanikawa, Kiyotaka; Soma, Mitsuru

2002-03-01

Records of solar and lunar eclipses and occultations of stars in the Nihongi have been investigated to show their usefulness in answering questions about the long term variability of the Earth's rate of rotation. Results show that reliability of these records depend on the volume of the Nihongi and records in β group of volumes in the classification by H. Mori based on Chinese characters employed as phonetic letters, i.e. Vol. 22 (Empress Suiko), Vol. 23 (Emperor Jomei), and Vol. 29 (Emperor Tenmu), are highly reliable for these studies. Studies of solar eclipses recorded as total eclipses in the Nihongi and the Suishu and an occultation of Mars recorded in the Nihongi show that good agreements can be obtained between descriptions in these Japanese and Chinese historical books and calculations when we adopt TT-UT=3000 sec with correction for tidal term -2.0"/cy2 in the 7th century. Descriptions of solar and lunar eclipses recorded in Vol. 24 (Empress Kogyoku) and Vol. 30 (Empress Jito) are not based on observations but on theoretical predictions. All records of comets, aurorae, volcanic explosions, earthquakes, and tsunami in the Nihongi are described in β group volumes.

Electronic health record use among cancer patients: Insights from the Health Information National Trends Survey.

PubMed

Strekalova, Yulia A

2017-04-01

Over 90% of US hospitals provide patients with access to e-copy of their health records, but the utilization of electronic health records by the US consumers remains low. Guided by the comprehensive information-seeking model, this study used data from the National Cancer Institute's Health Information National Trends Survey 4 (Cycle 4) and examined the factors that explain the level of electronic health record use by cancer patients. Consistent with the model, individual information-seeking factors and perceptions of security and utility were associated with the frequency of electronic health record access. Specifically, higher income, prior online information seeking, interest in accessing health information online, and normative beliefs were predictive of electronic health record access. Conversely, poorer general health status and lack of health care provider encouragement to use electronic health records were associated with lower utilization rates. The current findings provide theory-based evidence that contributes to the understanding of the explanatory factors of electronic health record use and suggest future directions for research and practice.

Ten Years on: Does Graduate Student Promise Predict Later Scientific Achievement?

ERIC Educational Resources Information Center

Haslam, Nick; Laham, Simon M.

2009-01-01

We examined publication records of 60 social psychologists to determine whether publication record at the time of the PhD (t0) predicted scientific achievement (publication quantity, quality, and impact) ten years later (t10). Publication quantity and quality each correlated moderately across this time-span. Productivity and impact at t10 were…

Preliminary genomic predictions of feed saved for 1.4 million Holsteins

USDA-ARS?s Scientific Manuscript database

Genomic predictions of transmitting ability (GPTAs) for residual feed intake (RFI) were computed using data from 4,621 42-day and 202 28-day feed intake trials of 3,947 U.S. Holsteins born 1999-2013 in 9 research herds. The 28-day records had 8.5% larger error variance than 42-day records and receiv...

Data Mining and Predictive Modeling in Institutional Advancement: How Ten Schools Found Success. Technical Report

ERIC Educational Resources Information Center

Luperchio, Dan

2009-01-01

This technical report, produced in partnership by the Council for Advancement and Support of Education (CASE) and SPSS Inc., explores the promise of data mining alumni records at educational institutions. Working with individual alumni records from The Johns Hopkins Zanvyl Krieger School of Arts and Sciences, a predictive regression model is…

Predictors of Persistence in Online Graduate Nursing Students

ERIC Educational Resources Information Center

Cauble, Denise

2015-01-01

Persistence is an important measure of success for individual students and institutions of higher learning. The purpose of this study was to explore personal and academic factors that influence persistence in online graduate nursing students. A predictive correlational study design was used. Data were extracted from existing student records in two…

Investigating Academic Success Factors for Undergraduate Business Students

ERIC Educational Resources Information Center

Kaighobadi, Mehdi; Allen, Marcus T.

2008-01-01

Student academic performance is of major interest to all stakeholders of higher education institutions. This study questions whether or not statistical analysis of information that is readily available in most universities' official records system can be used to predict overall academic success. In particular, this study is an attempt to…

Predicting reading and mathematics from neural activity for feedback learning.

PubMed

Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A

2017-01-01

Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task predicted reading and mathematics performance 2 years later. The results indicated that feedback learning performance predicted both reading and mathematics performance. Activity during feedback learning in left superior dorsolateral prefrontal cortex (DLPFC) predicted reading performance, whereas activity in presupplementary motor area/anterior cingulate cortex (pre-SMA/ACC) predicted mathematical performance. Moreover, left superior DLPFC and pre-SMA/ACC activity predicted unique variance in reading and mathematics ability over behavioral testing of feedback learning performance alone. These results provide valuable insights into the relationship between laboratory-based learning tasks and learning in school settings, and the value of neural assessments for prediction of school performance over behavioral testing alone. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Prediction is Production: The missing link between language production and comprehension.

PubMed

Martin, Clara D; Branzi, Francesca M; Bar, Moshe

2018-01-18

Language comprehension often involves the generation of predictions. It has been hypothesized that such prediction-for-comprehension entails actual language production. Recent studies provided evidence that the production system is recruited during language comprehension, but the link between production and prediction during comprehension remains hypothetical. Here, we tested this hypothesis by comparing prediction during sentence comprehension (primary task) in participants having the production system either available or not (non-verbal versus verbal secondary task). In the primary task, sentences containing an expected or unexpected target noun-phrase were presented during electroencephalography recording. Prediction, measured as the magnitude of the N400 effect elicited by the article (expected versus unexpected), was hindered only when the production system was taxed during sentence context reading. The present study provides the first direct evidence that the availability of the speech production system is necessary for generating lexical prediction during sentence comprehension. Furthermore, these important results provide an explanation for the recruitment of language production during comprehension.

A statistical method for predicting seizure onset zones from human single-neuron recordings

NASA Astrophysics Data System (ADS)

Valdez, André B.; Hickman, Erin N.; Treiman, David M.; Smith, Kris A.; Steinmetz, Peter N.

2013-02-01

Objective. Clinicians often use depth-electrode recordings to localize human epileptogenic foci. To advance the diagnostic value of these recordings, we applied logistic regression models to single-neuron recordings from depth-electrode microwires to predict seizure onset zones (SOZs). Approach. We collected data from 17 epilepsy patients at the Barrow Neurological Institute and developed logistic regression models to calculate the odds of observing SOZs in the hippocampus, amygdala and ventromedial prefrontal cortex, based on statistics such as the burst interspike interval (ISI). Main results. Analysis of these models showed that, for a single-unit increase in burst ISI ratio, the left hippocampus was approximately 12 times more likely to contain a SOZ; and the right amygdala, 14.5 times more likely. Our models were most accurate for the hippocampus bilaterally (at 85% average sensitivity), and performance was comparable with current diagnostics such as electroencephalography. Significance. Logistic regression models can be combined with single-neuron recording to predict likely SOZs in epilepsy patients being evaluated for resective surgery, providing an automated source of clinically useful information.

A stereotaxic method of recording from single neurons in the intact in vivo eye of the cat.

PubMed

Molenaar, J; Van de Grind, W A

1980-04-01

A method is described for recording stereotaxically from single retinal neurons in the optically intact in vivo eye of the cat. The method is implemented with the help of a new type of stereotaxic instrument and a specially developed stereotaxic atlas of the cat's eye and retina. The instrument is extremely stable and facilitates intracellular recording from retinal neurons. The microelectrode can be rotated about two mutually perpendicular axes, which intersect in the freely positionable pivot point of the electrode manipulation system. When the pivot point is made to coincide with a small electrode-entrance hole in the sclera of the eye, a large retinal region can be reached through this fixed hole in the immobilized eye. The stereotaxic method makes it possible to choose a target point on the presented eye atlas and predict the settings of the instrument necessary to reach this target. This method also includes the prediction of the corresponding light stimulus position on a tangent screen and the calculation of the projection of the recording electrode on this screen. The sources of error in the method were studied experimentally and a numerical perturbation analysis was carried out to study the influence of each of the sources of error on the final result. The overall accuracy of the method is of the order of 5 degrees of visual angle, which will be sufficient for most purposes.

Reappraisal of fetal abdominal circumference in an Asian population: analysis of 50,131 records.

PubMed

Lu, Szu-Ching; Chang, Chiung-Hsin; Yu, Chen-Hsiang; Kang, Lin; Tsai, Pei-Ying; Chang, Fong-Ming

2008-03-01

Fetuses from different populations may show different growth patterns. In obstetrics, fetal abdominal circumference (AC) is a very useful index for assessing fetal growth. In this study, we attempted to establish the normal fetal growth curves of AC in an Asian population in South Taiwan. We reviewed our computer ultrasound database of fetal AC records from January 1991 to December 2006. During the study period of 16 years, only the fetuses examined by ultrasonography with gestational age between 14 and 41 weeks were included. We excluded extreme bilateral records after initial analysis. Eventually, 50,131 records of AC were included for final analysis. The observed gestation-specific AC values and the predicted AC values were calculated. The best-fit regression equation of AC versus gestational age is a second-order polynomial equation. In general, fetal AC values in our population showed similar patterns to those in Western populations. Besides, we established a table of the predicted AC values based on specific gestational age, including the 5 th , 10 th , 50 th , 90 th and 95 th centiles, for clinical reference. To the best of our knowledge, our series is the largest sample of AC reported in the medical literature. We believe that the gestational age-specific nomogram of fetal AC is important for further clinical assessment of fetal growth.

The need to approximate the use-case in clinical machine learning

PubMed Central

Saeb, Sohrab; Jayaraman, Arun; Mohr, David C.; Kording, Konrad P.

2017-01-01

Abstract The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists. PMID:28327985

Latency-Based and Psychophysiological Measures of Sexual Interest Show Convergent and Concurrent Validity.

PubMed

Ó Ciardha, Caoilte; Attard-Johnson, Janice; Bindemann, Markus

2018-04-01

Latency-based measures of sexual interest require additional evidence of validity, as do newer pupil dilation approaches. A total of 102 community men completed six latency-based measures of sexual interest. Pupillary responses were recorded during three of these tasks and in an additional task where no participant response was required. For adult stimuli, there was a high degree of intercorrelation between measures, suggesting that tasks may be measuring the same underlying construct (convergent validity). In addition to being correlated with one another, measures also predicted participants' self-reported sexual interest, demonstrating concurrent validity (i.e., the ability of a task to predict a more validated, simultaneously recorded, measure). Latency-based and pupillometric approaches also showed preliminary evidence of concurrent validity in predicting both self-reported interest in child molestation and viewing pornographic material containing children. Taken together, the study findings build on the evidence base for the validity of latency-based and pupillometric measures of sexual interest.

Negatively valenced expectancy violation predicts emotionality: A longitudinal analysis.

PubMed

Bettencourt, B Ann; Manning, Mark

2016-09-01

We hypothesized that negatively valenced expectancy violations about the quality of 1's life would predict negative emotionality. We tested this hypothesis in a 4-wave longitudinal study of breast cancer survivors. The findings showed that higher levels of negatively valenced expectancy violation, at earlier time points, were associated with greater negative emotionality, at later time points. Implications of the findings are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Prediction of Imagined Single-Joint Movements in a Person with High Level Tetraplegia

PubMed Central

Simeral, John D.; Donoghue, John P.; Hochberg, Leigh R.; Kirsch, Robert F.

2013-01-01

Cortical neuroprostheses for movement restoration require developing models for relating neural activity to desired movement. Previous studies have focused on correlating single-unit activities (SUA) in primary motor cortex to volitional arm movements in able-bodied primates. The extent of the cortical information relevant to arm movements remaining in severely paralyzed individuals is largely unknown. We record intracortical signals using a microelectrode array chronically implanted in the precentral gyrus of a person with tetraplegia, and estimate positions of imagined single-joint arm movements. Using visually guided motor imagery, the participant imagined performing eight distinct single-joint arm movements while SUA, multi-spike trains (MSP), multi-unit activity (MUA), and local field potential time (LFPrms) and frequency signals (LFPstft) were recorded. Using linear system identification, imagined joint trajectories were estimated with 20 – 60% variance explained, with wrist flexion/extension predicted the best and pronation/supination the poorest. Statistically, decoding of MSP and LFPstft yielded estimates that equaled those of SUA. Including multiple signal types in a decoder increased prediction accuracy in all cases. We conclude that signals recorded from a single restricted region of the precentral gyrus in this person with tetraplegia contained useful information regarding the intended movements of upper extremity joints. PMID:22851229

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data

PubMed Central

Park, Juyong

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility. PMID:29432440

Power output measurement during treadmill cycling.

PubMed

Coleman, D A; Wiles, J D; Davison, R C R; Smith, M F; Swaine, I L

2007-06-01

The study aim was to consider the use of a motorised treadmill as a cycling ergometry system by assessing predicted and recorded power output values during treadmill cycling. Fourteen male cyclists completed repeated cycling trials on a motorised treadmill whilst riding their own bicycle fitted with a mobile ergometer. The speed, gradient and loading via an external pulley system were recorded during 20-s constant speed trials and used to estimate power output with an assumption about the contribution of rolling resistance. These values were then compared with mobile ergometer measurements. To assess the reliability of measured power output values, four repeated trials were conducted on each cyclist. During level cycling, the recorded power output was 257.2 +/- 99.3 W compared to the predicted power output of 258.2 +/- 99.9 W (p > 0.05). For graded cycling, there was no significant difference between measured and predicted power output, 268.8 +/- 109.8 W vs. 270.1 +/- 111.7 W, p > 0.05, SEE 1.2 %. The coefficient of variation for mobile ergometer power output measurements during repeated trials ranged from 1.5 % (95 % CI 1.2 - 2.0 %) to 1.8 % (95 % CI 1.5 - 2.4 %). These results indicate that treadmill cycling can be used as an ergometry system to assess power output in cyclists with acceptable accuracy.

Using Patient Flow Information to Determine Risk of Hospital Presentation: Protocol for a Proof-of-Concept Study.

PubMed

Pearce, Christopher M; McLeod, Adam; Patrick, Jon; Boyle, Douglas; Shearer, Marianne; Eustace, Paula; Pearce, Mary Catherine

2016-12-20

Every day, patients are admitted to the hospital with conditions that could have been effectively managed in the primary care sector. These admissions are expensive and in many cases are possible to avoid if early intervention occurs. General practitioners are in the best position to identify those at risk of imminent hospital presentation and admission; however, it is not always possible for all the factors to be considered. A lack of shared information contributes significantly to the challenge of understanding a patient's full medical history. Some health care systems around the world use algorithms to analyze patient data in order to predict events such as emergency presentation; however, those responsible for the design and use of such systems readily admit that the algorithms can only be used to assess the populations used to design the algorithm in the first place. The United Kingdom health care system has contributed data toward algorithm development, which is possible through the unified health care system in place there. The lack of unified patient records in Australia has made building an algorithm for local use a significant challenge. Our objective is to use linked patient records to track patient flow through primary and secondary health care in order to develop a tool that can be applied in real time at the general practice level. This algorithm will allow the generation of reports for general practitioners that indicate the relative risk of patients presenting to an emergency department. A previously designed tool was used to deidentify the general practice and hospital records of approximately 100,000 patients. Records were pooled for patients who had attended emergency departments within the Eastern Health Network of hospitals and general practices within the Eastern Health Network catchment. The next phase will involve development of a model using a predictive analytic machine learning algorithm. The model will be developed iteratively, testing the combination of variables that will provide the best predictive model. Records of approximately 97,000 patients who have attended both a general practice and an emergency department have been identified within the database. These records are currently being used to develop the predictive model. Records from general practice and emergency department visits have been identified and pooled for development of the algorithm. The next phase in the project will see validation and live testing of the algorithm in a practice setting. The algorithm will underpin a clinical decision support tool for general practitioners which will be tested for face validity in this initial study into its efficacy. ©Christopher M Pearce, Adam McLeod, Jon Patrick, Douglas Boyle, Marianne Shearer, Paula Eustace, Mary Catherine Pearce. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 20.12.2016.

Peak expiratory flow profiles delivered by pump systems. Limitations due to wave action.

PubMed

Miller, M R; Jones, B; Xu, Y; Pedersen, O F; Quanjer, P H

2000-06-01

Pump systems are currently used to test the performance of both spirometers and peak expiratory flow (PEF) meters, but for certain flow profiles the input signal (i.e., requested profile) and the output profile can differ. We developed a mathematical model of wave action within a pump and compared the recorded flow profiles with both the input profiles and the output predicted by the model. Three American Thoracic Society (ATS) flow profiles and four artificial flow-versus-time profiles were delivered by a pump, first to a pneumotachograph (PT) on its own, then to the PT with a 32-cm upstream extension tube (which would favor wave action), and lastly with the PT in series with and immediately downstream to a mini-Wright peak flow meter. With the PT on its own, recorded flow for the seven profiles was 2.4 +/- 1.9% (mean +/- SD) higher than the pump's input flow, and similarly was 2.3 +/- 2.3% higher than the pump's output flow as predicted by the model. With the extension tube in place, the recorded flow was 6.6 +/- 6.4% higher than the input flow (range: 0.1 to 18.4%), but was only 1.2 +/- 2.5% higher than the output flow predicted by the model (range: -0.8 to 5.2%). With the mini-Wright meter in series, the flow recorded by the PT was on average 6.1 +/- 9.1% below the input flow (range: -23.8 to 2. 5%), but was only 0.6 +/- 3.3% above the pump's output flow predicted by the model (range: -5.5 to 3.9%). The mini-Wright meter's reading (corrected for its nonlinearity) was on average 1.3 +/- 3.6% below the model's predicted output flow (range: -9.0 to 1. 5%). The mini-Wright meter would be deemed outside ATS limits for accuracy for three of the seven profiles when compared with the pump's input PEF, but this would be true for only one profile when compared with the pump's output PEF as predicted by the model. Our study shows that the output flow from pump systems can differ from the input waveform depending on the operating configuration. This effect can be predicted with reasonable accuracy using a model based on nonsteady flow analysis that takes account of pressure wave reflections within pump systems.

Synthesis of Virtual Environments for Aircraft Community Noise Impact Studies

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Sullivan, Brenda M.

2005-01-01

A new capability has been developed for the creation of virtual environments for the study of aircraft community noise. It is applicable for use with both recorded and synthesized aircraft noise. When using synthesized noise, a three-stage process is adopted involving non-real-time prediction and synthesis stages followed by a real-time rendering stage. Included in the prediction-based source noise synthesis are temporal variations associated with changes in operational state, and low frequency fluctuations that are present under all operating conditions. Included in the rendering stage are the effects of spreading loss, absolute delay, atmospheric absorption, ground reflections, and binaural filtering. Results of prediction, synthesis and rendering stages are presented.

Ultrasound-based clinical prediction rule model for detecting papillary thyroid cancer in cervical lymph nodes: A pilot study.

PubMed

Patel, Nayana U; McKinney, Kristin; Kreidler, Sarah M; Bieker, Teresa M; Russ, Paul; Roberts, Katherine; Glueck, Deborah H; Albuja-Cruz, Maria; Klopper, Joshua; Haugen, Bryan R

2016-01-01

To identify sonographic features of cervical lymph nodes (LNs) that are associated with papillary thyroid cancer (PTC) and to develop a prediction model for classifying nodes as metastatic or benign. This retrospective study included the records of postthyroidectomy patients with PTC who had undergone cervical ultrasound and LN biopsy. LN location, size, shape, hilum, echopattern, Doppler flow, and microcalcifications were assessed. Model selection was used to identify features associated with malignant LNs and to build a predictive, binary-outcome, generalized linear mixed model. A cross-validated receiver operating characteristic analysis was conducted to assess the accuracy of the model for classifying metastatic nodes. We analyzed records from 71 LNs (23 metastatic) in 44 patients (16 with PTC). The predictive model included a nonhomogeneous echopattern (odds ratio [OR], 5.73; 95% confidence interval [CI], 1.07-30.74; p = 0.04), microcalcifications (OR, 4.91; 95% CI, 0.91-26.54; p = 0.06), and volume (OR, 2.57; 95% CI, 0.66-9.99; p = 0.16) as predictors. The model had an area under the curve of 0.74 (95% CI, 0.60-0.85), sensitivity of 65% (95% CI, 50% to 78%), and specificity of 85% (95% CI, 73% to 94%) at the Youden optimal cut point of 0.38. Nonhomogeneous echopattern, microcalcifications, and node volume were predictive of malignant LNs in patients with PTC. A larger sample is needed to validate this model. © 2015 Wiley Periodicals, Inc.

Remote Sensing and halocene Vegetation: History of Global Change

NASA Technical Reports Server (NTRS)

D'Antoni, Hector L.; Schaebitz, Frank

1995-01-01

Predictions of the future evolution of the earth's atmospheric chemistry and its impact on global circulation patterns are based on Global Climate Models (GCMs) that integrate the complex interactions of the biosphere, atmosphere and the oceans. Most of the available records of climate and environment are short-term records (from decades to a few hundred years) with convolved information of real trends and short-term fluctuations. GCMs must be tested beyond the short-term record of climate and environment to insure that predictions are based on trends and therefore are appropriate to support long term policy making. Unfortunately different parts of the world, weather stations are scattered, records extend over a period of only few years, and there are no systematic climate records for large portions of the globe.

Linearity in the response of photopolymers as optical recording media.

PubMed

Gallego, Sergi; Marquez, Andrés; Guardiola, Francisco J; Riquelme, Marina; Fernández, Roberto; Pascual, Inmaculada; Beléndez, Augusto

2013-05-06

Photopolymer are appealing materials for diffractive elements recording. Two of their properties when they are illuminated are useful for this goal: the relief surface changes and the refractive index modifications. To this goal the linearity in the material response is crucial to design the optimum irradiance for each element. In this paper we measured directly some parameters to know how linear is the material response, in terms of the refractive index modulation versus exposure, then we can predict the refractive index distributions during recording. We have analyzed at different recording intensities the evolution of monomer diffusion during recording for photopolymers based on PVA/Acrylamide. This model has been successfully applied to PVA/Acrylamide photopolymers to predict the transmitted diffracted orders and the agreement with experimental values has been increased.

Classic Maya bloodletting and the cultural evolution of religious rituals: quantifying patterns of variation in hieroglyphic texts.

PubMed

Munson, Jessica; Amati, Viviana; Collard, Mark; Macri, Martha J

2014-01-01

Religious rituals that are painful or highly stressful are hypothesized to be costly signs of commitment essential for the evolution of complex society. Yet few studies have investigated how such extreme ritual practices were culturally transmitted in past societies. Here, we report the first study to analyze temporal and spatial variation in bloodletting rituals recorded in Classic Maya (ca. 250-900 CE) hieroglyphic texts. We also identify the sociopolitical contexts most closely associated with these ancient recorded rituals. Sampling an extensive record of 2,480 hieroglyphic texts, this study identifies every recorded instance of the logographic sign for the word ch'ahb' that is associated with ritual bloodletting. We show that documented rituals exhibit low frequency whose occurrence cannot be predicted by spatial location. Conversely, network ties better capture the distribution of bloodletting rituals across the southern Maya region. Our results indicate that bloodletting rituals by Maya nobles were not uniformly recorded, but were typically documented in association with antagonistic statements and may have signaled royal commitments among connected polities.

Classic Maya Bloodletting and the Cultural Evolution of Religious Rituals: Quantifying Patterns of Variation in Hieroglyphic Texts

PubMed Central

Munson, Jessica; Amati, Viviana; Collard, Mark; Macri, Martha J.

2014-01-01

Religious rituals that are painful or highly stressful are hypothesized to be costly signs of commitment essential for the evolution of complex society. Yet few studies have investigated how such extreme ritual practices were culturally transmitted in past societies. Here, we report the first study to analyze temporal and spatial variation in bloodletting rituals recorded in Classic Maya (ca. 250–900 CE) hieroglyphic texts. We also identify the sociopolitical contexts most closely associated with these ancient recorded rituals. Sampling an extensive record of 2,480 hieroglyphic texts, this study identifies every recorded instance of the logographic sign for the word ch’ahb’ that is associated with ritual bloodletting. We show that documented rituals exhibit low frequency whose occurrence cannot be predicted by spatial location. Conversely, network ties better capture the distribution of bloodletting rituals across the southern Maya region. Our results indicate that bloodletting rituals by Maya nobles were not uniformly recorded, but were typically documented in association with antagonistic statements and may have signaled royal commitments among connected polities. PMID:25254359

Prediction task guided representation learning of medical codes in EHR.

PubMed

Cui, Liwen; Xie, Xiaolei; Shen, Zuojun

2018-06-18

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples. Copyright © 2018. Published by Elsevier Inc.

Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.

PubMed

Ng, Kenney; Steinhubl, Steven R; deFilippi, Christopher; Dey, Sanjoy; Stewart, Walter F

2016-11-01

Using electronic health records data to predict events and onset of diseases is increasingly common. Relatively little is known, although, about the tradeoffs between data requirements and model utility. We examined the performance of machine learning models trained to detect prediagnostic heart failure in primary care patients using longitudinal electronic health records data. Model performance was assessed in relation to data requirements defined by the prediction window length (time before clinical diagnosis), the observation window length (duration of observation before prediction window), the number of different data domains (data diversity), the number of patient records in the training data set (data quantity), and the density of patient encounters (data density). A total of 1684 incident heart failure cases and 13 525 sex, age-category, and clinic matched controls were used for modeling. Model performance improved as (1) the prediction window length decreases, especially when <2 years; (2) the observation window length increases but then levels off after 2 years; (3) the training data set size increases but then levels off after 4000 patients; (4) more diverse data types are used, but, in order, the combination of diagnosis, medication order, and hospitalization data was most important; and (5) data were confined to patients who had ≥10 phone or face-to-face encounters in 2 years. These empirical findings suggest possible guidelines for the minimum amount and type of data needed to train effective disease onset predictive models using longitudinal electronic health records data. © 2016 American Heart Association, Inc.

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012

PubMed Central

Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O.

2018-01-01

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping. PMID:29617333

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999-2012.

PubMed

Stefanoff, Pawel; Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O; Rosinska, Magdalena

2018-04-04

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping.

Added value from 576 years of tree-ring records in the prediction of the Great Salt Lake level

Treesearch

Robert R. Gillies; Oi-Yu Chung; S.-Y. Simon Wang; R. Justin DeRose; Yan Sun

2015-01-01

Predicting lake level fluctuations of the Great Salt Lake (GSL) in Utah - the largest terminal salt-water lake in the Western Hemisphere - is critical from many perspectives. The GSL integrates both climate and hydrological variations within the region and is particularly sensitive to low-frequency climate cycles. Since most hydroclimate variable records cover...

On the distribution of species occurrence

USGS Publications Warehouse

Buzas, Martin A.; Koch, Carl F.; Culver, Stephen J.; Sohl, Norman F.

1982-01-01

The distribution of species abundance (number of individuals per species) is well documented. The distribution of species occurrence (number of localities per species), however, has received little attention. This study investigates the distribution of species occurrence for five large data sets. For modern benthic foraminifera, species occurrence is examined from the Atlantic continental margin of North America, where 875 species were recorded 10,017 times at 542 localities, the Gulf of Mexico, where 848 species were recorded 18,007 times at 426 localities, and the Caribbean, where 1,149 species were recorded 6,684 times at 268 localities. For Late Cretaceous molluscs, species occurrence is examined from the Gulf Coast where 716 species were recorded 6,236 times at 166 localities and a subset of this data consisting of 643 species recorded 3,851 times at 86 localities.Logseries and lognormal distributions were fitted to these data sets. In most instances the logseries best predicts the distribution of species occurrence. The lognormal, however, also fits the data fairly well, and, in one instance, better. The use of these distributions allows the prediction of the number of species occurring once, twice, ..., n times.Species abundance data are also available for the molluscan data sets. They indicate that the most abundant species (greatest number of individuals) usually occur most frequently. In all data sets approximately half the species occur four or less times. The probability of noting the presence of rarely occurring species is small, and, consequently, such species must be used with extreme caution in studies requiring knowledge of the distribution of species in space and time.

Peak ground motion predictions with empirical site factors using Taiwan Strong Motion Network recordings

NASA Astrophysics Data System (ADS)

Chung, Jen-Kuang

2013-09-01

A stochastic method called the random vibration theory (Boore, 1983) has been used to estimate the peak ground motions caused by shallow moderate-to-large earthquakes in the Taiwan area. Adopting Brune's ω-square source spectrum, attenuation models for PGA and PGV were derived from path-dependent parameters which were empirically modeled from about one thousand accelerograms recorded at reference sites mostly located in a mountain area and which have been recognized as rock sites without soil amplification. Consequently, the predicted horizontal peak ground motions at the reference sites, are generally comparable to these observed. A total number of 11,915 accelerograms recorded from 735 free-field stations of the Taiwan Strong Motion Network (TSMN) were used to estimate the site factors by taking the motions from the predictive models as references. Results from soil sites reveal site amplification factors of approximately 2.0 ~ 3.5 for PGA and about 1.3 ~ 2.6 for PGV. Finally, as a result of amplitude corrections with those empirical site factors, about 75% of analyzed earthquakes are well constrained in ground motion predictions, having average misfits ranging from 0.30 to 0.50. In addition, two simple indices, R 0.57 and R 0.38, are proposed in this study to evaluate the validity of intensity map prediction for public information reports. The average percentages of qualified stations for peak acceleration residuals less than R 0.57 and R 0.38 can reach 75% and 54%, respectively, for most earthquakes. Such a performance would be good enough to produce a faithful intensity map for a moderate scenario event in the Taiwan region.

Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.

PubMed

Hoogendoorn, Mark; Szolovits, Peter; Moons, Leon M G; Numans, Mattijs E

2016-05-01

Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploiting the rich content of the EMRs. In this paper, we explore the usage of a range of natural language processing (NLP) techniques to extract valuable predictors from uncoded consultation notes and study whether they can help to improve predictive performance. We study a number of existing techniques for the extraction of predictors from the consultation notes, namely a bag of words based approach and topic modeling. In addition, we develop a dedicated technique to match the uncoded consultation notes with a medical ontology. We apply these techniques as an extension to an existing pipeline to extract predictors from EMRs. We evaluate them in the context of predictive modeling for colorectal cancer (CRC), a disease known to be difficult to diagnose before performing an endoscopy. Our results show that we are able to extract useful information from the consultation notes. The predictive performance of the ontology-based extraction method moves significantly beyond the benchmark of age and gender alone (area under the receiver operating characteristic curve (AUC) of 0.870 versus 0.831). We also observe more accurate predictive models by adding features derived from processing the consultation notes compared to solely using coded data (AUC of 0.896 versus 0.882) although the difference is not significant. The extracted features from the notes are shown be equally predictive (i.e. there is no significant difference in performance) compared to the coded data of the consultations. It is possible to extract useful predictors from uncoded consultation notes that improve predictive performance. Techniques linking text to concepts in medical ontologies to derive these predictors are shown to perform best for predicting CRC in our EMR dataset. Copyright © 2016 Elsevier B.V. All rights reserved.

Opportunities of probabilistic flood loss models

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Lüdtke, Stefan; Vogel, Kristin; Merz, Bruno

2016-04-01

Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. However, reliable flood damage models are a prerequisite for the practical usefulness of the model results. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks and traditional stage damage functions. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005, 2006 and 2013 in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of sharpness of the predictions the reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The comparison of the uni-variable Stage damage function and the multivariable model approach emphasises the importance to quantify predictive uncertainty. With each explanatory variable, the multi-variable model reveals an additional source of uncertainty. However, the predictive performance in terms of precision (mbe), accuracy (mae) and reliability (HR) is clearly improved in comparison to uni-variable Stage damage function. Overall, Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

A Longitudinal Study of Child Maltreatment and Mental Health Predictors of Admission to Psychiatric Residential Treatment Facilities

PubMed Central

Rose, Roderick A.; Lanier, Paul

2017-01-01

The child welfare system is an access point for children’s mental health services. Psychiatric residential treatment facilities (PRTFs) are the most restrictive, and most expensive setting for children to receive long-term care. Given the high rates of behavioral health concerns among maltreated children in out-of-home care, research is needed to examine the factors that predict entry in PRTFs among children investigated for maltreatment. This exploratory study used cross-sector administrative records linked across multiple systems, including child welfare records and Medicaid claims, from a single state over a five-year period (n = 105,982). Cox proportional hazards modeling was used to predict entry into a PRTF. After controlling for many factors, PRTF entry was predicted by diagnosis code indicating a trauma-related condition, antipsychotic medication prescriptions, and entry into lower levels of out-of-home care, supporting the view that youth are admitted to PRTFs largely due to clinical need. However, PRTF admission is also associated with characteristics of their experiences with the social service system, primarily foster care placement stability and permanency. Implications for practice and research are discussed. PMID:28956825

Relationship of number of seizures recorded on video-EEG to surgical outcome in refractory medial temporal lobe epilepsy

PubMed Central

Sainju, Rup Kamal; Wolf, Bethany Jacobs; Bonilha, Leonardo; Martz, Gabriel

2014-01-01

Introduction Surgical planning for refractory medial temporal lobe epilepsy (rMTLE) relies on seizure localization by ictal electroencephalography (EEG). Multiple factors impact the number of seizures recorded. We evaluated whether seizure freedom correlated to the number of seizures recorded, and the related factors. Methods We collected data for 32 patients with rMTLE who underwent anterior temporal lobectomy. Primary analysis evaluated number of seizures captured as a predictor of surgical outcome. Subsequent analyses explored factors that may seizure number. Results Number of seizures recorded did not predict seizure freedom. More seizures were recorded with more days of seizure occurrence (p<0.001), seizure clusters (p≤0.011) and poorly localized seizures (PLSz) (p=0.004). Regression modeling showed a trend for subjects with fewer recorded poorly localized seizures to have better surgical outcome (p=0.052). Conclusions Total number of recorded seizures does not predict surgical outcome. Patients with more PLSz may have worse outcome. PMID:22990726

MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records.

PubMed

Huang, Zhengxing; Chan, Tak-Ming; Dong, Wei

2017-02-01

Major adverse cardiac events (MACE) of acute coronary syndrome (ACS) often occur suddenly resulting in high mortality and morbidity. Recently, the rapid development of electronic medical records (EMR) provides the opportunity to utilize the potential of EMR to improve the performance of MACE prediction. In this study, we present a novel data-mining based approach specialized for MACE prediction from a large volume of EMR data. The proposed approach presents a new classification algorithm by applying both over-sampling and under-sampling on minority-class and majority-class samples, respectively, and integrating the resampling strategy into a boosting framework so that it can effectively handle imbalance of MACE of ACS patients analogous to domain practice. The method learns a new and stronger MACE prediction model each iteration from a more difficult subset of EMR data with wrongly predicted MACEs of ACS patients by a previous weak model. We verify the effectiveness of the proposed approach on a clinical dataset containing 2930 ACS patient samples with 268 feature types. While the imbalanced ratio does not seem extreme (25.7%), MACE prediction targets pose great challenge to traditional methods. As these methods degenerate dramatically with increasing imbalanced ratios, the performance of our approach for predicting MACE remains robust and reaches 0.672 in terms of AUC. On average, the proposed approach improves the performance of MACE prediction by 4.8%, 4.5%, 8.6% and 4.8% over the standard SVM, Adaboost, SMOTE, and the conventional GRACE risk scoring system for MACE prediction, respectively. We consider that the proposed iterative boosting approach has demonstrated great potential to meet the challenge of MACE prediction for ACS patients using a large volume of EMR. Copyright © 2017 Elsevier Inc. All rights reserved.

Genetic and phenotypic analysis of daily Israeli Holstein milk, fat, and protein production as determined by a real-time milk analyzer.

PubMed

Weller, J I; Ezra, E

2016-12-01

The objective was to test the hypothesis that more frequent but less accurately analyzed milk components may give a more representative measure of a cow's total lactation production. Daily records for milk production and fat and protein concentration collected by the AfiLab recording system (Afimilk, Kibbutz Afikim, Israel) from January 2014 to January 2016 from 47 large kibbutz (communal) herds distributed throughout Israel with a total of 37,486 Israeli Holstein cows were compared with the same statistics derived from monthly test day records derived by Bentley and Foss milk analyzers at the central laboratory of the Israel Cattle Breeders Association. The lactation means for all traits were quite similar for the 2 methods in both parities, except for fat production, which was lower for the daily records. This finding corresponded to fat lactation curves, which showed that daily results were lower with low days in milk (DIM) but almost equal to the monthly results after 125 DIM. Relative to monthly records, daily records overestimated protein percentage before 150 DIM and underestimated protein percentage in the second half of the lactation. The standard deviation for first- and second-parity daily records scored by the monthly and daily system were least similar for fat percentage, but even for this trait the difference was no more than 0.1 percentage points. The standard deviations for complete lactation production were slightly lower for the daily results for all traits but protein production. First-parity heritabilities were higher for lactations computed from daily records for all traits except for protein percentage, but differences were not significant. For daily records, coefficients of determination to predict future milk, fat, and protein lactation production from truncated lactations were greatest and root mean squared errors were least if the mean production from the last 2 weeks before the truncation date was used to estimate future production. Daily first-parity partial lactations for milk, fat, and protein production with <150 DIM predicted future lactation more accurately than corresponding monthly partial lactations. With only 30 DIM, genetic correlations between predicted and actual lactations ranged from 0.73 to 0.79 for milk, fat, and protein production. Real-time daily recording of fat and protein concentration by the daily recording system may be preferable to monthly analysis for herd-management decisions and genetic evaluation. Further study is required to compare the results of individual cows in multiple lactations. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Pretreatment Acoustic Predictors of Gender, Femininity, and Naturalness Ratings in Individuals With Male-to-Female Gender Identity.

PubMed

Hardy, Teresa L D; Boliek, Carol A; Wells, Kristopher; Dearden, Carol; Zalmanowitz, Connie; Rieger, Jana M

2016-05-01

The purpose of this study was to describe the pretreatment acoustic characteristics of individuals with male-to-female gender identity (IMtFGI) and investigate the ability of the acoustic measures to predict ratings of gender, femininity, and vocal naturalness. This retrospective descriptive study included 2 groups of participants. Speakers were IMtFGI who had not previously received communication feminization treatment (N = 25). Listeners were members of the lay community (N = 30). Acoustic data were retrospectively obtained from pretreatment recordings, and pretreatment recordings also served as stimuli for 3 perceptual rating tasks (completed by listeners). Acoustic data generally were within normal limits for male speakers. All but 2 speakers were perceived to be male, limiting information about the relationship between acoustic measures and gender perception. Fundamental frequency (reading) significantly predicted femininity ratings (p = .000). A total of 3 stepwise regression models indicated that minimum frequency (range task), second vowel formant (sustained vowel), and shimmer percentage (sustained vowel) together significantly predicted naturalness ratings (p = .005, p = .003, and p = .002, respectively). Study aims were achieved with the exception of acoustic predictors of gender perception, which could be described for only 2 speakers. Future research should investigate measures of prosody, voice quality, and other aspects of communication as predictors of gender, femininity, and naturalness.

Near-inertial Wave Studies using Historical Mooring Records and a High-Resolution General Circulation Model

DTIC Science & Technology

2009-01-01

Mooring Records and a High- Resolution General Circulation Model Harper Simmons School of Fisheries and Ocean Sciences 903 Koyukuk Drive Fairbanks AK...oceanographic community has been to develop a global internal wave prediction system analogous to those already in place for surface waves. Early steps have...AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) School of Fisheries and Ocean

Missile impacts as sources of seismic energy on the moon

USGS Publications Warehouse

Latham, G.V.; McDonald, W.G.; Moore, H.J.

1970-01-01

Seismic signals recorded from impacts of missiles at the White Sands Missile Range are radically different from the signal recorded from the Apollo 12 lunar module impact. This implies that lunar structure to depths of at least 10 to 20 kilometers is quite different from the typical structure of the earth's crust. Results obtained from this study can be used to predict seismic wave amplitudes from future man-made lunar impacts. Seismic energy and crater dimensions from impacts are compared with measurements from chemical explosions.

Perceptions of organ donors and willingness to donate organs upon death: a test of the prototype/willingness model.

PubMed

Hyde, Melissa K; White, Katherine M

2014-01-01

Understanding people's organ donation decisions may narrow the gap between organ supply and demand. In two studies, participants who had not recorded their posthumous organ donation decision (Study 1, N = 210; Study 2, N = 307) completed items assessing prototype/willingness model (PWM; attitude, subjective norm, donor prototype favorability and similarity, willingness) constructs. Attitude, subjective norm, and prototype similarity predicted willingness to donate. Prototype favorability and a Prototype Favorability × Similarity interaction predicted willingness (Study 2). These findings provide support for the PWM in altruistic health contexts, highlighting the importance of people's perceptions about organ donors in their donation decisions.

Perceptions of Organ Donors and Willingness to Donate Organs Upon Death: A Test of the Prototype/Willingness Model.

PubMed

Hyde, Melissa K; White, Katherine M

2014-06-09

Understanding people's organ donation decisions may narrow the gap between organ supply and demand. In two studies, participants who had not recorded their posthumous organ donation decision (Study 1 N = 210; Study 2 N = 307) completed items assessing Prototype/Willingness Model (PWM) (attitude, subjective norm, donor prototype favorability and similarity, willingness) constructs. Attitude, subjective norm, and prototype similarity predicted willingness to donate. Prototype favorability and a prototype favorability x similarity interaction predicted willingness (Study 2). These findings provide support for the PWM in altruistic health contexts, highlighting the importance of people's perceptions about organ donors in their donation decisions.

EEG seizure detection and prediction algorithms: a survey

NASA Astrophysics Data System (ADS)

Alotaiby, Turkey N.; Alshebeili, Saleh A.; Alshawi, Tariq; Ahmad, Ishtiaq; Abd El-Samie, Fathi E.

2014-12-01

Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

Pathways to an Engineering Career

ERIC Educational Resources Information Center

Pearson, Willie, Jr.; Miller, Jon D.

2012-01-01

Utilizing data from the 20-year record of the Longitudinal Study of American Youth (LSAY), this analysis uses a set of variables to predict employment in engineering for a national sample of adults aged 34 to 37. The LSAY is one of the longest longitudinal studies of the impact of secondary education and postsecondary education conducted in the…

Predictors of Success for Students Entering Graduate School on a Probationary Basis.

ERIC Educational Resources Information Center

Nelson, Jacquelyn S.; Nelson, C. Van

This study sought to determine which combination of criteria would accurately predict the success of students in graduate education who began their graduate studies on probationary admission status. Variables examined included grade point average (GPA) after 9 hours of graduate coursework, Graduate Record Examination (GRE) verbal, quantitative,…

School Attendance Revisited: A Study of Urban African American Students' GPA and Coping Strategies.

ERIC Educational Resources Information Center

Steward, Robbie J.; Steward, Astin Devine; Blair, Jonathan

This study investigated the degree to which at-risk, urban, African American high school students' coping strategies and grade point average (GPA) would predict attendance. Data were collected from 100 high school freshmen using the Adolescent Coping Orientation for Problem Experiences. Students' GPAs were identified through school records.…

Field-level validation of a CLIMEX model for Cactoblastis cactorum (Lepidoptera: Pyralidae) using estimated larval growth rates.

PubMed

Legaspi, Benjamin C; Legaspi, Jesusa Crisostomo

2010-04-01

Invasive pests, such as the cactus moth, Cactoblastis cactorum (Berg) (Lepidoptera: Pyralidae), have not reached equilibrium distributions and present unique opportunities to validate models by comparing predicted distributions with eventual realized geographic ranges. A CLIMEX model was developed for C. cactorum. Model validation was attempted at the global scale by comparing worldwide distribution against known occurrence records and at the field scale by comparing CLIMEX "growth indices" against field measurements of larval growth. Globally, CLIMEX predicted limited potential distribution in North America (from the Caribbean Islands to Florida, Texas, and Mexico), Africa (South Africa and parts of the eastern coast), southern India, parts of Southeast Asia, and the northeastern coast of Australia. Actual records indicate the moth has been found in the Caribbean (Antigua, Barbuda, Montserrat Saint Kitts and Nevis, Cayman Islands, and U.S. Virgin Islands), Cuba, Bahamas, Puerto Rico, southern Africa, Kenya, Mexico, and Australia. However, the model did not predict that distribution would extend from India to the west into Pakistan. In the United States, comparison of the predicted and actual distribution patterns suggests that the moth may be close to its predicted northern range along the Atlantic coast. Parts of Texas and most of Mexico may be vulnerable to geographic range expansion of C. cactorum. Larval growth rates in the field were estimated by measuring differences in head capsules and body lengths of larval cohorts at weekly intervals. Growth indices plotted against measures of larval growth rates compared poorly when CLIMEX was run using the default historical weather data. CLIMEX predicted a single period conducive to insect development, in contrast to the three generations observed in the field. Only time and more complete records will tell whether C. cactorum will extend its geographical distribution to regions predicted by the CLIMEX model. In terms of small scale temporal predictions, this study suggests that CLIMEX indices may agree with field-specific population dynamics, provided an adequate metric for insect growth rate is used and weather data are location and time specific.

Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian learning mechanisms in the cerebral cortex.

PubMed

Spriggs, M J; Sumner, R L; McMillan, R L; Moran, R J; Kirk, I J; Muthukumaraswamy, S D

2018-04-30

The Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aim of the current study was to compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain. Forty participants were presented with both paradigms during EEG recording. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. This therefore indicates that both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations. Copyright © 2018 Elsevier Inc. All rights reserved.

Predictive validity of the HCR-20 for violent and non-violent sexual behaviour in a secure mental health service.

PubMed

O'Shea, Laura E; Thaker, Dev-Kishan; Picchioni, Marco M; Mason, Fiona L; Knight, Caroline; Dickens, Geoffrey L

2016-12-01

Violent and non-violent sexual behaviour is a fairly common problem among secure mental health service patients, but specialist sexual violence risk assessment is time-consuming and so performed infrequently. We aimed to establish whether a commonly used violence risk assessment tool, the Health Clinical Risk management 20(HCR-20), has predictive validity specifically for inappropriate sexual behaviour. A pseudo-prospective cohort design was used for a study in the adult wards of a large provider of specialist secure mental health services. Routine clinical team HCR-20 assessments were extracted from records, and incidents involving inappropriate sexual behaviour were recorded for the 3 months following assessment. Of 613 patients, 104 (17%) had engaged in at least one inappropriate sexual behaviour; in 65 (10.6%), the sexual act was violent. HCR-20 total score, clinical and risk management subscales, predicted violent and non-violent sexual behaviour. The negative predictive value of the HCR-20 for inappropriate sexual behaviour was over 90%. Prediction of violent sexual behaviour may be regarded as well within the scope of the HCR-20 as a structured professional judgement tool to aid violence risk prediction, but we found that it also predicts behaviours that may be of concern but fall below the violence threshold. High negative predictive values suggest that HCR-20 scores may have some utility for screening out patients who do not require more specialist assessment for inappropriate sexual behaviour. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Validity of administrative coding in identifying patients with upper urinary tract calculi.

PubMed

Semins, Michelle J; Trock, Bruce J; Matlaga, Brian R

2010-07-01

Administrative databases are increasingly used for epidemiological investigations. We performed a study to assess the validity of ICD-9 codes for upper urinary tract stone disease in an administrative database. We retrieved the records of all inpatients and outpatients at Johns Hopkins Hospital between November 2007 and October 2008 with an ICD-9 code of 592, 592.0, 592.1 or 592.9 as one of the first 3 diagnosis codes. A random number generator selected 100 encounters for further review. We considered a patient to have a true diagnosis of an upper tract stone if the medical records specifically referenced a kidney stone event, or included current or past treatment for a kidney stone. Descriptive and comparative analyses were performed. A total of 8,245 encounters coded as upper tract calculus were identified and 100 were randomly selected for review. Two patients could not be identified within the electronic medical record and were excluded from the study. The positive predictive value of using all ICD-9 codes for an upper tract calculus (592, 592.0, 592.1) to identify subjects with renal or ureteral stones was 95.9%. For 592.0 only the positive predictive value was 85%. However, although the positive predictive value for 592.1 only was 100%, 26 subjects (76%) with a ureteral stone were not appropriately billed with this code. ICD-9 coding for urinary calculi is likely to be sufficiently valid to be useful in studies using administrative data to analyze stone disease. However, ICD-9 coding is not a reliable means to distinguish between subjects with renal and ureteral calculi. Copyright (c) 2010 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Cortical Auditory Evoked Potentials Recorded From Nucleus Hybrid Cochlear Implant Users.

PubMed

Brown, Carolyn J; Jeon, Eun Kyung; Chiou, Li-Kuei; Kirby, Benjamin; Karsten, Sue A; Turner, Christopher W; Abbas, Paul J

2015-01-01

Nucleus Hybrid Cochlear Implant (CI) users hear low-frequency sounds via acoustic stimulation and high-frequency sounds via electrical stimulation. This within-subject study compares three different methods of coordinating programming of the acoustic and electrical components of the Hybrid device. Speech perception and cortical auditory evoked potentials (CAEP) were used to assess differences in outcome. The goals of this study were to determine whether (1) the evoked potential measures could predict which programming strategy resulted in better outcome on the speech perception task or was preferred by the listener, and (2) CAEPs could be used to predict which subjects benefitted most from having access to the electrical signal provided by the Hybrid implant. CAEPs were recorded from 10 Nucleus Hybrid CI users. Study participants were tested using three different experimental processor programs (MAPs) that differed in terms of how much overlap there was between the range of frequencies processed by the acoustic component of the Hybrid device and range of frequencies processed by the electrical component. The study design included allowing participants to acclimatize for a period of up to 4 weeks with each experimental program prior to speech perception and evoked potential testing. Performance using the experimental MAPs was assessed using both a closed-set consonant recognition task and an adaptive test that measured the signal-to-noise ratio that resulted in 50% correct identification of a set of 12 spondees presented in background noise. Long-duration, synthetic vowels were used to record both the cortical P1-N1-P2 "onset" response and the auditory "change" response (also known as the auditory change complex [ACC]). Correlations between the evoked potential measures and performance on the speech perception tasks are reported. Differences in performance using the three programming strategies were not large. Peak-to-peak amplitude of the ACC was not found to be sensitive enough to accurately predict the programming strategy that resulted in the best performance on either measure of speech perception. All 10 Hybrid CI users had residual low-frequency acoustic hearing. For all 10 subjects, allowing them to use both the acoustic and electrical signals provided by the implant improved performance on the consonant recognition task. For most subjects, it also resulted in slightly larger cortical change responses. However, the impact that listening mode had on the cortical change responses was small, and again, the correlation between the evoked potential and speech perception results was not significant. CAEPs can be successfully measured from Hybrid CI users. The responses that are recorded are similar to those recorded from normal-hearing listeners. The goal of this study was to see if CAEPs might play a role either in identifying the experimental program that resulted in best performance on a consonant recognition task or in documenting benefit from the use of the electrical signal provided by the Hybrid CI. At least for the stimuli and specific methods used in this study, no such predictive relationship was found.

Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

PubMed

Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

2017-01-01

The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

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).

VLF nighttime data analysis

NASA Astrophysics Data System (ADS)

1991-03-01

This paper documents a very low frequency/low frequency (VLF/LF) Data Analysis task by the Naval Ocean Systems Center to improve the modeling of the nighttime ionosphere when making propagation predictions with the Long Wave Propagation Capability (LWPC) computer program. The task utilizes an extensive database of VLF measured data recorded during the 1985 to 1986 trips of the merchant ship GTS Callaghan in the North Atlantic area. By constraining the Callaghan data to those periods when both the ship and the distant transmitters were in time zones consistent with all-nighttime propagation, and by eliminating data from trips outside the principal area of interest, an aggregated set of recorded data was assembled for each frequency of concern. Four frequencies were examined: 16.0, 19.0, 21.4 and 24.0 kHz. Recorded data sets were graphed as signal vs. distance plots, computing distance from the transmitter for each ship's location. The LWPC program was then utilized to compute signal vs. distance along a typical path in the same ocean area, and the predicted and recorded data were compared. By changing the LWPC parameters different propagation predictions were compared with the recorded data until a best fit was obtained.

Nuclear event time histories and computed site transfer functions for locations in the Los Angeles region

USGS Publications Warehouse

Rogers, A.M.; Covington, P.A.; Park, R.B.; Borcherdt, R.D.; Perkins, D.M.

1980-01-01

This report presents a collection of Nevada Test Site (NTS) nuclear explosion recordings obtained at sites in the greater Los Angeles, Calif., region. The report includes ground velocity time histories, as well as, derived site transfer functions. These data have been collected as part of a study to evaluate the validity of using low-level ground motions to predict the frequency-dependent response of a site during an earthquake. For this study 19 nuclear events were recorded at 98 separate locations. Some of these sites have recorded more than one of the nuclear explosions, and, consequently, there are a total of 159, three-component station records. The location of all the recording sites are shown in figures 1–5, the station coordinates and abbreviations are given in table 1. The station addresses are listed in table 2, and the nuclear explosions that were recorded are listed in table 3. The recording sites were chosen on the basis of three criteria: (1) that the underlying geological conditions were representative of conditions over significant areas of the region, (2) that the site was the location of a strong-motion recording of the 1971 San Fernando earthquake, or (3) that more complete geographical coverage was required in that location.

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

PubMed

Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

2015-07-01

As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

DSM-5 antisocial personality disorder: predictive validity in a prison sample.

PubMed

Edens, John F; Kelley, Shannon E; Lilienfeld, Scott O; Skeem, Jennifer L; Douglas, Kevin S

2015-04-01

Symptoms of antisocial personality disorder (ASPD), particularly remorselessness, are frequently introduced in legal settings as a risk factor for future violence in prison, despite a paucity of research on the predictive validity of this disorder. We examined whether an ASPD diagnosis or symptom-criteria counts could prospectively predict any form of institutional misconduct, as well as aggressive and violent infractions among newly admitted prisoners. Adult male (n = 298) and female (n = 55) offenders were recruited from 4 prison systems across the United States. At the time of study enrollment, diagnostic information was collected using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997) supplemented by a detailed review of official records. Disciplinary records were obtained from inmates' respective prisons covering a 1-year period following study enrollment and misconduct was categorized hierarchically as any (general), aggressive (verbal/physical), or violent (physical). Dichotomous ASPD diagnoses and adult symptom-criteria counts did not significantly predict institutional misconduct across our 3 outcome variables, with effect sizes being close to 0 in magnitude. The symptom of remorselessness in particular showed no relation to future misconduct in prison. Childhood symptom counts of conduct disorder demonstrated modest predictive utility. Our results offer essentially no support for the claim that ASPD diagnoses can predict institutional misconduct in prison, regardless of the number of adult symptoms present. In forensic contexts, testimony that an ASPD diagnosis identifies defendants who will pose a serious threat while incarcerated in prison presently lacks any substantial scientific foundation. (c) 2015 APA, all rights reserved).

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

PubMed Central

Luo, Wei; Tran, Truyen; Berk, Michael; Venkatesh, Svetha

2016-01-01

Background Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. Objective The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. Methods We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). Results The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. Conclusions This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk. PMID:27400764

Associations between psychometrically assessed life history strategy and daily behavior: data from the Electronically Activated Recorder (EAR)

PubMed Central

2018-01-01

Life history theory has generated cogent, well-supported hypotheses about individual differences in human biodemographic traits (e.g., age at sexual maturity) and psychometric traits (e.g., conscientiousness), but little is known about how variation in life history strategy (LHS) is manifest in quotidian human behavior. Here I test predicted associations between the self-report Arizona Life History Battery and frequencies of 12 behaviors observed over 72 h in 91 US college students using the Electronically Activated Recorder (EAR), a method of gathering periodic brief audio recordings as participants go about their daily lives. Bayesian multi-level aggregated binomial regression analysis found no strong associations between ALHB scores and behavior frequencies. One behavior, presence at amusement venues (bars, concerts, sports events) was weakly positively associated with ALHB-assessed slow LHS, contrary to prediction. These results may represent a challenge to the ALHB’s validity. However, it remains possible that situational influences on behavior, which were not measured in the present study, moderate the relationships between psychometrically-assessed LHS and quotidian behavior. PMID:29868275

Associations between psychometrically assessed life history strategy and daily behavior: data from the Electronically Activated Recorder (EAR).

PubMed

Manson, Joseph H

2018-01-01

Life history theory has generated cogent, well-supported hypotheses about individual differences in human biodemographic traits (e.g., age at sexual maturity) and psychometric traits (e.g., conscientiousness), but little is known about how variation in life history strategy (LHS) is manifest in quotidian human behavior. Here I test predicted associations between the self-report Arizona Life History Battery and frequencies of 12 behaviors observed over 72 h in 91 US college students using the Electronically Activated Recorder (EAR), a method of gathering periodic brief audio recordings as participants go about their daily lives. Bayesian multi-level aggregated binomial regression analysis found no strong associations between ALHB scores and behavior frequencies. One behavior, presence at amusement venues (bars, concerts, sports events) was weakly positively associated with ALHB-assessed slow LHS, contrary to prediction. These results may represent a challenge to the ALHB's validity. However, it remains possible that situational influences on behavior, which were not measured in the present study, moderate the relationships between psychometrically-assessed LHS and quotidian behavior.

The need to approximate the use-case in clinical machine learning.

PubMed

Saeb, Sohrab; Lonini, Luca; Jayaraman, Arun; Mohr, David C; Kording, Konrad P

2017-05-01

The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists. © The Author 2017. Published by Oxford University Press.

PREDICTING CHANGES USING MULTI-DATE SATELLITE IMAGERY: SAN PEDRO RIVER CASE STUDY

EPA Science Inventory

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Vegetation change in the American West has been a subject of concern throughout the twentieth century. Although many of the changes have been recorded qualitatively through the use of comparative photography and historical reports, little quantitative information has been a...

Absence of early epileptiform abnormalities predicts lack of seizures on continuous EEG.

PubMed

Shafi, Mouhsin M; Westover, M Brandon; Cole, Andrew J; Kilbride, Ronan D; Hoch, Daniel B; Cash, Sydney S

2012-10-23

To determine whether the absence of early epileptiform abnormalities predicts absence of later seizures on continuous EEG monitoring of hospitalized patients. We retrospectively reviewed 242 consecutive patients without a prior generalized convulsive seizure or active epilepsy who underwent continuous EEG monitoring lasting at least 18 hours for detection of nonconvulsive seizures or evaluation of unexplained altered mental status. The findings on the initial 30-minute screening EEG, subsequent continuous EEG recordings, and baseline clinical data were analyzed. We identified early EEG findings associated with absence of seizures on subsequent continuous EEG. Seizures were detected in 70 (29%) patients. A total of 52 patients had their first seizure in the initial 30 minutes of continuous EEG monitoring. Of the remaining 190 patients, 63 had epileptiform discharges on their initial EEG, 24 had triphasic waves, while 103 had no epileptiform abnormalities. Seizures were later detected in 22% (n = 14) of studies with epileptiform discharges on their initial EEG, vs 3% (n = 3) of the studies without epileptiform abnormalities on initial EEG (p < 0.001). In the 3 patients without epileptiform abnormalities on initial EEG but with subsequent seizures, the first epileptiform discharge or electrographic seizure occurred within the first 4 hours of recording. In patients without epileptiform abnormalities during the first 4 hours of recording, no seizures were subsequently detected. Therefore, EEG features early in the recording may indicate a low risk for seizures, and help determine whether extended monitoring is necessary.

Absence of early epileptiform abnormalities predicts lack of seizures on continuous EEG

PubMed Central

Westover, M. Brandon; Cole, Andrew J.; Kilbride, Ronan D.; Hoch, Daniel B.; Cash, Sydney S.

2012-01-01

Objective: To determine whether the absence of early epileptiform abnormalities predicts absence of later seizures on continuous EEG monitoring of hospitalized patients. Methods: We retrospectively reviewed 242 consecutive patients without a prior generalized convulsive seizure or active epilepsy who underwent continuous EEG monitoring lasting at least 18 hours for detection of nonconvulsive seizures or evaluation of unexplained altered mental status. The findings on the initial 30-minute screening EEG, subsequent continuous EEG recordings, and baseline clinical data were analyzed. We identified early EEG findings associated with absence of seizures on subsequent continuous EEG. Results: Seizures were detected in 70 (29%) patients. A total of 52 patients had their first seizure in the initial 30 minutes of continuous EEG monitoring. Of the remaining 190 patients, 63 had epileptiform discharges on their initial EEG, 24 had triphasic waves, while 103 had no epileptiform abnormalities. Seizures were later detected in 22% (n = 14) of studies with epileptiform discharges on their initial EEG, vs 3% (n = 3) of the studies without epileptiform abnormalities on initial EEG (p < 0.001). In the 3 patients without epileptiform abnormalities on initial EEG but with subsequent seizures, the first epileptiform discharge or electrographic seizure occurred within the first 4 hours of recording. Conclusions: In patients without epileptiform abnormalities during the first 4 hours of recording, no seizures were subsequently detected. Therefore, EEG features early in the recording may indicate a low risk for seizures, and help determine whether extended monitoring is necessary. PMID:23054233

A comparison of the recording of comorbidity in primary and secondary care by using the Charlson Index to predict short-term and long-term survival in a routine linked data cohort.

PubMed

Crooks, C J; West, J; Card, T R

2015-06-05

Hospital admission records provide snapshots of clinical histories for a subset of the population admitted to hospital. In contrast, primary care records provide continuous clinical histories for complete populations, but might lack detail about inpatient stays. Therefore, combining primary and secondary care records should improve the ability of comorbidity scores to predict survival in population-based studies, and provide better adjustment for case-mix differences when assessing mortality outcomes. Cohort study. English primary and secondary care 1 January 2005 to 1 January 2010. All patients 20 years and older registered to a primary care practice contributing to the linked Clinical Practice Research Datalink from England. The performance of the Charlson index with mortality was compared when derived from either primary or secondary care data or both. This was assessed in relation to short-term and long-term survival, age, consultation rate, and specific acute and chronic diseases. 657,264 people were followed up from 1 January 2005. Although primary care recorded more comorbidity than secondary care, the resulting C statistics for the Charlson index remained similar: 0.86 and 0.87, respectively. Higher consultation rates and restricted age bands reduced the performance of the Charlson index, but the index's excellent performance persisted over longer follow-up; the C statistic was 0.87 over 1 year, and 0.85 over all 5 years of follow-up. The Charlson index derived from secondary care comorbidity had a greater effect than primary care comorbidity in reducing the association of upper gastrointestinal bleeding with mortality. However, they had a similar effect in reducing the association of diabetes with mortality. These findings support the use of the Charlson index from linked data and show that secondary care comorbidity coding performed at least as well as that derived from primary care in predicting survival. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A diagnosis of bipolar spectrum disorder predicts diagnostic conversion from unipolar depression to bipolar disorder: a 5-year retrospective study.

PubMed

Woo, Young Sup; Shim, In Hee; Wang, Hee-Ryung; Song, Hoo Rim; Jun, Tae-Youn; Bahk, Won-Myong

2015-03-15

The major aims of this study were to identify factors that may predict the diagnostic conversion from major depressive disorder (MDD) to bipolar disorder (BP) and to evaluate the predictive performance of the bipolar spectrum disorder (BPSD) diagnostic criteria. The medical records of 250 patients with a diagnosis of MDD for at least 5 years were retrospectively reviewed for this study. The diagnostic conversion from MDD to BP was observed in 18.4% of 250 MDD patients, and the diagnostic criteria for BPSD predicted this conversion with high sensitivity (0.870) and specificity (0.917). A family history of BP, antidepressant-induced mania/hypomania, brief major depressive episodes, early age of onset, antidepressant wear-off, and antidepressant resistance were also independent predictors of this conversion. This study was conducted using a retrospective design and did not include structured diagnostic interviews. The diagnostic criteria for BPSD were highly predictive of the conversion from MDD to BP, and conversion was associated with several clinical features of BPSD. Thus, the BPSD diagnostic criteria may be useful for the prediction of bipolar diathesis in MDD patients. Copyright © 2014 Elsevier B.V. All rights reserved.

Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance.

PubMed

Liljeqvist, Henning T G; Muscatello, David; Sara, Grant; Dinh, Michael; Lawrence, Glenda L

2014-09-23

Syndromic surveillance in emergency departments (EDs) may be used to deliver early warnings of increases in disease activity, to provide situational awareness during events of public health significance, to supplement other information on trends in acute disease and injury, and to support the development and monitoring of prevention or response strategies. Changes in mental health related ED presentations may be relevant to these goals, provided they can be identified accurately and efficiently. This study aimed to measure the accuracy of using diagnostic codes in electronic ED presentation records to identify mental health-related visits. We selected a random sample of 500 records from a total of 1,815,588 ED electronic presentation records from 59 NSW public hospitals during 2010. ED diagnoses were recorded using any of ICD-9, ICD-10 or SNOMED CT classifications. Three clinicians, blinded to the automatically generated syndromic grouping and each other's classification, reviewed the triage notes and classified each of the 500 visits as mental health-related or not. A "mental health problem presentation" for the purposes of this study was defined as any ED presentation where either a mental disorder or a mental health problem was the reason for the ED visit. The combined clinicians' assessment of the records was used as reference standard to measure the sensitivity, specificity, and positive and negative predictive values of the automatic classification of coded emergency department diagnoses. Agreement between the reference standard and the automated coded classification was estimated using the Kappa statistic. Agreement between clinician's classification and automated coded classification was substantial (Kappa = 0.73. 95% CI: 0.58 - 0.87). The automatic syndromic grouping of coded ED diagnoses for mental health-related visits was found to be moderately sensitive (68% 95% CI: 46%-84%) and highly specific at 99% (95% CI: 98%-99.7%) when compared with the reference standard in identifying mental health related ED visits. Positive predictive value was 81% (95% CI: 0.57 - 0.94) and negative predictive value was 98% (95% CI: 0.97-0.99). Mental health presentations identified using diagnoses coded with various classifications in electronic ED presentation records offers sufficient accuracy for application in near real-time syndromic surveillance.

Voice correlates of mating success in men: examining "contests" versus "mate choice" modes of sexual selection.

PubMed

Hodges-Simeon, Carolyn R; Gaulin, Steven J C; Puts, David A

2011-06-01

Men's copulatory success can often be predicted by measuring traits involved in male contests and female choice. Previous research has demonstrated relationships between one such vocal trait in men, mean fundamental frequency (F(0)), and the outcomes and indicators of sexual success with women. The present study investigated the role of another vocal parameter, F(0) variation (the within-subject SD in F(0) across the utterance, F(0)-SD), in predicting men's reported number of female sexual partners in the last year. Male participants (N = 111) competed with another man for a date with a woman. Recorded interactions with the competitor ("competitive recording") and the woman ("courtship recording") were analyzed for five non-linguistic vocal parameters: F(0)-SD, mean F(0), intensity, duration, and formant dispersion (D( f ), an acoustic correlate of vocal tract length), as well as dominant and attractive linguistic content. After controlling for age and attitudes toward uncommitted sex (SOI), lower F(0)-SD (i.e., a more monotone voice) and more dominant linguistic content were strong predictors of the number of past-year sexual partners, whereas mean F(0) and D( f ) did not significantly predict past-year partners. These contrasts have implications for the relative importance of male contests and female choice in shaping men's mating success and hence the origins and maintenance of sexually dimorphic traits in humans.

Evaluation of statistical and rainfall-runoff models for predicting historical daily streamflow time series in the Des Moines and Iowa River watersheds

USGS Publications Warehouse

Farmer, William H.; Knight, Rodney R.; Eash, David A.; Kasey J. Hutchinson,; Linhart, S. Mike; Christiansen, Daniel E.; Archfield, Stacey A.; Over, Thomas M.; Kiang, Julie E.

2015-08-24

Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.

Testing hadronic interaction models using a highly granular silicon-tungsten calorimeter

NASA Astrophysics Data System (ADS)

Bilki, B.; Repond, J.; Schlereth, J.; Xia, L.; Deng, Z.; Li, Y.; Wang, Y.; Yue, Q.; Yang, Z.; Eigen, G.; Mikami, Y.; Price, T.; Watson, N. K.; Thomson, M. A.; Ward, D. R.; Benchekroun, D.; Hoummada, A.; Khoulaki, Y.; Cârloganu, C.; Chang, S.; Khan, A.; Kim, D. H.; Kong, D. J.; Oh, Y. D.; Blazey, G. C.; Dyshkant, A.; Francis, K.; Lima, J. G. R.; Salcido, P.; Zutshi, V.; Boisvert, V.; Green, B.; Misiejuk, A.; Salvatore, F.; Kawagoe, K.; Miyazaki, Y.; Sudo, Y.; Suehara, T.; Tomita, T.; Ueno, H.; Yoshioka, T.; Apostolakis, J.; Folger, G.; Ivantchenko, V.; Ribon, A.; Uzhinskiy, V.; Cauwenbergh, S.; Tytgat, M.; Zaganidis, N.; Hostachy, J.-Y.; Morin, L.; Gadow, K.; Göttlicher, P.; Günter, C.; Krüger, K.; Lutz, B.; Reinecke, M.; Sefkow, F.; Feege, N.; Garutti, E.; Laurien, S.; Lu, S.; Marchesini, I.; Matysek, M.; Ramilli, M.; Kaplan, A.; Norbeck, E.; Northacker, D.; Onel, Y.; Kim, E. J.; van Doren, B.; Wilson, G. W.; Wing, M.; Bobchenko, B.; Chadeeva, M.; Chistov, R.; Danilov, M.; Drutskoy, A.; Epifantsev, A.; Markin, O.; Mizuk, R.; Novikov, E.; Popov, V.; Rusinov, V.; Tarkovsky, E.; Besson, D.; Popova, E.; Gabriel, M.; Kiesling, C.; Simon, F.; Soldner, C.; Szalay, M.; Tesar, M.; Weuste, L.; Amjad, M. S.; Bonis, J.; Callier, S.; Conforti di Lorenzo, S.; Cornebise, P.; Doublet, Ph.; Dulucq, F.; Faucci-Giannelli, M.; Fleury, J.; Frisson, T.; Kégl, B.; van der Kolk, N.; Li, H.; Martin-Chassard, G.; Richard, F.; de La Taille, Ch.; Pöschl, R.; Raux, L.; Rouëné, J.; Seguin-Moreau, N.; Anduze, M.; Balagura, V.; Becheva, E.; Boudry, V.; Brient, J.-C.; Cornat, R.; Frotin, M.; Gastaldi, F.; Magniette, F.; Matthieu, A.; Mora de Freitas, P.; Videau, H.; Augustin, J.-E.; David, J.; Ghislain, P.; Lacour, D.; Lavergne, L.; Zacek, J.; Cvach, J.; Gallus, P.; Havranek, M.; Janata, M.; Kvasnicka, J.; Lednicky, D.; Marcisovsky, M.; Polak, I.; Popule, J.; Tomasek, L.; Tomasek, M.; Ruzicka, P.; Sicho, P.; Smolik, J.; Vrba, V.; Zalesak, J.; Jeans, D.; Götze, M.; Calice Collaboration

2015-09-01

A detailed study of hadronic interactions is presented using data recorded with the highly granular CALICE silicon-tungsten electromagnetic calorimeter. Approximately 350,000 selected π- events at energies between 2 and 10 GeV have been studied. The predictions of several physics models available within the GEANT4 simulation tool kit are compared to this data. A reasonable overall description of the data is observed; the Monte Carlo predictions are within 20% of the data, and for many observables much closer. The largest quantitative discrepancies are found in the longitudinal and transverse distributions of reconstructed energy.

The use of video clips in teleconsultation for preschool children with movement disorders.

PubMed

Gorter, Hetty; Lucas, Cees; Groothuis-Oudshoorn, Karin; Maathuis, Carel; van Wijlen-Hempel, Rietje; Elvers, Hans

2013-01-01

To investigate the reliability and validity of video clips in assessing movement disorders in preschool children. The study group included 27 children with neuromotor concerns. The explorative validity group included children with motor problems (n = 21) or with typical development (n = 9). Hempel screening was used for live observation of the child, full recording, and short video clips. The explorative study tested the validity of the clinical classifications "typical" or "suspect." Agreement between live observation and the full recording was almost perfect; Agreement for the clinical classification "typical" or "suspect" was substantial. Agreement between the full recording and short video clips was substantial to moderate. The explorative validity study, based on short video clips and the presence of a neuromotor developmental disorder, showed substantial agreement. Hempel screening enables reliable and valid observation of video clips, but further research is necessary to demonstrate the predictive value.

Extant-only comparative methods fail to recover the disparity preserved in the bird fossil record.

PubMed

Mitchell, Jonathan S

2015-09-01

Most extant species are in clades with poor fossil records, and recent studies of comparative methods show they have low power to infer even highly simplified models of trait evolution without fossil data. Birds are a well-studied radiation, yet their early evolutionary patterns are still contentious. The fossil record suggests that birds underwent a rapid ecological radiation after the end-Cretaceous mass extinction, and several smaller, subsequent radiations. This hypothesized series of repeated radiations from fossil data is difficult to test using extant data alone. By uniting morphological and phylogenetic data on 604 extant genera of birds with morphological data on 58 species of extinct birds from 50 million years ago, the "halfway point" of avian evolution, I have been able to test how well extant-only methods predict the diversity of fossil forms. All extant-only methods underestimate the disparity, although the ratio of within- to between-clade disparity does suggest high early rates. The failure of standard models to predict high early disparity suggests that recent radiations are obscuring deep time patterns in the evolution of birds. Metrics from different models can be used in conjunction to provide more valuable insights than simply finding the model with the highest relative fit. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Predicted sedimentary record of reflected bores

USGS Publications Warehouse

Higman, B.; Gelfenbaum, G.; Lynett, P.; Moore, A.; Jaffe, B.

2007-01-01

Where a steep slope blocks an inrushing tsunami, the tsunami commonly reverses direction as a reflected bore. A simple method for relating vertical and horizontal variation in sediment size to output from numerical models of depth-averaged tsunami flow yields predictions about the sedimentary record of reflected bores: 1. Near the reflector, a abrupt slowing of the flow as the reflected bore passes is recorded by a normally graded layer that drapes preexisting topography. 2. At intermediate distances from the reflector, the deposit consists of a single normally graded bed deposited preferentially in depressions, possibly including a sharp fine-over-coarse contact. This contact records a brief period of erosion as the front of the reflected bore passes. 3. Far seaward of the reflector, grading in the deposit includes two distinct normally graded beds deposited preferentially in depressions separated by an erosional unconformity. The second normally graded bed records the reflected bore.

Digital Recording and Documentation of Endoscopic Procedures: Do Patients and Doctors Think Alike?

PubMed

Willner, Nadav; Peled-Raz, Maya; Shteinberg, Dan; Shteinberg, Michal; Keren, Dean; Rainis, Tova

2016-01-01

Aims and Methods. Conducting a survey study of a large number of patients and gastroenterologists aimed at identifying relevant predictors of interest in digital recording and documentation (DRD) of endoscopic procedures. Outpatients presenting to the endoscopy unit at our institution for an endoscopy examination were anonymously surveyed, regarding their views and opinions of a possible recording of the procedure. A parallel survey for gastroenterologists was conducted. Results. 417 patients and 62 gastroenterologists participated in two parallel surveys regarding DRD of endoscopic procedures. 66.4% of the patients expressed interest in digital documentation of their endoscopic procedure, with 90.5% of them requesting a copy. 43.6% of the physicians supported digital recording while 27.4% opposed it, with 48.4% opposing to making a copy of the recording available to the patient. No sociodemographic or background factors predicted patient's interest in DRD. 66% of the physicians reported having recording facilities in their institutions, but only 43.6% of them stated performing recording. Having institutional guidelines for DRD was found to be the only significant predictor for routine recording. Conclusions. Our study exposes patients' positive views of digital recording and documentation of endoscopic procedures. In contrast, physicians appear to be much more reluctant towards DRD and are centrally motivated by legal concerns when opposing DRD, as well as when supporting it.

[Comparison of the factors influencing children's self-esteem between two parent families and single parent families].

PubMed

Sok, Sohyune R; Shin, Sung Hee

2010-06-01

This study was done to compare factors influencing children's self-esteem between two parent families and single parent families. The participants were 692 children aged 11 to 13 yr (388 in two parent families and 304 in single parent families) recruited from 20 community agencies and 5 elementary schools in Gyeonggi Province and Seoul City, South Korea. Data were collected from May to July, 2007 using a survey questionnaire containing items on self-esteem, internal control, problematic behavior, school record, family hardiness, parent-child communication and social support. The data were analyzed using SPSS 15.0 program and factors affecting children's self-esteem were analyzed by stepwise multiple regression. Scores for the study variables were significantly different between the two groups. The factors influencing children's self-esteem were also different according to family type. For two parent families, internal control, problematic behavior, school record, and parent-child communication significantly predicted the level of self-esteem (adjusted R(2)=.505, p<.001). For single parent families, social support, family hardiness, internal control, problematic behavior, school record, and parent-child communication significantly predicted the level of self-esteem (adjusted R(2)=.444, p<.001). Nurse working with children should consider family type-specific factors influencing their self-esteem.

Electrophysiological auditory responses and language development in infants with periventricular leukomalacia.

PubMed

Avecilla-Ramírez, G N; Ruiz-Correa, S; Marroquin, J L; Harmony, T; Alba, A; Mendoza-Montoya, O

2011-12-01

This study presents evidence suggesting that electrophysiological responses to language-related auditory stimuli recorded at 46weeks postconceptional age (PCA) are associated with language development, particularly in infants with periventricular leukomalacia (PVL). In order to investigate this hypothesis, electrophysiological responses to a set of auditory stimuli consisting of series of syllables and tones were recorded from a population of infants with PVL at 46weeks PCA. A communicative development inventory (i.e., parent report) was applied to this population during a follow-up study performed at 14months of age. The results of this later test were analyzed with a statistical clustering procedure, which resulted in two well-defined groups identified as the high-score (HS) and low-score (LS) groups. The event-induced power of the EEG data recorded at 46weeks PCA was analyzed using a dimensionality reduction approach, resulting in a new set of descriptive variables. The LS and HS groups formed well-separated clusters in the space spanned by these descriptive variables, which can therefore be used to predict whether a new subject will belong to either of these groups. A predictive classification rate of 80% was obtained by using a linear classifier that was trained with a leave-one-out cross-validation technique. 2011 Elsevier Inc. All rights reserved.

Accurate prediction of cardiorespiratory fitness using cycle ergometry in minimally disabled persons with relapsing-remitting multiple sclerosis.

PubMed

Motl, Robert W; Fernhall, Bo

2012-03-01

To examine the accuracy of predicting peak oxygen consumption (VO(2peak)) primarily from peak work rate (WR(peak)) recorded during a maximal, incremental exercise test on a cycle ergometer among persons with relapsing-remitting multiple sclerosis (RRMS) who had minimal disability. Cross-sectional study. Clinical research laboratory. Women with RRMS (n=32) and sex-, age-, height-, and weight-matched healthy controls (n=16) completed an incremental exercise test on a cycle ergometer to volitional termination. Not applicable. Measured and predicted VO(2peak) and WR(peak). There were strong, statistically significant associations between measured and predicted VO(2peak) in the overall sample (R(2)=.89, standard error of the estimate=127.4 mL/min) and subsamples with (R(2)=.89, standard error of the estimate=131.3 mL/min) and without (R(2)=.85, standard error of the estimate=126.8 mL/min) multiple sclerosis (MS) based on the linear regression analyses. Based on the 95% confidence limits for worst-case errors, the equation predicted VO(2peak) within 10% of its true value in 95 of every 100 subjects with MS. Peak VO(2) can be accurately predicted in persons with RRMS who have minimal disability as it is in controls by using established equations and WR(peak) recorded from a maximal, incremental exercise test on a cycle ergometer. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Accuracy of patient's turnover time prediction using RFID technology in an academic ambulatory surgery center.

PubMed

Marchand-Maillet, Florence; Debes, Claire; Garnier, Fanny; Dufeu, Nicolas; Sciard, Didier; Beaussier, Marc

2015-02-01

Patients flow in outpatient surgical unit is a major issue with regards to resource utilization, overall case load and patient satisfaction. An electronic Radio Frequency Identification Device (RFID) was used to document the overall time spent by the patients between their admission and discharge from the unit. The objective of this study was to evaluate how a RFID-based data collection system could provide an accurate prediction of the actual time for the patient to be discharged from the ambulatory surgical unit after surgery. This is an observational prospective evaluation carried out in an academic ambulatory surgery center (ASC). Data on length of stay at each step of the patient care, from admission to discharge, were recorded by a RFID device and analyzed according to the type of surgical procedure, the surgeon and the anesthetic technique. Based on these initial data (n = 1520), patients were scheduled in a sequential manner according to the expected duration of the previous case. The primary endpoint was the difference between actual and predicted time of discharge from the unit. A total of 414 consecutive patients were prospectively evaluated. One hundred seventy four patients (42%) were discharged at the predicted time ± 30 min. Only 24% were discharged behind predicted schedule. Using an automatic record of patient's length of stay would allow an accurate prediction of the discharge time according to the type of surgery, the surgeon and the anesthetic procedure.

Validation of a metered dose inhaler electronic monitoring device: implications for asthma clinical trial use.

PubMed

Pilcher, Janine; Holliday, Mark; Ebmeier, Stefan; McKinstry, Steve; Messaoudi, Fatiha; Weatherall, Mark; Beasley, Richard

2016-01-01

The SmartTouch Ventolin monitor (Adherium, Auckland, New Zealand) is an electronic monitor for use with a Ventolin metered dose inhaler, which records the date and time of inhaler actuations. This technology has the potential to allow in-depth analysis of patterns of inhaler use in clinical trial settings. The aim of this study was to determine the accuracy of the SmartTouch Ventolin monitor in recording Ventolin actuations. 20 SmartTouch Ventolin monitors were attached to Ventolin metered dose inhalers. Bench testing was performed over a 10-week period, to reflect the potential time frame between visits in a clinical trial. Inhaler actuations were recorded in a paper diary, which was compared with data uploaded from the monitors. 2560 actuations were performed during the 10-week study period. Monitor sensitivity for diary-recorded actuations was 99.9% with a lower 97.5% confidence bound of 99.7%. The positive predictive value for diary-recorded actuations was 100% with a 97.5% lower confidence bound of 99.9%. The SmartTouch Ventolin monitor is highly accurate in recording and retaining electronic data. It can be recommended for use in clinical trial settings in which training and quality control systems are incorporated into study protocols to ensure accurate data acquisition.

Validation of a metered dose inhaler electronic monitoring device: implications for asthma clinical trial use

PubMed Central

Pilcher, Janine; Holliday, Mark; Ebmeier, Stefan; McKinstry, Steve; Messaoudi, Fatiha; Weatherall, Mark; Beasley, Richard

2016-01-01

Background The SmartTouch Ventolin monitor (Adherium, Auckland, New Zealand) is an electronic monitor for use with a Ventolin metered dose inhaler, which records the date and time of inhaler actuations. This technology has the potential to allow in-depth analysis of patterns of inhaler use in clinical trial settings. The aim of this study was to determine the accuracy of the SmartTouch Ventolin monitor in recording Ventolin actuations. Methods 20 SmartTouch Ventolin monitors were attached to Ventolin metered dose inhalers. Bench testing was performed over a 10-week period, to reflect the potential time frame between visits in a clinical trial. Inhaler actuations were recorded in a paper diary, which was compared with data uploaded from the monitors. Results 2560 actuations were performed during the 10-week study period. Monitor sensitivity for diary-recorded actuations was 99.9% with a lower 97.5% confidence bound of 99.7%. The positive predictive value for diary-recorded actuations was 100% with a 97.5% lower confidence bound of 99.9%. Conclusions The SmartTouch Ventolin monitor is highly accurate in recording and retaining electronic data. It can be recommended for use in clinical trial settings in which training and quality control systems are incorporated into study protocols to ensure accurate data acquisition. PMID:27026805

Non-Contact Analysis of the Adsorptive Ink Capacity of Nano Silica Pigments on a Printing Coating Base

PubMed Central

Jiang, Bo; Huang, Yu Dong

2014-01-01

Near infrared spectra combined with partial least squares were proposed as a means of non-contact analysis of the adsorptive ink capacity of recording coating materials in ink jet printing. First, the recording coating materials were prepared based on nano silica pigments. 80 samples of the recording coating materials were selected to develop the calibration of adsorptive ink capacity against ink adsorption (g/m2). The model developed predicted samples in the validation set with r2  = 0.80 and SEP  = 1.108, analytical results showed that near infrared spectra had significant potential for the adsorption of ink capacity on the recording coating. The influence of factors such as recording coating thickness, mass ratio silica: binder-polyvinyl alcohol and the solution concentration on the adsorptive ink capacity were studied. With the help of the near infrared spectra, the adsorptive ink capacity of a recording coating material can be rapidly controlled. PMID:25329464

Non-contact analysis of the adsorptive ink capacity of nano silica pigments on a printing coating base.

PubMed

Jiang, Bo; Huang, Yu Dong

2014-01-01

Near infrared spectra combined with partial least squares were proposed as a means of non-contact analysis of the adsorptive ink capacity of recording coating materials in ink jet printing. First, the recording coating materials were prepared based on nano silica pigments. 80 samples of the recording coating materials were selected to develop the calibration of adsorptive ink capacity against ink adsorption (g/m2). The model developed predicted samples in the validation set with r2  = 0.80 and SEP = 1.108, analytical results showed that near infrared spectra had significant potential for the adsorption of ink capacity on the recording coating. The influence of factors such as recording coating thickness, mass ratio silica: binder-polyvinyl alcohol and the solution concentration on the adsorptive ink capacity were studied. With the help of the near infrared spectra, the adsorptive ink capacity of a recording coating material can be rapidly controlled.

Quantifying Calcium Intake in School Age Children: Development and Validation of the Calcium Counts!© Food Frequency Questionnaire

PubMed Central

ZEMEL, BABETTE S.; CAREY, LISA B.; PAULHAMUS, DONNA R.; STALLINGS, VIRGINIA A.; ITTENBACH, RICHARD F.

2014-01-01

Quantifying dietary behavior is difficult and can be intrusive. Calcium, an essential mineral for skeletal development during childhood, is difficult to assess. Few studies have examined the use of food frequency questionnaires (FFQs) for assessing calcium intake in school-age children. This study evaluated the validity and reliability of the Calcium Counts!© FFQ (CCFFQ) for estimating calcium intake in school children in the US. Healthy children, aged 7–10 years (n = 139) completed the CCFFQ and 7-day weighed food records. A subset of subjects completed a second CCFFQ within 3.6 months. Concurrent validity was determined using Pearson correlations between the CCFFQ and food record estimates of calcium intake, and the relationship between quintiles for the two measures. Predictive validity was determined using generalized linear regression models to explore the effects of age, race, and gender. Inter- and intra-individual variability in calcium intake was high (>300 mg/day). Calcium intake was ~300 mg/day higher by CCFFQ compared to food records. Concurrent validity was moderate (r = 0.61) for the entire cohort and higher for selected subgroups. Predictive validity estimates yielded significant relationships between CCFFQ and food record estimates of calcium intake alone and in the presence of such potential effect modifiers as age group, race, and gender. Test–retest reliability was high (r = 0.74). Although calcium intake estimated by the CCFFQ was greater than that measured by food records, the CCFFQ provides valid and reliable estimates of calcium intake in children. The CCFFQ is especially well-suited as a tool to identify children with low calcium intakes. PMID:19621431

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

Stathakis, S; Defoor, D; Linden, P

Purpose: To study the frequency of Multi-Leaf Collimator (MLC) leaf failures, investigate methods to predict them and reduce linac downtime. Methods: A Varian HD120 MLC was used in our study. The hyperterminal MLC errors logged from 06/2012 to 12/2014 were collected. Along with the hyperterminal errors, the MLC motor changes and all other MLC interventions by the linear accelerator engineer were recorded. The MLC dynalog files were also recorded on a daily basis for each treatment and during linac QA. The dynalog files were analyzed to calculate root mean square errors (RMS) and cumulative MLC travel distance per motor. Anmore » in-house MatLab code was used to analyze all dynalog files, record RMS errors and calculate the distance each MLC traveled per day. Results: A total of 269 interventions were recorded over a period of 18 months. Of these, 146 included MLC motor leaf change, 39 T-nut replacements, and 84 MLC cleaning sessions. Leaves close to the middle of each side required the most maintenance. In the A bank, leaves A27 to A40 recorded 73% of all interventions, while the same leaves in the B bank counted for 52% of the interventions. On average, leaves in the middle of the bank had their motors changed approximately every 1500m of travel. Finally, it was found that the number of RMS errors increased prior to an MLC motor change. Conclusion: An MLC dynalog file analysis software was developed that can be used to log daily MLC usage. Our eighteen-month data analysis showed that there is a correlation between the distance an MLC travels, the RMS and the life of the MLC motor. We plan to use this tool to predict MLC motor failures and with proper and timely intervention, reduce the downtime of the linac during clinical hours.« less

Pulse pressure waveform in hydrocephalus: what it is and what it isn't.

PubMed

Czosnyka, Marek; Czosnyka, Zofia; Keong, Nicole; Lavinio, Andreas; Smielewski, Piotr; Momjian, Shahan; Schmidt, Eric A; Petrella, Gianpaolo; Owler, Brian; Pickard, John D

2007-04-15

Apart from its mean value, the pulse waveform of intracranial pressure (ICP) is an essential element of pressure recording. The authors reviewed their experience with the measurement and interpretation of ICP pulse amplitude by referring to a database of recordings in hydrocephalic patients. The database contained computerized pressure recordings from 2100 infusion studies (either lumbar or intraventricular) or overnight ICP monitoring sessions in patients suffering from hydrocephalus of various types (both communicating and noncommunicating), origins, and stages of management (shunt or no shunt). Amplitude was calculated from ICP waveforms by using a spectral analysis methodology. The appearance of a pulse waveform amplitude is positive evidence of a technically correct recording of ICP and helps to distinguish between postural and vasogenic variations in ICP. Pulse amplitude is significantly correlated with the amplitude of cerebral blood flow velocity (R = 0.4, p = 0.012) as assessed using Doppler ultrasonography. Amplitude is positively correlated with a mean ICP (R = 0.21 in idiopathic normal-pressure hydrocephalus [NPH]; number of cases 131; p < 0.01) and resistance to cerebrospinal fluid outflow (R = 0.22) but does not seem to be correlated with cerebrospinal elasticity, dilation of ventricles, or severity of hydrocephalus (NPH score). Amplitude increases slightly with age (R = 0.39, p < 0.01; number of cases 46). A positive association between pulse amplitude and increased ICP during an infusion study is helpful in distinguishing between hydrocephalus and predominant brain atrophy. A large amplitude is associated with a good outcome after shunting (positive predictive power 0.9), whereas a low amplitude has no predictive power in outcome prognostication (0.5). Pulse amplitude is reduced by a properly functioning shunt. Proper recording, detection, and interpretation of ICP pulse waveforms provide clinically useful information about patients suffering from hydrocephalus.

Rapid modeling of complex multi-fault ruptures with simplistic models from real-time GPS: Perspectives from the 2016 Mw 7.8 Kaikoura earthquake

NASA Astrophysics Data System (ADS)

Crowell, B.; Melgar, D.

2017-12-01

The 2016 Mw 7.8 Kaikoura earthquake is one of the most complex earthquakes in recent history, rupturing across at least 10 disparate faults with varying faulting styles, and exhibiting intricate surface deformation patterns. The complexity of this event has motivated the need for multidisciplinary geophysical studies to get at the underlying source physics to better inform earthquake hazards models in the future. However, events like Kaikoura beg the question of how well (or how poorly) such earthquakes can be modeled automatically in real-time and still satisfy the general public and emergency managers. To investigate this question, we perform a retrospective real-time GPS analysis of the Kaikoura earthquake with the G-FAST early warning module. We first perform simple point source models of the earthquake using peak ground displacement scaling and a coseismic offset based centroid moment tensor (CMT) inversion. We predict ground motions based on these point sources as well as simple finite faults determined from source scaling studies, and validate against true recordings of peak ground acceleration and velocity. Secondly, we perform a slip inversion based upon the CMT fault orientations and forward model near-field tsunami maximum expected wave heights to compare against available tide gauge records. We find remarkably good agreement between recorded and predicted ground motions when using a simple fault plane, with the majority of disagreement in ground motions being attributable to local site effects, not earthquake source complexity. Similarly, the near-field tsunami maximum amplitude predictions match tide gauge records well. We conclude that even though our models for the Kaikoura earthquake are devoid of rich source complexities, the CMT driven finite fault is a good enough "average" source and provides useful constraints for rapid forecasting of ground motion and near-field tsunami amplitudes.

Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers.

PubMed

Vaegter, Katarina Kebbon; Lakic, Tatevik Ghukasyan; Olovsson, Matts; Berglund, Lars; Brodin, Thomas; Holte, Jan

2017-03-01

To construct a prediction model for live birth after in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and single-embryo transfer (SET) after 2 days of embryo culture. Prospective observational cohort study. University-affiliated private infertility center. SET in 8,451 IVF/ICSI treatments in 5,699 unselected consecutive couples during 1999-2014. A total of 100 basal patient characteristics and treatment data were analyzed for associations with live birth after IVF/ICSI (adjusted for repeated treatments) and subsequently combined for prediction model construction. Live birth rate (LBR) and performance of live birth prediction model. Embryo score, treatment history, ovarian sensitivity index (OSI; number of oocytes/total dose of FSH administered), female age, infertility cause, endometrial thickness, and female height were all independent predictors of live birth. A prediction model (training data set; n = 5,722) based on these variables showed moderate discrimination, but predicted LBR with high accuracy in subgroups of patients, with LBR estimates ranging from <10% to >40%. Outcomes were similar in an internal validation data set (n = 2,460). Based on 100 variables prospectively recorded during a 15-year period, a model for live birth prediction after strict SET was constructed and showed excellent calibration in internal validation. For the first time, female height qualified as a predictor of live birth after IVF/ICSI. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

The influence of passband limitation on the waveform of extracellular action potential.

PubMed

Mizuhiki, Takashi; Inaba, Kiyonori; Setogawa, Tsuyoshi; Toda, Koji; Ozaki, Shigeru; Shidara, Muneteka

2012-03-01

The duration of the extracellular action potential (EAP) in single neuronal recording has often been used as a clue to infer biochemical, physiological or functional substrate of the recorded neurons, e.g. neurochemical type. However, when recording a neuronal activity, the high-pass filter is routinely used to achieve higher signal-to-noise ratio. Signal processing theory predicts that passband limitation stretches the waveform of discrete brief impulse. To examine whether the duration of filtered EAP could be the reliable measure, we investigated the influence of high-pass filter both by simulation and unfiltered unit recording data from monkey dorsal raphe. Consistent with the findings in recent theoretical study, the unfiltered EAPs displayed the sharp wave without following bumps. The duration of unfiltered EAP was not correlated with that of filtered EAP. Thus the duration of original EAP cannot be estimated from filtered EAP. It is needed to reexamine the EAP duration measured for classifying the neurons whose activities were recorded under the passband limitation in the related studies. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Applying the Seattle Heart Failure Model in the Office Setting in the Era of Electronic Medical Records.

PubMed

Williams, Brent A; Agarwal, Shikhar

2018-02-23

Prediction models such as the Seattle Heart Failure Model (SHFM) can help guide management of heart failure (HF) patients, but the SHFM has not been validated in the office environment. This retrospective cohort study assessed the predictive performance of the SHFM among patients with new or pre-existing HF in the context of an office visit.Methods and Results:SHFM elements were ascertained through electronic medical records at an office visit. The primary outcome was all-cause mortality. A "warranty period" for the baseline SHFM risk estimate was sought by examining predictive performance over time through a series of landmark analyses. Discrimination and calibration were estimated according to the proposed warranty period. Low- and high-risk thresholds were proposed based on the distribution of SHFM estimates. Among 26,851 HF patients, 14,380 (54%) died over a mean 4.7-year follow-up period. The SHFM lost predictive performance over time, with C=0.69 and C<0.65 within 3 and beyond 12 months from baseline respectively. The diminishing predictive value was attributed to modifiable SHFM elements. Discrimination (C=0.66) and calibration for 12-month mortality were acceptable. A low-risk threshold of ∼5% mortality risk within 12 months reflects the 10% of HF patients in the office setting with the lowest risk. The SHFM has utility in the office environment.

Predictive modeling of cardiovascular complications in incident hemodialysis patients.

PubMed

Ion Titapiccolo, J; Ferrario, M; Barbieri, C; Marcelli, D; Mari, F; Gatti, E; Cerutti, S; Smyth, P; Signorini, M G

2012-01-01

The administration of hemodialysis (HD) treatment leads to the continuous collection of a vast quantity of medical data. Many variables related to the patient health status, to the treatment, and to dialyzer settings can be recorded and stored at each treatment session. In this study a dataset of 42 variables and 1526 patients extracted from the Fresenius Medical Care database EuCliD was used to develop and apply a random forest predictive model for the prediction of cardiovascular events in the first year of HD treatment. A ridge-lasso logistic regression algorithm was then applied to the subset of variables mostly involved in the prediction model to get insights in the mechanisms underlying the incidence of cardiovascular complications in this high risk population of patients.

Fire danger index efficiency as a function of fuel moisture and fire behavior.

PubMed

Torres, Fillipe Tamiozzo Pereira; Romeiro, Joyce Machado Nunes; Santos, Ana Carolina de Albuquerque; de Oliveira Neto, Ricardo Rodrigues; Lima, Gumercindo Souza; Zanuncio, José Cola

2018-08-01

Assessment of the performance of forest fire hazard indices is important for prevention and management strategies, such as planning prescribed burnings, public notifications and firefighting resource allocation. The objective of this study was to evaluate the performance of fire hazard indices considering fire behavior variables and susceptibility expressed by the moisture of combustible material. Controlled burns were carried out at different times and information related to meteorological conditions, characteristics of combustible material and fire behavior variables were recorded. All variables analyzed (fire behavior and fuel moisture content) can be explained by the prediction indices. The Brazilian EVAP/P showed the best performance, both at predicting moisture content of the fuel material and fire behavior variables, and the Canadian system showed the best performance to predicting the rate of spread. The coherence of the correlations between the indices and the variables analyzed makes the methodology, which can be applied anywhere, important for decision-making in regions with no records or with only unreliable forest fire data. Copyright © 2018 Elsevier B.V. All rights reserved.

Neural Prediction of Multidimensional Decisions in Monkey Superior Colliculus

NASA Astrophysics Data System (ADS)

Hasegawa, Ryohei P.; Hasegawa, Yukako T.; Segraves, Mark A.

To examine the function of the superior colliculus (SC) in decision-making processes and the application of its single trial activity for “neural mind reading,” we recorded from SC deep layers while two monkeys performed oculomotor go/no-go tasks. We have recently focused on monitoring single trial activities in single SC neurons, and designed a virtual decision function (VDF) to provide a good estimation of single-dimensional decisions (go/no-go decisions for a cue presented at a specific visual field, a response field of each neuron). In this study, we used two VDFs for multidimensional decisions (go/no-go decisions at two cue locations) with the ensemble activity which was simultaneously recorded from a small group (4 to 6) of neurons at both sides of the SC. VDFs predicted cue locations as well as go/no-go decisions. These results suggest that monitoring of ensemble SC activity had sufficient capacity to predict multidimensional decisions on a trial-by-trial basis, which is an ideal candidate to serve for cognitive brain-machine interfaces (BMI) such as two-dimensional word spellers.

The cost of doing business: cost structure of electronic immunization registries.

PubMed

Fontanesi, John M; Flesher, Don S; De Guire, Michelle; Lieberthal, Allan; Holcomb, Kathy

2002-10-01

To predict the true cost of developing and maintaining an electronic immunization registry, and to set the framework for developing future cost-effective and cost-benefit analysis. Primary data collected at three immunization registries located in California, accounting for 90 percent of all immunization records in registries in the state during the study period. A parametric cost analysis compared registry development and maintenance expenditures to registry performance requirements. Data were collected at each registry through interviews, reviews of expenditure records, technical accomplishments development schedules, and immunization coverage rates. The cost of building immunization registries is predictable and independent of the hardware/software combination employed. The effort requires four man-years of technical effort or approximately $250,000 in 1998 dollars. Costs for maintaining a registry were approximately $5,100 per end user per three-year period. There is a predictable cost structure for both developing and maintaining immunization registries. The cost structure can be used as a framework for examining the cost-effectiveness and cost-benefits of registries. The greatest factor effecting improvement in coverage rates was ongoing, user-based administrative investment.

Prediction of Academic Achievement in an NATA-Approved Graduate Athletic Training Education Program

PubMed Central

Keskula, Douglas R.; Sammarone, Paula G.; Perrin, David H.

1995-01-01

The Purpose of this investigation was to determine which information used in the applicant selection process would best predict the final grade point average of students in a National Athletic Trainers Association (NATA) graduate athletic training education program. The criterion variable used was the graduate grade-point average (GPAg) calculated at the completion of the program of study. The predictor variables included: 1) Graduate Record Examination-Quantitative (GRE-Q) scores; and 2) Graduate Record Examination-Verbal (GRE-V) scores, 3) preadmission grade point average (GPAp), 4) total athletic training hours (hours), and 5) curriculum or internship undergraduate athletic training education (program). Data from 55 graduate athletic training students during a 5-year period were evaluated. Stepwise multiple regression analysis indicated that GPAp was a significant predictor of GPAg, accounting for 34% of the variance. GRE-Q, GRE-V, hours, and program did not significantly contribute individually or in combination to the prediction of GPAg. The results of this investigation suggest that, of the variables examined, GPAp is the best predictor of academic success in an NATA-approved graduate athletic training education program. PMID:16558312

Application of Newtonian physics to predict the speed of a gravity racer

NASA Astrophysics Data System (ADS)

Driscoll, H. F.; Bullas, A. M.; King, C. E.; Senior, T.; Haake, S. J.; Hart, J.

2016-07-01

Gravity racing can be studied using numerical solutions to the equations of motion derived from Newton’s second law. This allows students to explore the physics of gravity racing and to understand how design and course selection influences vehicle speed. Using Euler’s method, we have developed a spreadsheet application that can be used to predict the speed of a gravity powered vehicle. The application includes the effects of air and rolling resistance. Examples of the use of the application for designing a gravity racer are presented and discussed. Predicted speeds are compared to the results of an official world record attempt.

Do fertility intentions predict subsequent behavior? Evidence from Peninsular Malaysia.

PubMed

Tan, P C; Tey, N P

1994-01-01

Data from the 1984 Malaysian Population and Family Survey were matched with birth registration records for 1985-87 to determine the accuracy of statements regarding desired family size that were reported in a household survey in predicting subsequent reproductive behavior. The findings of this study were that stated fertility intention provides fairly accurate forecasts of fertility behavior in the subsequent period. In other words, whether a woman has another child is predicted closely by whether she wanted an additional child. Informational, educational, and motivational activities of family planning programs would, therefore, have greater success in reducing family size if fertility intentions were taken into account.

Posterior Beta and Anterior Gamma Oscillations Predict Cognitive Insight

ERIC Educational Resources Information Center

Sheth, Bhavin R.; Sandkuhler, Simone; Bhattacharya, Joydeep

2009-01-01

Pioneering neuroimaging studies on insight have revealed neural correlates of the emotional "Aha!" component of the insight process, but neural substrates of the cognitive component, such as problem restructuring (a key to transformative reasoning), remain a mystery. Here, multivariate electroencephalogram signals were recorded from human…

Validity of verbal autopsy method to determine causes of death among adults in the urban setting of Ethiopia

PubMed Central

2012-01-01

Background Verbal autopsy has been widely used to estimate causes of death in settings with inadequate vital registries, but little is known about its validity. This analysis was part of Addis Ababa Mortality Surveillance Program to examine the validity of verbal autopsy for determining causes of death compared with hospital medical records among adults in the urban setting of Ethiopia. Methods This validation study consisted of comparison of verbal autopsy final diagnosis with hospital diagnosis taken as a “gold standard”. In public and private hospitals of Addis Ababa, 20,152 adult deaths (15 years and above) were recorded between 2007 and 2010. With the same period, a verbal autopsy was conducted for 4,776 adult deaths of which, 1,356 were deceased in any of Addis Ababa hospitals. Then, verbal autopsy and hospital data sets were merged using the variables; full name of the deceased, sex, address, age, place and date of death. We calculated sensitivity, specificity and positive predictive values with 95% confidence interval. Results After merging, a total of 335 adult deaths were captured. For communicable diseases, the values of sensitivity, specificity and positive predictive values of verbal autopsy diagnosis were 79%, 78% and 68% respectively. For non-communicable diseases, sensitivity of the verbal autopsy diagnoses was 69%, specificity 78% and positive predictive value 79%. Regarding injury, sensitivity of the verbal autopsy diagnoses was 70%, specificity 98% and positive predictive value 83%. Higher sensitivity was achieved for HIV/AIDS and tuberculosis, but lower specificity with relatively more false positives. Conclusion These findings may indicate the potential of verbal autopsy to provide cost-effective information to guide policy on communicable and non communicable diseases double burden among adults in Ethiopia. Thus, a well structured verbal autopsy method, followed by qualified physician reviews could be capable of providing reasonable cause specific mortality estimates in Ethiopia. However, the limited generalizability of this study due to the fact that matched verbal autopsy deaths were all in-hospital deaths in an urban center, thus results may not be generalizable to rural home deaths. Such application and refinement of existing verbal autopsy methods holds out the possibility of obtaining replicable, sustainable and internationally comparable mortality statistics of known quality. Similar validation studies need to be undertaken considering the limitation of medical records as “gold standard” since records may not be confirmed using laboratory investigations or medical technologies. The validation studies need to address child and maternal causes of death and possibly all underlying causes of death. PMID:22928712

Pathological tremor prediction using surface EMG and acceleration: potential use in “ON-OFF” demand driven deep brain stimulator design

PubMed Central

Basu, Ishita; Graupe, Daniel; Tuninetti, Daniela; Shukla, Pitamber; Slavin, Konstantin V.; Metman, Leo Verhagen; Corcos, Daniel M.

2013-01-01

Objective We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinson’s disease (PD) and Essential tremor (ET). Approach The tremor prediction algorithm uses a set of spectral (fourier and wavelet) and non-linear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals. Main results The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials and 80.2% for all PD trials. By using a Pearson’s chi-square test, the prediction results are shown to significantly differ from a random prediction outcome. Significance The tremor prediction algorithm can be potentially used for designing the next generation of non-invasive closed-loop predictive ON-OFF controllers for deep brain stimulation (DBS), used for suppressing pathological tremor in such patients. Such a system is based on alternating ON and OFF DBS periods, an incoming tremor being predicted during the time intervals when DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before tremor re-appears so that the patient is tremor-free for the entire DBS ON-OFF cycle as well as the tremor-free DBS OFF interval should be maximized in order to minimize the current injected in the brain and battery usage. PMID:23658233

Pathological tremor prediction using surface electromyogram and acceleration: potential use in ‘ON-OFF’ demand driven deep brain stimulator design

NASA Astrophysics Data System (ADS)

Basu, Ishita; Graupe, Daniel; Tuninetti, Daniela; Shukla, Pitamber; Slavin, Konstantin V.; Verhagen Metman, Leo; Corcos, Daniel M.

2013-06-01

Objective. We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinson’s disease (PD) and essential tremor (ET). Approach. The tremor prediction algorithm uses a set of spectral (Fourier and wavelet) and nonlinear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals. Main results. The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials and 80.2% for all PD trials. By using a Pearson’s chi-square test, the prediction results are shown to significantly differ from a random prediction outcome. Significance. The tremor prediction algorithm can be potentially used for designing the next generation of non-invasive closed-loop predictive ON-OFF controllers for deep brain stimulation (DBS), used for suppressing pathological tremor in such patients. Such a system is based on alternating ON and OFF DBS periods, an incoming tremor being predicted during the time intervals when DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before tremor re-appears so that the patient is tremor-free for the entire DBS ON-OFF cycle and the tremor-free DBS OFF interval should be maximized in order to minimize the current injected in the brain and battery usage.

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Quantitative Sensory Testing Predicts Pregabalin Efficacy in Painful Chronic Pancreatitis

PubMed Central

Olesen, Søren S.; Graversen, Carina; Bouwense, Stefan A. W.; van Goor, Harry; Wilder-Smith, Oliver H. G.; Drewes, Asbjørn M.

2013-01-01

Background A major problem in pain medicine is the lack of knowledge about which treatment suits a specific patient. We tested the ability of quantitative sensory testing to predict the analgesic effect of pregabalin and placebo in patients with chronic pancreatitis. Methods Sixty-four patients with painful chronic pancreatitis received pregabalin (150–300 mg BID) or matching placebo for three consecutive weeks. Analgesic effect was documented in a pain diary based on a visual analogue scale. Responders were defined as patients with a reduction in clinical pain score of 30% or more after three weeks of study treatment compared to baseline recordings. Prior to study medication, pain thresholds to electric skin and pressure stimulation were measured in dermatomes T10 (pancreatic area) and C5 (control area). To eliminate inter-subject differences in absolute pain thresholds an index of sensitivity between stimulation areas was determined (ratio of pain detection thresholds in pancreatic versus control area, ePDT ratio). Pain modulation was recorded by a conditioned pain modulation paradigm. A support vector machine was used to screen sensory parameters for their predictive power of pregabalin efficacy. Results The pregabalin responders group was hypersensitive to electric tetanic stimulation of the pancreatic area (ePDT ratio 1.2 (0.9–1.3)) compared to non-responders group (ePDT ratio: 1.6 (1.5–2.0)) (P = 0.001). The electrical pain detection ratio was predictive for pregabalin effect with a classification accuracy of 83.9% (P = 0.007). The corresponding sensitivity was 87.5% and specificity was 80.0%. No other parameters were predictive of pregabalin or placebo efficacy. Conclusions The present study provides first evidence that quantitative sensory testing predicts the analgesic effect of pregabalin in patients with painful chronic pancreatitis. The method can be used to tailor pain medication based on patient’s individual sensory profile and thus comprises a significant step towards personalized pain medicine. PMID:23469256

Distal and proximal predictors of snacking at work: A daily-survey study.

PubMed

Sonnentag, Sabine; Pundt, Alexander; Venz, Laura

2017-02-01

This study aimed at examining predictors of healthy and unhealthy snacking at work. As proximal predictors we looked at food-choice motives (health motive, affect-regulation motive); as distal predictors we included organizational eating climate, emotional eating, and self-control demands at work. We collected daily survey data from 247 employees, over a period of 2 workweeks. Multilevel structural equation modeling showed that organizational eating climate predicted health as food-choice motive, whereas emotional eating and self-control demands predicted affect regulation as food-choice motive. The health motive, in turn, predicted consuming more fruits and more cereal bars and less sweet snacks; the affect-regulation motive predicted consuming more sweet snacks. Findings highlight the importance of a health-promoting eating climate within the organization and point to the potential harm of high self-control demands at work. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Tactile communication, cooperation, and performance: an ethological study of the NBA.

PubMed

Kraus, Michael W; Huang, Cassey; Keltner, Dacher

2010-10-01

Tactile communication, or physical touch, promotes cooperation between people, communicates distinct emotions, soothes in times of stress, and is used to make inferences of warmth and trust. Based on this conceptual analysis, we predicted that in group competition, physical touch would predict increases in both individual and group performance. In an ethological study, we coded the touch behavior of players from the National Basketball Association (NBA) during the 2008-2009 regular season. Consistent with hypotheses, early season touch predicted greater performance for individuals as well as teams later in the season. Additional analyses confirmed that touch predicted improved performance even after accounting for player status, preseason expectations, and early season performance. Moreover, coded cooperative behaviors between teammates explained the association between touch and team performance. Discussion focused on the contributions touch makes to cooperative groups and the potential implications for other group settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

Emotional inertia prospectively predicts the onset of depressive disorder in adolescence.

PubMed

Kuppens, Peter; Sheeber, Lisa B; Yap, Marie B H; Whittle, Sarah; Simmons, Julian G; Allen, Nicholas B

2012-04-01

Emotional inertia refers to the degree to which a person's current emotional state is predicted by their prior emotional state, reflecting how much it carries over from one moment to the next. Recently, in a cross-sectional study, we showed that high inertia is an important characteristic of the emotion dynamics observed in psychological maladjustment such as depression. In the present study, we examined whether emotional inertia prospectively predicts the onset of first-episode depression during adolescence. Emotional inertia was assessed in a sample of early adolescents (N = 165) based on second-to-second behavioral coding of videotaped naturalistic interactions with a parent. Greater inertia of both negative and positive emotional behaviors predicted the emergence of clinical depression 2.5 years later. The implications of these findings for the understanding of the etiology and early detection of depression are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Emotional intelligence predicts success in medical school.

PubMed

Libbrecht, Nele; Lievens, Filip; Carette, Bernd; Côté, Stéphane

2014-02-01

Accumulating evidence suggests that effective communication and interpersonal sensitivity during interactions between doctors and patients impact therapeutic outcomes. There is an important need to identify predictors of these behaviors, because traditional tests used in medical admissions offer limited predictions of "bedside manners" in medical practice. This study examined whether emotional intelligence would predict the performance of 367 medical students in medical school courses on communication and interpersonal sensitivity. One of the dimensions of emotional intelligence, the ability to regulate emotions, predicted performance in courses on communication and interpersonal sensitivity over the next 3 years of medical school, over and above cognitive ability and conscientiousness. Emotional intelligence did not predict performance on courses on medical subject domains. The results suggest that medical schools may better predict who will communicate effectively and show interpersonal sensitivity if they include measures of emotional intelligence in their admission systems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Empirical models for the prediction of ground motion duration for intraplate earthquakes

NASA Astrophysics Data System (ADS)

Anbazhagan, P.; Neaz Sheikh, M.; Bajaj, Ketan; Mariya Dayana, P. J.; Madhura, H.; Reddy, G. R.

2017-07-01

Many empirical relationships for the earthquake ground motion duration were developed for interplate region, whereas only a very limited number of empirical relationships exist for intraplate region. Also, the existing relationships were developed based mostly on the scaled recorded interplate earthquakes to represent intraplate earthquakes. To the author's knowledge, none of the existing relationships for the intraplate regions were developed using only the data from intraplate regions. Therefore, an attempt is made in this study to develop empirical predictive relationships of earthquake ground motion duration (i.e., significant and bracketed) with earthquake magnitude, hypocentral distance, and site conditions (i.e., rock and soil sites) using the data compiled from intraplate regions of Canada, Australia, Peninsular India, and the central and southern parts of the USA. The compiled earthquake ground motion data consists of 600 records with moment magnitudes ranging from 3.0 to 6.5 and hypocentral distances ranging from 4 to 1000 km. The non-linear mixed-effect (NLMEs) and logistic regression techniques (to account for zero duration) were used to fit predictive models to the duration data. The bracketed duration was found to be decreased with an increase in the hypocentral distance and increased with an increase in the magnitude of the earthquake. The significant duration was found to be increased with the increase in the magnitude and hypocentral distance of the earthquake. Both significant and bracketed durations were predicted higher in rock sites than in soil sites. The predictive relationships developed herein are compared with the existing relationships for interplate and intraplate regions. The developed relationship for bracketed duration predicts lower durations for rock and soil sites. However, the developed relationship for a significant duration predicts lower durations up to a certain distance and thereafter predicts higher durations compared to the existing relationships.

Optimal control model predictions of system performance and attention allocation and their experimental validation in a display design study

NASA Technical Reports Server (NTRS)

Johannsen, G.; Govindaraj, T.

1980-01-01

The influence of different types of predictor displays in a longitudinal vertical takeoff and landing (VTOL) hover task is analyzed in a theoretical study. Several cases with differing amounts of predictive and rate information are compared. The optimal control model of the human operator is used to estimate human and system performance in terms of root-mean-square (rms) values and to compute optimized attention allocation. The only part of the model which is varied to predict these data is the observation matrix. Typical cases are selected for a subsequent experimental validation. The rms values as well as eye-movement data are recorded. The results agree favorably with those of the theoretical study in terms of relative differences. Better matching is achieved by revised model input data.

Predicting attendance at peer-assisted study sessions for statistics: role identity and the theory of planned behavior.

PubMed

White, Katherine M; Thomas, Ian; Johnston, Kim L; Hyde, Melissa K

2008-08-01

Using a prospective study of 77 1st-year psychology students' voluntary attendance at peer-assisted study sessions for statistics, the authors tested the addition of role identity to the theory of planned behavior. The authors used a revised set of role-identity items to capture the personal and social aspects of role identity within a specific behavioral context. At the commencement of the semester, the authors assessed the students' attitudes, subjective norm, perceived behavioral control, role identity, and intention. The authors examined the students' class attendance records 3 months later. Attitudes and perceived behavioral control predicted intention, with intention as the sole predictor of attendance. Role identity also predicted intention, reflecting the importance of the student role identity in influencing decision making related to supplementary academic activities.

The work practice of medical secretaries and the implementation of electronic health records in Denmark.

PubMed

Bertelsen, Pernille; Nøhr, Christian

The introduction of electronic health records will entail substantial organisational changes to the clinical and administrative staff in hospitals. Hospital owners in Denmark have predicted that these changes will render up to half of medical secretaries redundant. The present study however shows that medical secretaries have a great variety of duties, and often act as the organisational "glue" or connecting thread between other professional groups at the hospital. The aim of this study is to obtain a detailed understanding of the pluralism of work tasks the medical secretaries perform. It is concluded that clinicians as well as nurses depend on medical secretaries, and therefore to reduce the number of secretaries because electronic health record systems are implemented needs very careful thinking, planning and discussion with the other professions involved.

A numerical simulation strategy on occupant evacuation behaviors and casualty prediction in a building during earthquakes

NASA Astrophysics Data System (ADS)

Li, Shuang; Yu, Xiaohui; Zhang, Yanjuan; Zhai, Changhai

2018-01-01

Casualty prediction in a building during earthquakes benefits to implement the economic loss estimation in the performance-based earthquake engineering methodology. Although after-earthquake observations reveal that the evacuation has effects on the quantity of occupant casualties during earthquakes, few current studies consider occupant movements in the building in casualty prediction procedures. To bridge this knowledge gap, a numerical simulation method using refined cellular automata model is presented, which can describe various occupant dynamic behaviors and building dimensions. The simulation on the occupant evacuation is verified by a recorded evacuation process from a school classroom in real-life 2013 Ya'an earthquake in China. The occupant casualties in the building under earthquakes are evaluated by coupling the building collapse process simulation by finite element method, the occupant evacuation simulation, and the casualty occurrence criteria with time and space synchronization. A case study of casualty prediction in a building during an earthquake is provided to demonstrate the effect of occupant movements on casualty prediction.

Predicting adverse neonatal outcomes in fetuses with abdominal wall defects using prenatal risk factors.

PubMed

Nicholas, Sara S; Stamilio, David M; Dicke, Jeffery M; Gray, Diana L; Macones, George A; Odibo, Anthony O

2009-10-01

The aim of this study was to determine whether prenatal variables can predict adverse neonatal outcomes in fetuses with abdominal wall defects. A retrospective cohort study that used ultrasound and neonatal records for all cases of gastroschisis and omphalocele seen over a 16-year period. Cases with adverse neonatal outcomes were compared with noncases for multiple candidate predictive factors. Univariable and multivariable statistical methods were used to develop the prediction models, and effectiveness was evaluated using the area under the receiver operating characteristic curve. Of 80 fetuses with gastroschisis, 29 (36%) had the composite adverse outcome, compared with 15 of 33 (47%) live neonates with omphalocele. Intrauterine growth restriction was the only significant variable in gastroschisis, whereas exteriorized liver was the only predictor in omphalocele. The areas under the curve for the prediction models with gastroschisis and omphalocele are 0.67 and 0.74, respectively. Intrauterine growth restriction and exteriorization of the liver are significant predictors of adverse neonatal outcome with gastroschisis and omphalocele.

The use of body mass changes as a practical measure of dehydration in team sports.

PubMed

Harvey, Gemma; Meir, Rudi; Brooks, Lyndon; Holloway, Kate

2008-11-01

Body mass changes, hematocrit, specific gravity and urine colour were recorded during two games of soccer to determine which of these methods was the most practical in a field setting for monitoring dehydration. Members (n=13) of a premiership soccer team with a mean age of 22.6 (+/-4.9) years old, height of 177.8 (+/-7.1)cm and sum of skinfolds (four sites) of 37 (+/-12.8) were invited to participate in this study with 11 participating in each game. Players had weight, hematocrit, specific gravity and urine colour recorded pre- and post-game. Players were allowed to ingest fluid ad libitum throughout the matches with the amount consumed recorded. Urine excretion was also recorded and included in the calculation of final body mass loss (kg). A mean ambient temperature of 21 degrees C and relative humidity 77% was recorded for both games. Pre- and post-game body mass, sweat loss, hematocrit, urine specific gravity and colour were significantly different (p<0.01) for both games. Linear mixed effects models were fitted to the data in order to identify an optimal prediction equation for sweat loss. The model predicting from mass change was clearly the best fitting. The results demonstrate that a change in body mass during a game of soccer is an effective method of monitoring dehydration due to sweat loss when compared to other known methods that may be invasive and inappropriate in the field.

Joint use of eclipse records in China, Japan and Korea for the study of the earth's paleorotation

NASA Astrophysics Data System (ADS)

Li, Zhisen

It has become a new field to use the ancient records of astronomical phenomena for studying the secular change of the earth's rotation. China is very rich in ancient astronomical observations, to which much attention has been given recently. But the continuum of the observational series is affected critically by gaps with lengths of over half a century (TABLE 1). China, Japan and Korea are close neighbours, either adjacent to each other, or separated by a sea, and have a long history of contact and exchange in culture and science. Their ancient astronomies are similar in many aspects, and their astronomical records may be regarded as a unit. Japan and Korea have also accumulated a wealth of ancient records in astronomy, including 232 time observations from AD 840 to 1639 and 149 records of central eclipses from AD 61 to 862 (TABLE 2). However, they have not been utilized in this field. The author has especially analyzed the records of the central eclipse and eclipse time of these three countries, compared their respective merits and shortcomings, and concluded that their joint use may compose a valuable record series for the study of the earth's rotation. This work could change the situation of neglect of the ancient records of east Asia in this field. From TABLE 3 it may be seen that the united series of records are more excellent than any others. The ancient records of astronomical phenomena may also be used to study the evolution of the Earth-Moon system and to test the theoritical predictions of general relativity. The author has completed the analyses of the records of eclipse time, equinox time and the central eclipse, and points out that China, Japan and Korea have the potential for studying this subject. Our hope is laid on the new development of archaeology of remote ages and inscriptions on hones of the Shang Dynasty, and on interdisciplinary cooperation.

Support for the beam focusing hypothesis in the false killer whale.

PubMed

Kloepper, Laura N; Buck, John R; Smith, Adam B; Supin, Alexander Ya; Gaudette, Jason E; Nachtigall, Paul E

2015-08-01

The odontocete sound production system is complex and composed of tissues, air sacs and a fatty melon. Previous studies suggested that the emitted sonar beam might be actively focused, narrowing depending on target distance. In this study, we further tested this beam focusing hypothesis in a false killer whale. Using three linear arrays of hydrophones, we recorded the same emitted click at 2, 4 and 7 m distance and calculated the beamwidth, intensity, center frequency and bandwidth as recorded on each array at every distance. If the whale did not focus her beam, acoustics predicts the intensity would decay with range as a function of spherical spreading and the angular beamwidth would remain constant. On the contrary, our results show that as the distance from the whale to the array increases, the beamwidth is narrower and the received click intensity is higher than that predicted by a spherical spreading function. Each of these measurements is consistent with the animal focusing her beam on a target at a given range. These results support the hypothesis that the false killer whale is 'focusing' its sonar beam, producing a narrower and more intense signal than that predicted by spherical spreading. © 2015. Published by The Company of Biologists Ltd.

Validating computational predictions of night-time ventilation in Stanford's Y2E2 building

NASA Astrophysics Data System (ADS)

Chen, Chen; Lamberti, Giacomo; Gorle, Catherine

2017-11-01

Natural ventilation can significantly reduce building energy consumption, but robust design is a challenging task. We previously presented predictions of natural ventilation performance in Stanford's Y2E2 building using two models with different levels of fidelity, embedded in an uncertainty quantification framework to identify the dominant uncertain parameters and predict quantified confidence intervals. The results showed a slightly high cooling rate for the volume-averaged temperature, and the initial thermal mass temperature and window discharge coefficients were found to have an important influence on the results. To further investigate the potential role of these parameters on the observed discrepancies, the current study is performing additional measurements in the Y2E2 building. Wall temperatures are recorded throughout the nightflush using thermocouples; flow rates through windows are measured using hotwires; and spatial variability in the air temperature is explored. The measured wall temperatures are found the be within the range of our model assumptions, and the measured velocities agree reasonably well with our CFD predications. Considerable local variations in the indoor air temperature have been recorded, largely explaining the discrepancies in our earlier validation study. Future work will therefore focus on a local validation of the CFD results with the measurements. Center for Integrated Facility Engineering (CIFE).

Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study.

PubMed

Lee, Theresa M; Tu, Karen; Wing, Laura L; Gershon, Andrea S

2017-05-15

Little is known about using electronic medical records to identify patients with chronic obstructive pulmonary disease to improve quality of care. Our objective was to develop electronic medical record algorithms that can accurately identify patients with obstructive pulmonary disease. A retrospective chart abstraction study was conducted on data from the Electronic Medical Record Administrative data Linked Database (EMRALD ® ) housed at the Institute for Clinical Evaluative Sciences. Abstracted charts provided the reference standard based on available physician-diagnoses, chronic obstructive pulmonary disease-specific medications, smoking history and pulmonary function testing. Chronic obstructive pulmonary disease electronic medical record algorithms using combinations of terminology in the cumulative patient profile (CPP; problem list/past medical history), physician billing codes (chronic bronchitis/emphysema/other chronic obstructive pulmonary disease), and prescriptions, were tested against the reference standard. Sensitivity, specificity, and positive/negative predictive values (PPV/NPV) were calculated. There were 364 patients with chronic obstructive pulmonary disease identified in a 5889 randomly sampled cohort aged ≥ 35 years (prevalence = 6.2%). The electronic medical record algorithm consisting of ≥ 3 physician billing codes for chronic obstructive pulmonary disease per year; documentation in the CPP; tiotropium prescription; or ipratropium (or its formulations) prescription and a chronic obstructive pulmonary disease billing code had sensitivity of 76.9% (95% CI:72.2-81.2), specificity of 99.7% (99.5-99.8), PPV of 93.6% (90.3-96.1), and NPV of 98.5% (98.1-98.8). Electronic medical record algorithms can accurately identify patients with chronic obstructive pulmonary disease in primary care records. They can be used to enable further studies in practice patterns and chronic obstructive pulmonary disease management in primary care. NOVEL ALGORITHM SEARCH TECHNIQUE: Researchers develop an algorithm that can accurately search through electronic health records to find patients with chronic lung disease. Mining population-wide data for information on patients diagnosed and treated with chronic obstructive pulmonary disease (COPD) in primary care could help inform future healthcare and spending practices. Theresa Lee at the University of Toronto, Canada, and colleagues used an algorithm to search electronic medical records and identify patients with COPD from doctors' notes, prescriptions and symptom histories. They carefully adjusted the algorithm to improve sensitivity and predictive value by adding details such as specific medications, physician codes related to COPD, and different combinations of terminology in doctors' notes. The team accurately identified 364 patients with COPD in a randomly-selected cohort of 5889 people. Their results suggest opportunities for broader, informative studies of COPD in wider populations.

Can a Resident's Publication Record Predict Fellowship Publications?

PubMed Central

Prasad, Vinay; Rho, Jason; Selvaraj, Senthil; Cheung, Mike; Vandross, Andrae; Ho, Nancy

2014-01-01

Background Internal medicine fellowship programs have an incentive to select fellows who will ultimately publish. Whether an applicant's publication record predicts long term publishing remains unknown. Methods Using records of fellowship bound internal medicine residents, we analyzed whether publications at time of fellowship application predict publications more than 3 years (2 years into fellowship) and up to 7 years after fellowship match. We calculate the sensitivity, specificity, positive and negative predictive values and likelihood ratios for every cutoff number of application publications, and plot a receiver operator characteristic curve of this test. Results Of 307 fellowship bound residents, 126 (41%) published at least one article 3 to 7 years after matching, and 181 (59%) of residents do not publish in this time period. The area under the receiver operator characteristic curve is 0.59. No cutoff value for application publications possessed adequate test characteristics. Conclusion The number of publications an applicant has at time of fellowship application is a poor predictor of who publishes in the long term. These findings do not validate the practice of using application publications as a tool for selecting fellows. PMID:24658088

Can a resident's publication record predict fellowship publications?

PubMed

Prasad, Vinay; Rho, Jason; Selvaraj, Senthil; Cheung, Mike; Vandross, Andrae; Ho, Nancy

2014-01-01

Internal medicine fellowship programs have an incentive to select fellows who will ultimately publish. Whether an applicant's publication record predicts long term publishing remains unknown. Using records of fellowship bound internal medicine residents, we analyzed whether publications at time of fellowship application predict publications more than 3 years (2 years into fellowship) and up to 7 years after fellowship match. We calculate the sensitivity, specificity, positive and negative predictive values and likelihood ratios for every cutoff number of application publications, and plot a receiver operator characteristic curve of this test. Of 307 fellowship bound residents, 126 (41%) published at least one article 3 to 7 years after matching, and 181 (59%) of residents do not publish in this time period. The area under the receiver operator characteristic curve is 0.59. No cutoff value for application publications possessed adequate test characteristics. The number of publications an applicant has at time of fellowship application is a poor predictor of who publishes in the long term. These findings do not validate the practice of using application publications as a tool for selecting fellows.

Prediction of Neonatal Hyperthyroidism.

PubMed

Banigé, Maïa; Polak, Michel; Luton, Dominique

2018-06-01

To assess whether it is possible to identify the neonatal predictors of neonatal hyperthyroidism at the presymptomatic stage of the disease. This retrospective multicenter study in 10 maternity units was based on the medical records of all patients monitored for a pregnancy between January 1, 2007, and January 1, 2014. Among 280 000 births, 2288 medical records of women with thyroid dysfunction were selected and screened. Of these, 415 women had Graves disease and were positive for thyrotropin receptor antibody during pregnancy, and were included. A thyroid-stimulating hormone (TSH) level of less than 0.90 mIU/L between days 3 and 7 of life predicted neonatal hyperthyroidism with a sensitivity 78% (95% CI, 74%-82%) and a and specificity of 99% (95% CI, 98%-100%), a positive predictive value of 90% (95% CI, 87%-93%), a negative predictive value of 98% (95% CI, 97%-99%), and an area under the receiver operating characteristic curve of 0.99 (95% CI, 0.97-1.0). A thyrotropin receptor antibody (TRAb) elimination time was calculated using the equation: 7.28 + 2.88 × log() + 11.62 log(TRAb 2 ). All newborns with a TSH level of less than 0.90 mIU/L should be examined by a pediatrician. Using TSH, it is possible to screen for neonatal hypothyroidism and for neonatal hyperthyroidism with a TSH cutoff of 0.90 mIU/L, and this shows the relevance of our study in terms of public health. Copyright © 2018 Elsevier Inc. All rights reserved.

Predicting Future Suicide Attempts Among Adolescent and Emerging Adult Psychiatric Emergency Patients

PubMed Central

Horwitz, Adam G.; Czyz, Ewa K.; King, Cheryl A.

2014-01-01

Objective The purpose of this study was to longitudinally examine specific characteristics of suicidal ideation in combination with histories of suicide attempts and non-suicidal self-injury (NSSI) to best evaluate risk for a future attempt among high-risk adolescents and emerging adults. Method Participants in this retrospective medical record review study were 473 (53% female; 69% Caucasian) consecutive patients, ages 15–24 years (M = 19.4 years) who presented for psychiatric emergency (PE) services during a 9-month period. These patients’ medical records, including a clinician-administered Columbia-Suicide Severity Rating Scale, were coded at the index visit and at future visits occurring within the next 18 months. Logistic regression models were used to predict suicide attempts during this period. Results SES, suicidal ideation severity (i.e., intent, method), suicidal ideation intensity (i.e., frequency, controllability), a lifetime history of suicide attempt, and a lifetime history of NSSI were significant independent predictors of a future suicide attempt. Suicidal ideation added incremental validity to the prediction of future suicide attempts above and beyond the influence of a past suicide attempt, whereas a lifetime history of NSSI did not. Sex moderated the relationship between the duration of suicidal thoughts and future attempts (predictive for males, but not females). Conclusions Results suggest value in incorporating both past behaviors and current thoughts into suicide risk formulation. Furthermore, suicidal ideation duration warrants additional examination as a potential critical factor for screening assessments evaluating suicide risk among high-risk samples, particularly for males. PMID:24871489

Effect of Spatio-Temporal Variability of Rainfall on Stream flow Prediction of Birr Watershed

NASA Astrophysics Data System (ADS)

Demisse, N. S.; Bitew, M. M.; Gebremichael, M.

2012-12-01

The effect of rainfall variability on our ability to forecast flooding events was poorly studied in complex terrain region of Ethiopia. In order to establish relation between rainfall variability and stream flow, we deployed 24 rain gauges across Birr watershed. Birr watershed is a medium size mountainous watershed with an area of 3000 km2 and elevation ranging between 1435 m.a.s.l and 3400 m.a.s.l in the central Ethiopia highlands. One summer monsoon rainfall of 2012 recorded at high temporal scale of 15 minutes interval and stream flow recorded at an hourly interval in three sub-watershed locations representing different scales were used in this study. Based on the data obtained from the rain gauges and stream flow observations, we quantify extent of temporal and spatial variability of rainfall across the watershed using standard statistical measures including mean, standard deviation and coefficient of variation. We also establish rainfall-runoff modeling system using a physically distributed hydrological model: the Soil and Water Assessment Tool (SWAT) and examine the effect of rainfall variability on stream flow prediction. The accuracy of predicted stream flow is measured through direct comparison with observed flooding events. The results demonstrate the significance of relation between stream flow prediction and rainfall variability in the understanding of runoff generation mechanisms at watershed scale, determination of dominant water balance components, and effect of variability on accuracy of flood forecasting activities.

A model for national outcome audit in vascular surgery.

PubMed

Prytherch, D R; Ridler, B M; Beard, J D; Earnshaw, J J

2001-06-01

The aim was to model vascular surgical outcome in a national study using POSSUM scoring. One hundred and twenty-one British and Irish surgeons completed data questionnaires on patients undergoing arterial surgery under their care (mean 12 patients, range 1-49) in May/June 1998. A total of 1480 completed data records were available for logistic regression analysis using P-POSSUM methodology. Information collected included all POSSUM data items plus other factors thought to have a significant bearing on patient outcome: "extra items". The main outcome measures were death and major postoperative complications. The data were checked and inconsistent records were excluded. The remaining 1313 were divided into two sets for analysis. The first "training" set was used to obtain logistic regression models that were applied prospectively to the second "test" dataset. using POSSUM data items alone, it was possible to predict both mortality and morbidity after vascular reconstruction using P-POSSUM analysis. The addition of the "extra items" found significant in regression analysis did not significantly improve the accuracy of prediction. It was possible to predict both mortality and morbidity derived from the preoperative physiology components of the POSSUM data items alone. this study has shown that P-POSSUM methodology can be used to predict outcome after arterial surgery across a range of surgeons in different hospitals and could form the basis of a national outcome audit. It was also possible to obtain accurate models for both mortality and major morbidity from the POSSUM physiology scores alone. Copyright 2001 Harcourt Publishers Limited.

Validity of the Apple iPhone® /iPod Touch® as an accelerometer-based physical activity monitor: a proof-of-concept study.

PubMed

Nolan, Meaghan; Mitchell, J Ross; Doyle-Baker, Patricia K

2014-05-01

The popularity of smartphones has led researchers to ask if they can replace traditional tools for assessing free-living physical activity. Our purpose was to establish proof-of-concept that a smartphone could record acceleration during physical activity, and those data could be modeled to predict activity type (walking or running), speed (km·h-1), and energy expenditure (METs). An application to record and e-mail accelerations was developed for the Apple iPhone®/iPod Touch®. Twenty-five healthy adults performed treadmill walking (4.0 km·h-1 to 7.2 km·h-1) and running (8.1 km·h-1 to 11.3 km·h-1) wearing the device. Criterion energy expenditure measurements were collected via metabolic cart. Activity type was classified with 99% accuracy. Speed was predicted with a bias of 0.02 km·h-1 (SEE: 0.57 km·h-1) for walking, -0.03 km·h-1 (SEE: 1.02 km·h-1) for running. Energy expenditure was predicted with a bias of 0.35 METs (SEE: 0.75 METs) for walking, -0.43 METs (SEE: 1.24 METs) for running. Our results suggest that an iPhone/iPod Touch can predict aspects of locomotion with accuracy similar to other accelerometer-based tools. Future studies may leverage this and the additional features of smartphones to improve data collection and compliance.

Fitting neuron models to spike trains.

PubMed

Rossant, Cyrille; Goodman, Dan F M; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K; Brette, Romain

2011-01-01

Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model.

Experiments with the living dead: Plants as monitors and recorders of Biosphere Geosphere interactions.

NASA Astrophysics Data System (ADS)

Lomax, Barry; Fraser, Wesley

2016-04-01

Understanding variations in the Earth's climate history will enhance our understanding of and capacity to predict future climate change. Importantly this information can then be used to reduce uncertainty around future climate change predictions. However to achieve this, it is necessary to develop well constrained and robustly tested palaeo-proxies. Plants are innately coupled to the atmosphere requiring both sunlight and CO2 to drive photosynthesis and carbon assimilation. When combined with their resilience and persistence, the study of plant responses to climate change in concert with the analysis of fossil plants offer the opportunity to monitor past atmospheric conditions and infer palaeoclimate change. In this presentation we highlight how this approach is leading to the development of mechanistic palaeoproxies tested on palaeobotanically relevant extant species showing that plant fossils can be used as both monitors and geochemical recorders of atmospheric changes.

What can we learn about the dynamics of DO-events from studying the high resolution ice core records?

NASA Astrophysics Data System (ADS)

Ditlevsen, Peter

2017-04-01

The causes for and possible predictions of rapid climate changes are poorly understood. The most pronounced changes observed, beside the glacial terminations, are the Dansgaard-Oeschger events. Present day general circulation climate models simulating glacial conditions are not capable of reproducing these rapid shifts. It is thus not known if they are due to bifurcations in the structural stability of the climate or if they are induced by stochastic fluctuations. By analyzing a high resolution ice core record we exclude the bifurcation scenario, which strongly suggests that they are noise induced and thus have very limited predictability. Ref: Peter Ditlevsen, "Tipping points in the climate system", in Nonlinear and Stochastic Climate Dynamics, Cambridge University Press (C. Franzke and T. O'Kane, eds.) (2016) P. D. Ditlevsen and S. Johnsen, "Tipping points: Early warning and wishful thinking", Geophys. Res. Lett., 37, L19703, 2010

The role of abnormal fetal heart rate in scheduling chorionic villus sampling.

PubMed

Yagel, S; Anteby, E; Ron, M; Hochner-Celnikier, D; Achiron, R

1992-09-01

To assess the value of fetal heart rate (FHR) measurements in predicting spontaneous fetal loss in pregnancies scheduled for chorionic villus sampling (CVS). A prospective descriptive study. Two hospital departments of obstetrics and gynaecology in Israel. 114 women between 9 and 11 weeks gestation scheduled for chorionic villus sampling (CVS). Fetal heart rate was measured by transvaginal Doppler ultrasound and compared with a monogram established from 75 fetuses. Whenever a normal FHR was recorded, CVS was performed immediately. 106 women had a normal FHR and underwent CVS; two of these pregnancies ended in miscarriage. In five pregnancies no fetal heart beats could be identified and fetal death was diagnosed. In three pregnancies an abnormal FHR was recorded and CVS was postponed; all three pregnancies ended in miscarriage within 2 weeks. Determination of FHR correlated with crown-rump length could be useful in predicting spontaneous miscarriage before performing any invasive procedure late in the first trimester.

Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements.

PubMed

Marschollek, M; Nemitz, G; Gietzelt, M; Wolf, K H; Meyer Zu Schwabedissen, H; Haux, R

2009-08-01

Falls are among the predominant causes for morbidity and mortality in elderly persons and occur most often in geriatric clinics. Despite several studies that have identified parameters associated with elderly patients' fall risk, prediction models -- e.g., based on geriatric assessment data -- are currently not used on a regular basis. Furthermore, technical aids to objectively assess mobility-associated parameters are currently not used. To assess group differences in clinical as well as common geriatric assessment data and sensory gait measurements between fallers and non-fallers in a geriatric sample, and to derive and compare two prediction models based on assessment data alone (model #1) and added sensory measurement data (model #2). For a sample of n=110 geriatric in-patients (81 women, 29 men) the following fall risk-associated assessments were performed: Timed 'Up & Go' (TUG) test, STRATIFY score and Barthel index. During the TUG test the subjects wore a triaxial accelerometer, and sensory gait parameters were extracted from the data recorded. Group differences between fallers (n=26) and non-fallers (n=84) were compared using Student's t-test. Two classification tree prediction models were computed and compared. Significant differences between the two groups were found for the following parameters: time to complete the TUG test, transfer item (Barthel), recent falls (STRATIFY), pelvic sway while walking and step length. Prediction model #1 (using common assessment data only) showed a sensitivity of 38.5% and a specificity of 97.6%, prediction model #2 (assessment data plus sensory gait parameters) performed with 57.7% and 100%, respectively. Significant differences between fallers and non-fallers among geriatric in-patients can be detected for several assessment subscores as well as parameters recorded by simple accelerometric measurements during a common mobility test. Existing geriatric assessment data may be used for falls prediction on a regular basis. Adding sensory data improves the specificity of our test markedly.

Middle School Student Records as Dropout Indicators

ERIC Educational Resources Information Center

Gregg, William Sherman

2010-01-01

Dropping out of school is associated with a wide array of negative outcomes and the extraordinarily high United States dropout rate has brought the issue to the forefront of American education. This study investigated normally collected middle school data from a suburban Colorado school district to determine the predictive value toward students…

Predicting county-level southern pine beetle outbreaks from neighborhood patterns

USDA-ARS?s Scientific Manuscript database

The southern pine beetle (Dendroctonus frontalis, Coleoptera: Curculionidae) is the most destructive insect in southern forests. States have kept county-level records on the locations of beetle outbreaks for the past forty-eight years. In this study, we seek to determine how accurately patterns of c...

Prediction of Residential Independence of Special Education High School Students.

ERIC Educational Resources Information Center

Heal, Laird W.; Rusch, Frank

1994-01-01

The residential independence of students with disabilities who had exited high school was assessed, using data from 2,686 interviewees in the National Longitudinal Transition Study. Individual characteristics, such as intelligence, living skills, and bad conduct records, were better predictors of postschool living arrangement status than were…

Intra-Personal and Extra-Personal Predictors of Suicide Attempts of South Korean Adolescents

ERIC Educational Resources Information Center

Lee, Ji-Young; Bae, Sung-Man

2015-01-01

The purpose of this study was to explore significant variables predicting adolescent suicidal attempts. Socio-environmental variables such as gender, school record, school grade, school adaptation, and family intimacy together with intra-individual variables including depression, anxiety, delinquency, stress, and self-esteem were considered as…

Estimating air drying times of lumber with multiple regression

Treesearch

William T. Simpson

2004-01-01

In this study, the applicability of a multiple regression equation for estimating air drying times of red oak, sugar maple, and ponderosa pine lumber was evaluated. The equation allows prediction of estimated air drying times from historic weather records of temperature and relative humidity at any desired location.

Predictors of Quality Verbal Engagement in Third-Grade Literature Discussions

ERIC Educational Resources Information Center

Young, Chase

2014-01-01

This study investigates how reading ability and personality traits predict the quality of verbal discussions in peer-led literature circles. Third grade literature discussions were recorded, transcribed, and coded. The coded statements and questions were quantified into a quality of engagement score. Through multiple linear regression, the…

Predictive factors for the regression of canine transmissible venereal tumor during vincristine therapy.

PubMed

Scarpelli, Karime C; Valladão, Maria L; Metze, Konradin

2010-03-01

Canine transmissible venereal tumor (CTVT) is a neoplasm transmitted by transplantation. Monochemotherapy with vincristine is considered to be effective, but treatment time until complete clinical remission may vary. The aim of this study was to determine which clinical data at diagnosis could predict the responsiveness of CTVT to vincristine chemotherapy. One hundred dogs with CTVT entered this prospective study. The animals were treated with vincristine sulfate (0.025 mg/kg) at weekly intervals until the tumor had macroscopically disappeared. The time to complete remission was recorded. A multivariate Cox regression model indicated that larger tumor mass, increased age and therapy during hot and rainy months were independent significant unfavorable predictive factors retarding remission, whereas sex, weight, status as owned dog or breed were of no predictive relevance. Further studies are necessary to investigate whether these results are due to changes in immunological response mechanisms in animals with a diminished immune surveillance, resulting in delays in tumor regression. 2008 Elsevier Ltd. All rights reserved.

Early Shear Failure Prediction in Incremental Sheet Forming Process Using FEM and ANN

NASA Astrophysics Data System (ADS)

Moayedfar, Majid; Hanaei, Hengameh; Majdi Rani, Ahmad; Musa, Mohd Azam Bin; Sadegh Momeni, Mohammad

2018-03-01

The application of incremental sheet forming process as a rapid forming technique is rising in variety of industries such as aerospace, automotive and biomechanical purposes. However, the sheet failure is a big challenge in this process which leads wasting lots of materials. Hence, this study tried to propose a method to predict the early sheet failure in this process using mathematical solution. For the feasibility of the study, design of experiment with the respond surface method is employed to extract a set of experiments data for the simulation. The significant forming parameters were recognized and their integration was used for prediction system. Then, the results were inserted to the artificial neural network as input parameters to predict a vast range of applicable parameters avoiding sheet failure in ISF. The value of accuracy R2 ∼0.93 was obtained and the maximum sheet stretch in the depth of 25mm were recorded. The figures generate from the trend of interaction between effective parameters were provided for future studies.

Predicting asthma exacerbations using artificial intelligence.

PubMed

Finkelstein, Joseph; Wood, Jeffrey

2013-01-01

Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

Associations between age at first calving and subsequent lactation performance in UK Holstein and Holstein-Friesian dairy cows

PubMed Central

Eastham, Neil T.; Coates, Amy; Cripps, Peter; Richardson, Henry; Smith, Robert

2018-01-01

Lactation records from 396,534 pedigree Holstein and Holstein-Friesian primiparous cows from 6,985 UK milk recorded herds, calving for the first time during the period between the 1st of January 2006 and the 31st of December 2008, were examined in order to determine the associations between age at first calving (AFC) and subsequent production, udder health, fertility and survivability parameters. Heifers were grouped by AFC into single month classes ranging from 21 to 42 months. Mixed effects multivariable regression modelling was used for data analysis. Mean and median AFC were 29.1 and 28 months respectively. Within the study, only 48,567 heifers (12.3% of the studied population) calved for the first time at 24 months of age or younger. 162,157 heifers (40.9%) were 30 months or older at their first calving. An increased AFC was associated with increased first lactation milk, fat and protein yields. The lowest predicted mean 305-day yield (6,617kgs; 95% confidence interval (CI): 6,546–6,687 kgs) was recorded for the 21 month AFC class, significantly lower than any other class. The 36 month AFC class had the highest predicted mean (7,774 kgs; 95% CI: 7,737–7,811 kgs). However, an increased AFC was also associated with increased calving interval and increased first lactation somatic cell count (SCC). Animals calving at 21 months had a predicted mean lactation SCC of 72,765 (95% CI: 68427–77378). Animals calving at 36 months of age had a predicted mean lactation SCC of 86,648 (95% CI: 84,499–88,853). Importantly, an increased AFC was also associated with decreased lifetime daily milk yield and decreased likelihood of calving for a second successive time. Animals calving at 22 months of age had a predicted mean daily lifetime milk yield of 15.24 kgs (95% CI: 15.06–15.35); animals calving at 36 months of age had a predicted mean daily lifetime milk yield of 12.83 kgs (95% CI: 12.76–12.91). Our results highlight the importance of achieving a lower age at first calving which was here associated with improved udder health, increased lifetime daily milk yield, improved reproductive performance and increased likelihood of calving for a second time. PMID:29897929

Associations between age at first calving and subsequent lactation performance in UK Holstein and Holstein-Friesian dairy cows.

PubMed

Eastham, Neil T; Coates, Amy; Cripps, Peter; Richardson, Henry; Smith, Robert; Oikonomou, Georgios

2018-01-01

Lactation records from 396,534 pedigree Holstein and Holstein-Friesian primiparous cows from 6,985 UK milk recorded herds, calving for the first time during the period between the 1st of January 2006 and the 31st of December 2008, were examined in order to determine the associations between age at first calving (AFC) and subsequent production, udder health, fertility and survivability parameters. Heifers were grouped by AFC into single month classes ranging from 21 to 42 months. Mixed effects multivariable regression modelling was used for data analysis. Mean and median AFC were 29.1 and 28 months respectively. Within the study, only 48,567 heifers (12.3% of the studied population) calved for the first time at 24 months of age or younger. 162,157 heifers (40.9%) were 30 months or older at their first calving. An increased AFC was associated with increased first lactation milk, fat and protein yields. The lowest predicted mean 305-day yield (6,617kgs; 95% confidence interval (CI): 6,546-6,687 kgs) was recorded for the 21 month AFC class, significantly lower than any other class. The 36 month AFC class had the highest predicted mean (7,774 kgs; 95% CI: 7,737-7,811 kgs). However, an increased AFC was also associated with increased calving interval and increased first lactation somatic cell count (SCC). Animals calving at 21 months had a predicted mean lactation SCC of 72,765 (95% CI: 68427-77378). Animals calving at 36 months of age had a predicted mean lactation SCC of 86,648 (95% CI: 84,499-88,853). Importantly, an increased AFC was also associated with decreased lifetime daily milk yield and decreased likelihood of calving for a second successive time. Animals calving at 22 months of age had a predicted mean daily lifetime milk yield of 15.24 kgs (95% CI: 15.06-15.35); animals calving at 36 months of age had a predicted mean daily lifetime milk yield of 12.83 kgs (95% CI: 12.76-12.91). Our results highlight the importance of achieving a lower age at first calving which was here associated with improved udder health, increased lifetime daily milk yield, improved reproductive performance and increased likelihood of calving for a second time.

Attenuation Characteristics of Strong Motions during the 2016 Kumamoto Earthquakes including Near-Field Records

NASA Astrophysics Data System (ADS)

Si, H.; Koketsu, K.; Miyake, H.; Ibrahim, R.

2016-12-01

During the two major earthquakes occurred in Kumamoto prefecture, at 21:26 on 14 April, 2016 (Mw 6.2, GCMT), and at 1:25 on 16 April, 2016 (Mw7.0, GCMT), a large number of strong ground motions were recorded, including those very close to the surface fault. In this study, we will discuss the attenuation characteristics of strong ground motions observed during the earthquakes. The data used in this study are mainly observed by K-NET, KiK-net, Osaka University, JMA and Kumamoto prefecture. The 5% damped acceleration response spectra (GMRotI50) are calculated based on the method proposed by Boore et al. (2006). PGA and PGV is defined as the larger one among the PGAs and PGVs of two horizontal components. The PGA, PGV, and GMRotI50 data were corrected to the bedrock with Vs of 1.5km/s based on the method proposed by Si et al. (2016) using the average shear wave velocity (Vs30) and the thickness of sediments over the bedrock. The thickness is estimated based on the velocity structure model provided by J-SHIS. We use a source model proposed by Koketsu et al. (2016) to calculate the fault distance and the median distance (MED) which defined as the closest distance from a station to the median line of the fault plane (Si et al., 2014). We compared the observed PGAs, PGVs, and GMRotI50 with the GMPEs developed in Japan using MED (Si et al., 2014). The predictions by the GMPEs are generally consistent with the observations during the two Kumamoto earthquakes. The results of the comparison also indicated that, (1) strong motion records from the earthquake on April 14th are generally consistent with the predictions by GMPE, however, at the periods of 0.5 to 2 seconds, several records close to the fault plane show larger amplitudes than the predictions by GMPE, including the KiK-net station Mashiki (KMMH16); (2) for the earthquake on April 16, the PGAs and GMRotI50 at periods from 0.1s to 0.4s with short distance from the fault plane are slightly smaller than the predictions by GMPE. On the other hand, for the PGVs and GMRotI50s at periods longer than 2.5s with MED larger than about 100 km, the observations are generally larger than the prediction by GMPE, showing smaller attenuation.

A joint computational and experimental study to evaluate Inconel-sheathed thermocouple performance in flames.

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

Brundage, Aaron L.; Nicolette, Vernon F.; Donaldson, A. Burl

2005-09-01

A joint experimental and computational study was performed to evaluate the capability of the Sandia Fire Code VULCAN to predict thermocouple response temperature. Thermocouple temperatures recorded by an Inconel-sheathed thermocouple inserted into a near-adiabatic flat flame were predicted by companion VULCAN simulations. The predicted thermocouple temperatures were within 6% of the measured values, with the error primarily attributable to uncertainty in Inconel 600 emissivity and axial conduction losses along the length of the thermocouple assembly. Hence, it is recommended that future thermocouple models (for Inconel-sheathed designs) include a correction for axial conduction. Given the remarkable agreement between experiment and simulation,more » it is recommended that the analysis be repeated for thermocouples in flames with pollutants such as soot.« less

Monocyte to HDL ratio in prediction of BMS restenosis in subjects with stable and unstable angina pectoris.

PubMed

Tok, Derya; Turak, Osman; Yayla, Çağrı; Ozcan, Fırat; Tok, Duran; Çağlı, Kumral

2016-08-01

This study aims to assess the predictive role of the preprocedural circulating monocyte to high-density lipoprotein (HDL) cholesterol ratio (MHR) on the occurrence of stent restenosis (SR) in patients with stable and unstable angina pectoris undergoing successful bare-metal stenting (BMS). Between February 2008 and June 2014, a total of 831 patients with stable and unstable angina pectoris who underwent successful BMS were retrospectively analyzed. Demographic and clinical characteristics of the patients were recorded. Left ventricular ejection fraction and laboratory data were also noted. In the receiver operating characteristics curve analysis, MHR >14 had 71% sensitivity and 69% specificity in predicting SR. Our study results show that preprocedural MHR is an independent predictor of SR in this patient population.

Moral Attitudes Predict Cheating and Gamesmanship Behaviors Among Competitive Tennis Players

PubMed Central

Lucidi, Fabio; Zelli, Arnaldo; Mallia, Luca; Nicolais, Giampaolo; Lazuras, Lambros; Hagger, Martin S.

2017-01-01

Background: The present study tested Lee et al.’s (2008) model of moral attitudes and cheating behavior in sports in an Italian sample of young tennis players and extended it to predict behavior in actual match play. In the first phase of the study we proposed that moral, competence and status values would predict prosocial and antisocial moral attitudes directly, and indirectly through athletes’ goal orientations. In the second phase, we hypothesized that moral attitudes would directly predict actual cheating behavior observed during match play. Method: Adolescent competitive tennis players (N = 314, 76.75% males, M age = 14.36 years, SD = 1.50) completed measures of values, goal orientations, and moral attitudes. A sub-sample (n = 90) was observed in 45 competitive tennis matches by trained observers who recorded their cheating and gamesmanship behaviors on a validated checklist. Results: Consistent with hypotheses, athletes’ values predicted their moral attitudes through the effects of goal orientations. Anti-social attitudes directly predicted cheating behavior in actual match play providing support for a direct link between moral attitude and actual behavior. Conclusion: The present study findings support key propositions of Lee and colleagues’ model, and extended its application to competitive athletes in actual match play. PMID:28446891

Category-Specific Neural Oscillations Predict Recall Organization During Memory Search

PubMed Central

Morton, Neal W.; Kahana, Michael J.; Rosenberg, Emily A.; Baltuch, Gordon H.; Litt, Brian; Sharan, Ashwini D.; Sperling, Michael R.; Polyn, Sean M.

2013-01-01

Retrieved-context models of human memory propose that as material is studied, retrieval cues are constructed that allow one to target particular aspects of past experience. We examined the neural predictions of these models by using electrocorticographic/depth recordings and scalp electroencephalography (EEG) to characterize category-specific oscillatory activity, while participants studied and recalled items from distinct, neurally discriminable categories. During study, these category-specific patterns predict whether a studied item will be recalled. In the scalp EEG experiment, category-specific activity during study also predicts whether a given item will be recalled adjacent to other same-category items, consistent with the proposal that a category-specific retrieval cue is used to guide memory search. Retrieved-context models suggest that integrative neural circuitry is involved in the construction and maintenance of the retrieval cue. Consistent with this hypothesis, we observe category-specific patterns that rise in strength as multiple same-category items are studied sequentially, and find that individual differences in this category-specific neural integration during study predict the degree to which a participant will use category information to organize memory search. Finally, we track the deployment of this retrieval cue during memory search: Category-specific patterns are stronger when participants organize their responses according to the category of the studied material. PMID:22875859

Predictions of Experimentally Observed Stochastic Ground Vibrations Induced by Blasting

PubMed Central

Kostić, Srđan; Perc, Matjaž; Vasović, Nebojša; Trajković, Slobodan

2013-01-01

In the present paper, we investigate the blast induced ground motion recorded at the limestone quarry “Suva Vrela” near Kosjerić, which is located in the western part of Serbia. We examine the recorded signals by means of surrogate data methods and a determinism test, in order to determine whether the recorded ground velocity is stochastic or deterministic in nature. Longitudinal, transversal and the vertical ground motion component are analyzed at three monitoring points that are located at different distances from the blasting source. The analysis reveals that the recordings belong to a class of stationary linear stochastic processes with Gaussian inputs, which could be distorted by a monotonic, instantaneous, time-independent nonlinear function. Low determinism factors obtained with the determinism test further confirm the stochastic nature of the recordings. Guided by the outcome of time series analysis, we propose an improved prediction model for the peak particle velocity based on a neural network. We show that, while conventional predictors fail to provide acceptable prediction accuracy, the neural network model with four main blast parameters as input, namely total charge, maximum charge per delay, distance from the blasting source to the measuring point, and hole depth, delivers significantly more accurate predictions that may be applicable on site. We also perform a sensitivity analysis, which reveals that the distance from the blasting source has the strongest influence on the final value of the peak particle velocity. This is in full agreement with previous observations and theory, thus additionally validating our methodology and main conclusions. PMID:24358140

Magnitude Estimation for Large Earthquakes from Borehole Recordings

NASA Astrophysics Data System (ADS)

Eshaghi, A.; Tiampo, K. F.; Ghofrani, H.; Atkinson, G.

2012-12-01

We present a simple and fast method for magnitude determination technique for earthquake and tsunami early warning systems based on strong ground motion prediction equations (GMPEs) in Japan. This method incorporates borehole strong motion records provided by the Kiban Kyoshin network (KiK-net) stations. We analyzed strong ground motion data from large magnitude earthquakes (5.0 ≤ M ≤ 8.1) with focal depths < 50 km and epicentral distances of up to 400 km from 1996 to 2010. Using both peak ground acceleration (PGA) and peak ground velocity (PGV) we derived GMPEs in Japan. These GMPEs are used as the basis for regional magnitude determination. Predicted magnitudes from PGA values (Mpga) and predicted magnitudes from PGV values (Mpgv) were defined. Mpga and Mpgv strongly correlate with the moment magnitude of the event, provided sufficient records for each event are available. The results show that Mpgv has a smaller standard deviation in comparison to Mpga when compared with the estimated magnitudes and provides a more accurate early assessment of earthquake magnitude. We test this new method to estimate the magnitude of the 2011 Tohoku earthquake and we present the results of this estimation. PGA and PGV from borehole recordings allow us to estimate the magnitude of this event 156 s and 105 s after the earthquake onset, respectively. We demonstrate that the incorporation of borehole strong ground-motion records immediately available after the occurrence of large earthquakes significantly increases the accuracy of earthquake magnitude estimation and the associated improvement in earthquake and tsunami early warning systems performance. Moment magnitude versus predicted magnitude (Mpga and Mpgv).

A novel method for prediction of dynamic smiling expressions after orthodontic treatment: a case report.

PubMed

Dai, Fanfan; Li, Yangjing; Chen, Gui; Chen, Si; Xu, Tianmin

2016-02-01

Smile esthetics has become increasingly important for orthodontic patients, thus prediction of post-treatment smile is necessary for a perfect treatment plan. In this study, with a combination of three-dimensional craniofacial data from the cone beam computed tomography and color-encoded structured light system, a novel method for smile prediction was proposed based on facial expression transfer, in which dynamic facial expression was interpreted as a matrix of facial depth changes. Data extracted from the pre-treatment smile expression record were applied to the post-treatment static model to realize expression transfer. Therefore smile esthetics of the patient after treatment could be evaluated in pre-treatment planning procedure. The positive and negative mean values of error for prediction accuracy were 0.9 and - 1.1 mm respectively, with the standard deviation of ± 1.5 mm, which is clinically acceptable. Further studies would be conducted to reduce the prediction error from both the static and dynamic sides as well as to explore automatically combined prediction from the two sides.

Can Rheumatologists Predict Eventual Need for Orthopaedic Intervention in Patients with Rheumatoid Arthritis? Results of a Systematic Review and Analysis of Two UK Inception Cohorts.

PubMed

Nikiphorou, Elena; Carpenter, Lewis; Norton, Sam; Morris, Stephen; MacGregor, Alex; Dixey, Josh; Williams, Peter; Kiely, Patrick; Walsh, David Andrew; Young, Adam

2017-03-01

The structural damage caused by rheumatoid arthritis (RA) can often be mitigated by orthopaedic surgery in late disease. This study evaluates the value of predictive factors for orthopaedic intervention. A systematic review of literature was undertaken to identify papers describing predictive factors for orthopaedic surgery in RA. Manuscripts were selected if they met inclusion criteria of cohort study design, diagnosis of RA, follow-up duration/disease duration ≥3 years, any orthopaedic surgical interventions recorded, and then summarised for predictive factors. A separate predictive analysis was performed on two consecutive UK Early RA cohorts, linked to national datasets. The literature search identified 15 reports examining predictive factors for orthopaedic intervention, 4 inception, 5 prospective and 6 retrospective. Despite considerable variation, acute phase, x-ray scores, women and genotyping were the most commonly reported prognostic markers. The current predictive analysis included 1602 procedures performed in 711 patients (25-year cumulative incidence 26%). Earlier recruitment year, erosions and lower haemoglobin predicted both intermediate and major surgery (P<0.05). Studies report variations in type of and predictive power of clinical and laboratory parameters for different surgical interventions suggesting specific contributions from different pathological and/or patient-level factors. Our current analysis suggests that attention to non-inflammatory factors in addition to suppression of inflammation is needed to minimise the burden of orthopaedic surgery.

Study of the Factors Leading to Fetal and Neonatal Dysthyroidism in Children of Patients With Graves Disease.

PubMed

Banigé, Maïa; Estellat, Candice; Biran, Valerie; Desfrere, Luc; Champion, Valerie; Benachi, Alexandra; Ville, Yves; Dommergues, Marc; Jarreau, Pierre-Henri; Mokhtari, Mostafa; Boithias, Claire; Brioude, Frederic; Mandelbrot, Laurent; Ceccaldi, Pierre-François; Mitanchez, Delphine; Polak, Michel; Luton, Dominique

2017-06-01

Neonatal hyperthyroidism was first described in 1912 and in 1964 was shown to be linked to transplacental passage of maternal antibodies. Few multicenter studies have described the perinatal factors leading to fetal and neonatal dysthyroidism. To show how fetal dysthyroidism (FD) and neonatal dysthyroidism (ND) can be predicted from perinatal variables, in particular, the levels of anti-thyrotropin receptor antibodies (TRAbs) circulating in the mother and child. This was a retrospective multicenter study of data from the medical records of all patients monitored for pregnancy from 2007 to 2014. Among 280,000 births, the medical records of 2288 women with thyroid dysfunction were selected and screened, and 417 women with Graves disease and positive for TRAbs during pregnancy were included. Using the maternal TRAb levels, the cutoff value of 2.5 IU/L best predicted for FD, with a sensitivity of 100% and specificity of 64%. Using the newborn TRAb levels, the cutoff value of 6.8 IU/L best predicted for ND, with a sensitivity of 100% and a specificity of 94%. In our study, 65% of women with a history of Graves disease did not receive antithyroid drugs during pregnancy but still had infants at risk of ND. In pregnant women with TRAb levels ≥2.5 IU/L, fetal ultrasound monitoring is essential until delivery. All newborns with TRAb levels ≥6.8 IU/L should be examined by a pediatrician with special attention for thyroid dysfunction and treated, if necessary.

Predicting the magnetospheric plasma of weather

NASA Technical Reports Server (NTRS)

Dawson, John M.

1986-01-01

The prediction of the plasma environment in time, the plasma weather, is discussed. It is important to be able to predict when large magnetic storms will produce auroras, which will affect the space station operating in low orbit, and what precautions to take both for personnel and sensitive control (computer) equipment onboard. It is also important to start to establish a set of plasma weather records and a record of the ability to predict this weather. A successful forecasting system requires a set of satellite weather stations to provide data from which predictions can be made and a set of plasma weather codes capable of accurately forecasting the status of the Earth's magnetosphere. A numerical magnetohydrodynamic fluid model which is used to model the flow in the magnetosphere, the currents flowing into and out of the auroral regions, the magnetopause, the bow shock location and the magnetotail of the Earth is discussed.

Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference.

PubMed

Sutherland, Scott M; Chawla, Lakhmir S; Kane-Gill, Sandra L; Hsu, Raymond K; Kramer, Andrew A; Goldstein, Stuart L; Kellum, John A; Ronco, Claudio; Bagshaw, Sean M

2016-01-01

The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.

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

Gilchrist, Kristin H., E-mail: kgilchrist@rti.org; Lewis, Gregory F.; Gay, Elaine A.

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak formore » field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5 minute recordings at multiple time points (0.5, 1, 2 and 4 h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. - Highlights: • Six parameters describing arrhythmia were defined and measured for known compounds. • Software for efficient parameter extraction from large MEA data sets was developed. • The proposed cellular parameter set is predictive of clinical drug proarrhythmia.« less

A Novel Model for Predicting Rehospitalization Risk Incorporating Physical Function, Cognitive Status, and Psychosocial Support Using Natural Language Processing.

PubMed

Greenwald, Jeffrey L; Cronin, Patrick R; Carballo, Victoria; Danaei, Goodarz; Choy, Garry

2017-03-01

With the increasing focus on reducing hospital readmissions in the United States, numerous readmissions risk prediction models have been proposed, mostly developed through analyses of structured data fields in electronic medical records and administrative databases. Three areas that may have an impact on readmission but are poorly captured using structured data sources are patients' physical function, cognitive status, and psychosocial environment and support. The objective of the study was to build a discriminative model using information germane to these 3 areas to identify hospitalized patients' risk for 30-day all cause readmissions. We conducted clinician focus groups to identify language used in the clinical record regarding these 3 areas. We then created a dataset including 30,000 inpatients, 10,000 from each of 3 hospitals, and searched those records for the focus group-derived language using natural language processing. A 30-day readmission prediction model was developed on 75% of the dataset and validated on the other 25% and also on hospital specific subsets. Focus group language was aggregated into 35 variables. The final model had 16 variables, a validated C-statistic of 0.74, and was well calibrated. Subset validation of the model by hospital yielded C-statistics of 0.70-0.75. Deriving a 30-day readmission risk prediction model through identification of physical, cognitive, and psychosocial issues using natural language processing yielded a model that performs similarly to the better performing models previously published with the added advantage of being based on clinically relevant factors and also automated and scalable. Because of the clinical relevance of the variables in the model, future research may be able to test if targeting interventions to identified risks results in reductions in readmissions.

Study on Rainfall Forecasting by Using Weather Satellite Imagery in a Small Watershed Located at Mountainous Area of Central Taiwan

NASA Astrophysics Data System (ADS)

Wei, C.; Cheng, K. S.

Using meteorological radar and satellite imagery had become an efficient tool for rainfall forecasting However few studies were aimed to predict quantitative rainfall in small watersheds for flood forecasting by using remote sensing data Due to the terrain shelter and ground clutter effect of Central Mountain Ridges the application of meteorological radar data was limited in mountainous areas of central Taiwan This study devises a new scheme to predict rainfall of a small upstream watershed by combing GOES-9 geostationary weather satellite imagery and ground rainfall records which can be applied for local quantitative rainfall forecasting during periods of typhoon and heavy rainfall Imagery of two typhoon events in 2004 and five correspondent ground raingauges records of Chitou Forest Recreational Area which is located in upstream region of Bei-Shi river were analyzed in this study The watershed accounts for 12 7 square kilometers and altitudes ranging from 1000 m to 1800 m Basin-wide Average Rainfall BAR in study area were estimated by block kriging Cloud Top Temperature CTT from satellite imagery and ground hourly rainfall records were medium correlated The regression coefficient ranges from 0 5 to 0 7 and the value decreases as the altitude of the gauge site increases The regression coefficient of CCT and next 2 to 6 hour accumulated BAR decrease as the time scale increases The rainfall forecasting for BAR were analyzed by Kalman Filtering Technique The correlation coefficient and average hourly deviates between estimated and observed value of BAR for

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age.

PubMed

Wang, Zichen; Li, Li; Glicksberg, Benjamin S; Israel, Ariel; Dudley, Joel T; Ma'ayan, Avi

2017-12-01

Determining the discrepancy between chronological and physiological age of patients is central to preventative and personalized care. Electronic medical records (EMR) provide rich information about the patient physiological state, but it is unclear whether such information can be predictive of chronological age. Here we present a deep learning model that uses vital signs and lab tests contained within the EMR of Mount Sinai Health System (MSHS) to predict chronological age. The model is trained on 377,686 EMR from patients of ages 18-85 years old. The discrepancy between the predicted and real chronological age is then used as a proxy to estimate physiological age. Overall, the model can predict the chronological age of patients with a standard deviation error of ∼7 years. The ages of the youngest and oldest patients were more accurately predicted, while patients of ages ranging between 40 and 60 years were the least accurately predicted. Patients with the largest discrepancy between their physiological and chronological age were further inspected. The patients predicted to be significantly older than their chronological age have higher systolic blood pressure, higher cholesterol, damaged liver, and anemia. In contrast, patients predicted to be younger than their chronological age have lower blood pressure and shorter stature among other indicators; both groups display lower weight than the population average. Using information from ∼10,000 patients from the entire cohort who have been also profiled with SNP arrays, genome-wide association study (GWAS) uncovers several novel genetic variants associated with aging. In particular, significant variants were mapped to genes known to be associated with inflammation, hypertension, lipid metabolism, height, and increased lifespan in mice. Several genes with missense mutations were identified as novel candidate aging genes. In conclusion, we demonstrate how EMR data can be used to assess overall health via a scale that is based on deviation from the patient's predicted chronological age. Copyright © 2017 Elsevier Inc. All rights reserved.

Calibration of diatom-pH-alkalinity methodology for the interpretation of the sedimentary record in Emerald Lake Integrated watershed study. Final report, 6 May 1985-10 October 1986

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

Holmes, R.W.

1986-10-10

The present study was designed to establish quantitative relationships between lake air-equilibrated pH, alkalinity, and diatoms occurring in the surface sediments in high-elevation Sierra Nevada Lakes. These relationships provided the necessary information to develop predictive equations relating lake pH to the composition of surface-sediment diatom assemblages in 27 study lakes. Using the Hustedt diatom pH classification system, Index B of Renberg and Hellberg, and multiple linear regression analysis, two equations were developed which predict lake pH from the relative abundance of sediment diatoms occurring in each of four diatom pH groupings.

Can Global Weed Assemblages Be Used to Predict Future Weeds?

PubMed Central

Morin, Louise; Paini, Dean R.; Randall, Roderick P.

2013-01-01

Predicting which plant taxa are more likely to become weeds in a region presents significant challenges to both researchers and government agencies. Often it is done in a qualitative or semi-quantitative way. In this study, we explored the potential of using the quantitative self-organising map (SOM) approach to analyse global weed assemblages and estimate likelihoods of plant taxa becoming weeds before and after they have been moved to a new region. The SOM approach examines plant taxa associations by analysing where a taxon is recorded as a weed and what other taxa are recorded as weeds in those regions. The dataset analysed was extracted from a pre-existing, extensive worldwide database of plant taxa recorded as weeds or other related status and, following reformatting, included 187 regions and 6690 plant taxa. To assess the value of the SOM approach we selected Australia as a case study. We found that the key and most important limitation in using such analytical approach lies with the dataset used. The classification of a taxon as a weed in the literature is not often based on actual data that document the economic, environmental and/or social impact of the taxon, but mostly based on human perceptions that the taxon is troublesome or simply not wanted in a particular situation. The adoption of consistent and objective criteria that incorporate a standardized approach for impact assessment of plant taxa will be necessary to develop a new global database suitable to make predictions regarding weediness using methods like SOM. It may however, be more realistic to opt for a classification system that focuses on the invasive characteristics of plant taxa without any inference to impacts, which to be defined would require some level of research to avoid bias from human perceptions and value systems. PMID:23393591

Elevated left mid-frontal cortical activity prospectively predicts conversion to bipolar I disorder

PubMed Central

Nusslock, Robin; Harmon-Jones, Eddie; Alloy, Lauren B.; Urosevic, Snezana; Goldstein, Kim; Abramson, Lyn Y.

2013-01-01

Bipolar disorder is characterized by a hypersensitivity to reward-relevant cues and a propensity to experience an excessive increase in approach-related affect, which may be reflected in hypo/manic symptoms. The present study examined the relationship between relative left-frontal electroencephalographic (EEG) activity, a proposed neurophysiological index of approach-system sensitivity and approach/reward-related affect, and bipolar course and state-related variables. Fifty-eight individuals with cyclothymia or bipolar II disorder and 59 healthy control participants with no affective psychopathology completed resting EEG recordings. Alpha power was obtained and asymmetry indices computed for homologous electrodes. Bipolar spectrum participants were classified as being in a major/minor depressive episode, a hypomanic episode, or a euthymic/remitted state at EEG recording. Participants were then followed prospectively for an average 4.7 year follow-up period with diagnostic interview assessments every four-months. Sixteen bipolar spectrum participants converted to bipolar I disorder during follow-up. Consistent with hypotheses, elevated relative left-frontal EEG activity at baseline 1) prospectively predicted a greater likelihood of converting from cyclothymia or bipolar II disorder to bipolar I disorder over the 4.7 year follow-up period, 2) was associated with an earlier age-of-onset of first bipolar spectrum episode, and 3) was significantly elevated in bipolar spectrum individuals in a hypomanic episode at EEG recording. This is the first study to identify a neurophysiological marker that prospectively predicts conversion to bipolar I disorder. The fact that unipolar depression is characterized by decreased relative left-frontal EEG activity suggests that unipolar depression and vulnerability to hypo/mania may be characterized by different profiles of frontal EEG asymmetry. PMID:22775582

Connectivity in MEG resting-state networks increases after resective surgery for low-grade glioma and correlates with improved cognitive performance☆

PubMed Central

van Dellen, E.; de Witt Hamer, P.C.; Douw, L.; Klein, M.; Heimans, J.J.; Stam, C.J.; Reijneveld, J.C.; Hillebrand, A.

2012-01-01

Purpose Low-grade glioma (LGG) patients often have cognitive deficits. Several disease- and treatment related factors affect cognitive processing. Cognitive outcome of resective surgery is unpredictable, both for improvement and deterioration, especially for complex domains such as attention and executive functioning. MEG analysis of resting-state networks (RSNs) is a good candidate for presurgical prediction of cognitive outcome. In this study, we explore the relation between alterations in connectivity of RSNs and changes in cognitive processing after resective surgery, as a stepping stone to ultimately predict postsurgical cognitive outcome. Methods Ten patients with LGG were included, who had no adjuvant therapy. MEG recording and neuropsychological assessment were obtained before and after resective surgery. MEG data were recorded during a no-task eyes-closed condition, and projected to the anatomical space of the AAL atlas. Alterations in functional connectivity, as characterized by the phase lag index (PLI), within the default mode network (DMN), executive control network (ECN), and left- and right-sided frontoparietal networks (FPN) were compared to cognitive changes. Results Lower alpha band DMN connectivity was increased after surgery, and this increase was related to improved verbal memory functioning. Similarly, right FPN connectivity was increased after resection in the upper alpha band, which correlated with improved attention, working memory and executive functioning. Discussion Increased alpha band RSN functional connectivity in MEG recordings correlates with improved cognitive outcome after resective surgery. The mechanisms resulting in functional connectivity alterations after resection remain to be elucidated. Importantly, our findings indicate that connectivity of MEG RSNs may be used for presurgical prediction of cognitive outcome in future studies. PMID:24179752

Accuracy of diagnosis codes to identify febrile young infants using administrative data.

PubMed

Aronson, Paul L; Williams, Derek J; Thurm, Cary; Tieder, Joel S; Alpern, Elizabeth R; Nigrovic, Lise E; Schondelmeyer, Amanda C; Balamuth, Fran; Myers, Angela L; McCulloh, Russell J; Alessandrini, Evaline A; Shah, Samir S; Browning, Whitney L; Hayes, Katie L; Feldman, Elana A; Neuman, Mark I

2015-12-01

Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development. Determine the most accurate International Classification of Diseases, Ninth Revision (ICD-9) diagnosis coding strategies for identification of febrile infants. Retrospective cross-sectional study. Eight emergency departments in the Pediatric Health Information System. Infants aged <90 days evaluated between July 1, 2012 and June 30, 2013 were randomly selected for medical record review from 1 of 4 ICD-9 diagnosis code groups: (1) discharge diagnosis of fever, (2) admission diagnosis of fever without discharge diagnosis of fever, (3) discharge diagnosis of serious infection without diagnosis of fever, and (4) no diagnosis of fever or serious infection. The ICD-9 diagnosis code groups were compared in 4 case-identification algorithms to a reference standard of fever ≥100.4°F documented in the medical record. Algorithm predictive accuracy was measured using sensitivity, specificity, and negative and positive predictive values. Among 1790 medical records reviewed, 766 (42.8%) infants had fever. Discharge diagnosis of fever demonstrated high specificity (98.2%, 95% confidence interval [CI]: 97.8-98.6) but low sensitivity (53.2%, 95% CI: 50.0-56.4). A case-identification algorithm of admission or discharge diagnosis of fever exhibited higher sensitivity (71.1%, 95% CI: 68.2-74.0), similar specificity (97.7%, 95% CI: 97.3-98.1), and the highest positive predictive value (86.9%, 95% CI: 84.5-89.3). A case-identification strategy that includes admission or discharge diagnosis of fever should be considered for febrile infant studies using administrative data, though underclassification of patients is a potential limitation. © 2015 Society of Hospital Medicine.

Accuracy of Diagnosis Codes to Identify Febrile Young Infants Using Administrative Data

PubMed Central

Aronson, Paul L.; Williams, Derek J.; Thurm, Cary; Tieder, Joel S.; Alpern, Elizabeth R.; Nigrovic, Lise E.; Schondelmeyer, Amanda C.; Balamuth, Fran; Myers, Angela L.; McCulloh, Russell J.; Alessandrini, Evaline A.; Shah, Samir S.; Browning, Whitney L.; Hayes, Katie L.; Feldman, Elana A.; Neuman, Mark I.

2015-01-01

Background Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development. Objective Determine the most accurate International Classification of Diseases, 9th revision (ICD-9) diagnosis coding strategies for identification of febrile infants. Design Retrospective cross-sectional study. Setting Eight emergency departments in the Pediatric Health Information System. Patients Infants age < 90 days evaluated between July 1, 2012 and June 30, 2013 were randomly selected for medical record review from one of four ICD-9 diagnosis code groups: 1) discharge diagnosis of fever, 2) admission diagnosis of fever without discharge diagnosis of fever, 3) discharge diagnosis of serious infection without diagnosis of fever, and 4) no diagnosis of fever or serious infection. Exposure The ICD-9 diagnosis code groups were compared in four case-identification algorithms to a reference standard of fever ≥ 100.4°F documented in the medical record. Measurements Algorithm predictive accuracy was measured using sensitivity, specificity, negative and positive predictive values. Results Among 1790 medical records reviewed, 766 (42.8%) infants had fever. Discharge diagnosis of fever demonstrated high specificity (98.2%, 95% confidence interval [CI]: 97.8-98.6) but low sensitivity (53.2%, 95% CI: 50.0-56.4). A case-identification algorithm of admission or discharge diagnosis of fever exhibited higher sensitivity (71.1%, 95% CI: 68.2-74.0), similar specificity (97.7%, 95% CI: 97.3-98.1), and the highest positive predictive value (86.9%, 95% CI: 84.5-89.3). Conclusions A case-identification strategy that includes admission or discharge diagnosis of fever should be considered for febrile infant studies using administrative data, though under-classification of patients is a potential limitation. PMID:26248691

Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients

NASA Astrophysics Data System (ADS)

Mormann, Florian; Andrzejak, Ralph G.; Kreuz, Thomas; Rieke, Christoph; David, Peter; Elger, Christian E.; Lehnertz, Klaus

2003-02-01

The question whether information extracted from the electroencephalogram (EEG) of epilepsy patients can be used for the prediction of seizures has recently attracted much attention. Several studies have reported evidence for the existence of a preseizure state that can be detected using different measures derived from the theory of dynamical systems. Most of these studies, however, have neglected to sufficiently investigate the specificity of the observed effects or suffer from other methodological shortcomings. In this paper we present an automated technique for the detection of a preseizure state from EEG recordings using two different measures for synchronization between recording sites, namely, the mean phase coherence as a measure for phase synchronization and the maximum linear cross correlation as a measure for lag synchronization. Based on the observation of characteristic drops in synchronization prior to seizure onset, we used this phenomenon for the characterization of a preseizure state and its distinction from the remaining seizure-free interval. After optimizing our technique on a group of 10 patients with temporal lobe epilepsy we obtained a successful detection of a preseizure state prior to 12 out of 14 analyzed seizures for both measures at a very high specificity as tested on recordings from the seizure-free interval. After checking for in-sample overtraining via cross validation, we applied a surrogate test to validate the observed predictability. Based on our results, we discuss the differences of the two synchronization measures in terms of the dynamics underlying seizure generation in focal epilepsies.

Endoscopic assessment of airway function as a predictor of racing performance in Thoroughbred yearlings: 427 cases (1997-2000).

PubMed

Stick, J A; Peloso, J G; Morehead, J P; Lloyd, J; Eberhart, S; Padungtod, P; Derksen, F J

2001-10-01

To compare endoscopic findings of the upper portion of the respiratory tract in Thoroughbred yearlings with their subsequent race records to determine whether subjective assessment of airway function may be used as a predictor of future racing performance. Retrospective study. 427 Thoroughbred yearlings. Endoscopic examination findings were obtained from the medical records and the videoendoscopic repository of the Keeneland 1996 September yearling sales. Racing records were requested for the yearlings through the end of their 4-year-old racing season (1997-2000). Twenty-nine measures of racing performance were correlated with endoscopic findings. Subjective arytenoid cartilage movement grades were determined, using a 4-point grading scale (grade 1 = symmetrical synchronous abduction of the arytenoid cartilages; grade 4 = no substantial movement of the left arytenoid cartilage). Of the 427 Thoroughbred yearlings included in this study, 364 established race records, and 63 did not. Opinions regarding suitability for purchase, meeting conditions of the sale, and the presence of epiglottic abnormalities had no significant association with racing performance. Arytenoid cartilage movement grades were significantly associated with many of the dependent variables. However, palatine abnormalities were not predictive of inferior racing performance. Thoroughbred yearlings with grade-1 and -2 arytenoid cartilage movements had significantly better racing performance as adults, compared with yearlings with grade-3 arytenoid cartilage movements. In contrast, epiglottic and palatine abnormalities were not predictive of inferior racing performance. Therefore, evaluation of laryngeal function, but not epiglottic or palatine abnormalities, using the 4-point grading system, should be the major factor in developing recommendations for prospective buyers.

Prediction of adult height in girls: the Beunen-Malina-Freitas method.

PubMed

Beunen, Gaston P; Malina, Robert M; Freitas, Duarte L; Thomis, Martine A; Maia, José A; Claessens, Albrecht L; Gouveia, Elvio R; Maes, Hermine H; Lefevre, Johan

2011-12-01

The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10-15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R (2) regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R (2)) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12-15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12-15 years. It is applicable to European populations or populations of European ancestry.

Coarse-resolution Ecology of Etiological Agent, Vector, and Reservoirs of Zoonotic Cutaneous Leishmaniasis in Libya.

PubMed

Samy, Abdallah M; Annajar, Badereddin B; Dokhan, Mostafa Ramadhan; Boussaa, Samia; Peterson, A Townsend

2016-02-01

Cutaneous leishmaniasis ranks among the tropical diseases least known and most neglected in Libya. World Health Organization reports recognized associations of Phlebotomus papatasi, Psammomys obesus, and Meriones spp., with transmission of zoonotic cutaneous leishmaniasis (ZCL; caused by Leishmania major) across Libya. Here, we map risk of ZCL infection based on occurrence records of L. major, P. papatasi, and four potential animal reservoirs (Meriones libycus, Meriones shawi, Psammomys obesus, and Gerbillus gerbillus). Ecological niche models identified limited risk areas for ZCL across the northern coast of the country; most species associated with ZCL transmission were confined to this same region, but some had ranges extending to central Libya. All ENM predictions were significant based on partial ROC tests. As a further evaluation of L. major ENM predictions, we compared predictions with 98 additional independent records provided by the Libyan National Centre for Disease Control (NCDC); all of these records fell inside the belt predicted as suitable for ZCL. We tested ecological niche similarity among vector, parasite, and reservoir species and could not reject any null hypotheses of niche similarity. Finally, we tested among possible combinations of vector and reservoir that could predict all recent human ZCL cases reported by NCDC; only three combinations could anticipate the distribution of human cases across the country.

Coarse-resolution Ecology of Etiological Agent, Vector, and Reservoirs of Zoonotic Cutaneous Leishmaniasis in Libya

PubMed Central

Samy, Abdallah M.; Annajar, Badereddin B.; Dokhan, Mostafa Ramadhan; Boussaa, Samia; Peterson, A. Townsend

2016-01-01

Abstract Cutaneous leishmaniasis ranks among the tropical diseases least known and most neglected in Libya. World Health Organization reports recognized associations of Phlebotomus papatasi, Psammomys obesus, and Meriones spp., with transmission of zoonotic cutaneous leishmaniasis (ZCL; caused by Leishmania major) across Libya. Here, we map risk of ZCL infection based on occurrence records of L. major, P. papatasi, and four potential animal reservoirs (Meriones libycus, Meriones shawi, Psammomys obesus, and Gerbillus gerbillus). Ecological niche models identified limited risk areas for ZCL across the northern coast of the country; most species associated with ZCL transmission were confined to this same region, but some had ranges extending to central Libya. All ENM predictions were significant based on partial ROC tests. As a further evaluation of L. major ENM predictions, we compared predictions with 98 additional independent records provided by the Libyan National Centre for Disease Control (NCDC); all of these records fell inside the belt predicted as suitable for ZCL. We tested ecological niche similarity among vector, parasite, and reservoir species and could not reject any null hypotheses of niche similarity. Finally, we tested among possible combinations of vector and reservoir that could predict all recent human ZCL cases reported by NCDC; only three combinations could anticipate the distribution of human cases across the country. PMID:26863317

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

PubMed

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

Determinants and development of a web-based child mortality prediction model in resource-limited settings: A data mining approach.

PubMed

Tesfaye, Brook; Atique, Suleman; Elias, Noah; Dibaba, Legesse; Shabbir, Syed-Abdul; Kebede, Mihiretu

2017-03-01

Improving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. Decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data. Waikato Environment for Knowledge Analysis (WEKA) for windows version 3.6.8 was used to develop optimal models. 8157 (70%) records were randomly allocated to training group for model building while; the remaining 3496 (30%) records were allocated as the test group for model validation. The validation of the model was assessed using accuracy, sensitivity, specificity and area under Receiver Operating Characteristics (ROC) curve. Using Statistical Package for Social Sciences (SPSS) version 20.0; logistic regressions and Odds Ratio (OR) with 95% Confidence Interval (CI) was used to identify determinants of child mortality. The child mortality rate was 72 deaths per 1000 live births. Breast-feeding (AOR= 1.46, (95% CI [1.22. 1.75]), maternal education (AOR= 1.40, 95% CI [1.11, 1.81]), family planning (AOR= 1.21, [1.08, 1.43]), preceding birth interval (AOR= 4.90, [2.94, 8.15]), presence of diarrhea (AOR= 1.54, 95% CI [1.32, 1.66]), father's education (AOR= 1.4, 95% CI [1.04, 1.78]), low birth weight (AOR= 1.2, 95% CI [0.98, 1.51]) and, age of the mother at first birth (AOR= 1.42, [1.01-1.89]) were found to be determinants for child mortality. The J48 model had better performance, accuracy (94.3%), sensitivity (93.8%), specificity (94.3%), Positive Predictive Value (PPV) (92.2%), Negative Predictive Value (NPV) (94.5%) and, the area under ROC (94.8%). Subsequent to developing an optimal prediction model, we relied on this model to develop a web-based application system for child mortality prediction. In this study, nearly accurate results were obtained by employing decision tree and rule induction techniques. Determinants are identified and a web-based child mortality prediction model in Ethiopian local language is developed. Thus, the result obtained could support child health intervention programs in Ethiopia where trained human resource for health is limited. Advanced classification algorithms need to be tested to come up with optimal models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Medial Orbitofrontal Neurons Preferentially Signal Cues Predicting Changes in Reward during Unblocking

PubMed Central

Lopatina, Nina; McDannald, Michael A.; Styer, Clay V.; Peterson, Jacob F.; Sadacca, Brian F.; Cheer, Joseph F.

2016-01-01

The orbitofrontal cortex (OFC) has been broadly implicated in the ability to use the current value of expected outcomes to guide behavior. Although value correlates have been prominently reported in lateral OFC, they are more often associated with more medial areas. Further, recent studies in primates have suggested a dissociation in which the lateral OFC is involved in credit assignment and representation of reward identity and more medial areas are critical to representing value. Previously, we used unblocking to test more specifically what information about outcomes is represented by OFC neurons in rats; consistent with the proposed dichotomy between the lateral and medial OFC, we found relatively little linear value coding in the lateral OFC (Lopatina et al., 2015). Here we have repeated this experiment, recording in the medial OFC, to test whether such value signals might be found there. Neurons were recorded in an unblocking task as rats learned about cues that signaled either more, less, or the same amount of reward. We found that medial OFC neurons acquired responses to these cues; however, these responses did not signal different reward values across cues. Surprisingly, we found that cells developed responses to cues predicting a change, particularly a decrease, in reward value. This is consistent with a special role for medial OFC in representing current value to support devaluation/revaluation sensitive changes in behavior. SIGNIFICANCE STATEMENT This study uniquely examines encoding in rodent mOFC at the single-unit level in response to cues that predict more, less, or no change in reward in rats during training in a Pavlovian unblocking task, finding more cells responding to change-predictive cues and stronger activity in response to cues predictive of less reward. PMID:27511013

In-session caregiver behavior predicts symptom change in youth receiving trauma-focused cognitive behavioral therapy (TF-CBT).

PubMed

Yasinski, Carly; Hayes, Adele M; Ready, C Beth; Cummings, Jorden A; Berman, Ilana S; McCauley, Thomas; Webb, Charles; Deblinger, Esther

2016-12-01

Involving caregivers in trauma-focused treatments for youth has been shown to result in better outcomes, but it is not clear which in-session caregiver behaviors enhance or inhibit this effect. The current study examined the associations between caregiver behaviors during Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) and youth cognitive processes and symptoms. Participants were a racially diverse sample of Medicaid-eligible youth (ages 7-17) and their nonoffending caregivers (N = 71 pairs) who received TF-CBT through an effectiveness study in a community setting. Caregiver and youth processes were coded from audio-recorded sessions, and outcomes were measured using the Child Behavior Checklist (CBCL) and UCLA PTSD Reaction Index for Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition (DSM-IV; UPID) at 3, 6, 9, and 12 months postintake. Piecewise linear growth curve modeling revealed that during the trauma narrative phase of TF-CBT, caregivers' cognitive-emotional processing of their own and their child's trauma-related reactions predicted decreases in youth internalizing and externalizing symptoms over treatment. Caregiver support predicted lower internalizing symptoms over follow-up. In contrast, caregiver avoidance and blame of the child predicted worsening of youth internalizing and externalizing symptoms over follow-up. Caregiver avoidance early in treatment also predicted worsening of externalizing symptoms over follow-up. During the narrative phase, caregiver blame and avoidance were correlated with more child overgeneralization of trauma beliefs, and blame was also associated with less child accommodation of balanced beliefs. The association between in-session caregiver behaviors and youth symptomatology during and after TF-CBT highlights the importance of assessing and targeting these behaviors to improve clinical outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

40 CFR Table 8 to Subpart Zzzz of... - Applicability of General Provisions to Subpart ZZZZ.

Code of Federal Regulations, 2012 CFR

2012-07-01

... predictable SSM Yes. § 63.8(c)(1)(ii) SSM not in Startup Shutdown Malfunction Plan Yes. § 63.8(c)(1)(iii...)-(v) Records related to SSM No. § 63.10(b)(2)(vi)-(xi) Records Yes. § 63.10(b)(2)(xii) Record when...

40 CFR Table 8 to Subpart Zzzz of... - Applicability of General Provisions to Subpart ZZZZ.

Code of Federal Regulations, 2011 CFR

2011-07-01

... predictable SSM Yes. § 63.8(c)(1)(ii) SSM not in Startup Shutdown Malfunction Plan Yes. § 63.8(c)(1)(iii...)-(v) Records related to SSM No. § 63.10(b)(2)(vi)-(xi) Records Yes. § 63.10(b)(2)(xii) Record when...

An empirical approach to predicting long term behavior of metal particle based recording media

NASA Technical Reports Server (NTRS)

Hadad, Allan S.

1991-01-01

Alpha iron particles used for magnetic recording are prepared through a series of dehydration and reduction steps of alpha-Fe2O3-H2O resulting in acicular, polycrystalline, body centered cubic (bcc) alpha-Fe particles that are single magnetic domains. Since fine iron particles are pyrophoric by nature, stabilization processes had to be developed in order for iron particles to be considered as a viable recording medium for long term archival (i.e., 25+ years) information storage. The primary means of establishing stability is through passivation or controlled oxidation of the iron particle's surface. Since iron particles used for magnetic recording are small, additional oxidation has a direct impact on performance especially where archival storage of recorded information for long periods of time is important. Further stabilization chemistry/processes had to be developed to guarantee that iron particles could be considered as a viable long term recording medium. In an effort to retard the diffusion of iron ions through the oxide layer, other elements such as silicon, aluminum, and chromium have been added to the base iron to promote more dense scale formation or to alleviate some of the non-stoichiometric behavior of the oxide or both. The presence of water vapor has been shown to disrupt the passive layer, subsequently increasing the oxidation rate of the iron. A study was undertaken to examine the degradation in magnetic properties as a function of both temperature and humidity on silicon-containing iron particles between 50-120 deg C and 3-89 percent relative humidity. The methodology to which experimental data was collected and analyzed leading to predictive capability is discussed.

Estimating the magnitude and frequency of floods for streams in west-central Florida, 2001

USGS Publications Warehouse

Hammett, Kathleen M.; DelCharco, Michael J.

2005-01-01

Flood discharges were estimated for recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for 94 streamflow stations in west-central Florida. Most of the stations are located within the 10,000 square-mile, 16-county area that forms the Southwest Florida Water Management District. All stations had at least 10 years of homogeneous record, and none have flood discharges that are significantly affected by regulation or urbanization. Guidelines established by the U.S. Water Resources Council in Bulletin 17B were used to estimate flood discharges from gaging station records. Multiple linear regression analysis was then used to mathematically relate estimates of flood discharge for selected recurrence intervals to explanatory basin characteristics. Contributing drainage area, channel slope, and the percent of total drainage area covered by lakes (percent lake area) were the basin characteristics that provided the best regression estimates. The study area was subdivided into four geographic regions to further refine the regression equations. Region 1 at the northern end of the study area includes large rivers that are characteristic of the rolling karst terrain of northern Florida. Only a small part of Region 1 lies within the boundaries of the Southwest Florida Water Management District. Contributing drainage area and percent lake area were the most statistically significant basin characteristics in Region 1; the prediction error of the regression equations varied with the recurrence interval and ranged from 57 to 69 percent. In the three other regions of the study area, contributing drainage area, channel slope, and percent lake area were the most statistically significant basin characteristics, and are the three characteristics that can be used to best estimate the magnitude and frequency of floods on most streams within the Southwest Florida Water Management District. The Withlacoochee River Basin dominates Region 2; the prediction error of the regression models in the region ranged from 65 to 68 percent. The basins that drain into the northern part of Tampa Bay and the upper reaches of the Peace River Basin are in Region 3, which had prediction errors ranging from 54 to 74 percent. Region 4, at the southern end of the study area, had prediction errors that ranged from 40 to 56 percent. Estimates of flood discharge become more accurate as longer periods of record are used for analyses; results of this study should be used in lieu of results from earlier U.S. Geological Survey studies of flood magnitude and frequency in west-central Florida. A comparison of current results with earlier studies indicates that use of a longer period of record with additional high-water events produces substantially higher flood-discharge estimates for many gaging stations. Another comparison indicates that the use of a computed, generalized skew in a previous study in 1979 tended to overestimate flood discharges.

A feasibility study for long-path multiple detection using a neural network

NASA Technical Reports Server (NTRS)

Feuerbacher, G. A.; Moebes, T. A.

1994-01-01

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

[Comparison of predictive models for the selection of high-complexity patients].

PubMed

Estupiñán-Ramírez, Marcos; Tristancho-Ajamil, Rita; Company-Sancho, María Consuelo; Sánchez-Janáriz, Hilda

2017-08-18

To compare the concordance of complexity weights between Clinical Risk Groups (CRG) and Adjusted Morbidity Groups (AMG). To determine which one is the best predictor of patient admission. To optimise the method used to select the 0.5% of patients of higher complexity that will be included in an intervention protocol. Cross-sectional analytical study in 18 Canary Island health areas, 385,049 citizens were enrolled, using sociodemographic variables from health cards; diagnoses and use of healthcare resources obtained from primary health care electronic records (PCHR) and the basic minimum set of hospital data; the functional status recorded in the PCHR, and the drugs prescribed through the electronic prescription system. The correlation between stratifiers was estimated from these data. The ability of each stratifier to predict patient admissions was evaluated and prediction optimisation models were constructed. Concordance between weights complexity stratifiers was strong (rho = 0.735) and the correlation between categories of complexity was moderate (weighted kappa = 0.515). AMG complexity weight predicts better patient admission than CRG (AUC: 0.696 [0.695-0.697] versus 0.692 [0.691-0.693]). Other predictive variables were added to the AMG weight, obtaining the best AUC (0.708 [0.707-0.708]) the model composed by AMG, sex, age, Pfeiffer and Barthel scales, re-admissions and number of prescribed therapeutic groups. strong concordance was found between stratifiers, and higher predictive capacity for admission from AMG, which can be increased by adding other dimensions. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Anger, hostility, and hospitalizations in patients with heart failure.

PubMed

Keith, Felicia; Krantz, David S; Chen, Rusan; Harris, Kristie M; Ware, Catherine M; Lee, Amy K; Bellini, Paula G; Gottlieb, Stephen S

2017-09-01

Heart failure patients have a high hospitalization rate, and anger and hostility are associated with coronary heart disease morbidity and mortality. Using structural equation modeling, this prospective study assessed the predictive validity of anger and hostility traits for cardiovascular and all-cause rehospitalizations in patients with heart failure. 146 heart failure patients were administered the STAXI and Cook-Medley Hostility Inventory to measure anger, hostility, and their component traits. Hospitalizations were recorded for up to 3 years following baseline. Causes of hospitalizations were categorized as heart failure, total cardiac, noncardiac, and all-cause (sum of cardiac and noncardiac). Measurement models were separately fit for Anger and Hostility, followed by a Confirmatory Factor Analysis to estimate the relationship between the Anger and Hostility constructs. An Anger model consisted of State Anger, Trait Anger, Anger Expression Out, and Anger Expression In, and a Hostility model included Cynicism, Hostile Affect, Aggressive Responding, and Hostile Attribution. The latent construct of Anger did not predict any of the hospitalization outcomes, but Hostility significantly predicted all-cause hospitalizations. Analyses of individual trait components of each of the 2 models indicated that Anger Expression Out predicted all-cause and noncardiac hospitalizations, and Trait Anger predicted noncardiac hospitalizations. None of the individual components of Hostility were related to rehospitalizations or death. The construct of Hostility and several components of Anger are predictive of hospitalizations that were not specific to cardiac causes. Mechanisms common to a variety of health problems, such as self-care and risky health behaviors, may be involved in these associations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records.

PubMed

Huang, Zhengxing; Dong, Wei; Duan, Huilong; Liu, Jiquan

2018-05-01

Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention and treatment. Existing ACS risk scoring models are based mainly on a small set of hand-picked risk factors and often dichotomize predictive variables to simplify the score calculation. This study develops a regularized stacked denoising autoencoder (SDAE) model to stratify clinical risks of ACS patients from a large volume of electronic health records (EHR). To capture characteristics of patients at similar risk levels, and preserve the discriminating information across different risk levels, two constraints are added on SDAE to make the reconstructed feature representations contain more risk information of patients, which contribute to a better clinical risk prediction result. We validate our approach on a real clinical dataset consisting of 3464 ACS patient samples. The performance of our approach for predicting ACS risk remains robust and reaches 0.868 and 0.73 in terms of both AUC and accuracy, respectively. The obtained results show that the proposed approach achieves a competitive performance compared to state-of-the-art models in dealing with the clinical risk prediction problem. In addition, our approach can extract informative risk factors of ACS via a reconstructive learning strategy. Some of these extracted risk factors are not only consistent with existing medical domain knowledge, but also contain suggestive hypotheses that could be validated by further investigations in the medical domain.

Use of the International Classification of Diseases, 9th revision, coding in identifying chronic hepatitis B virus infection in health system data: implications for national surveillance.

PubMed

Mahajan, Reena; Moorman, Anne C; Liu, Stephen J; Rupp, Loralee; Klevens, R Monina

2013-05-01

With increasing use electronic health records (EHR) in the USA, we looked at the predictive values of the International Classification of Diseases, 9th revision (ICD-9) coding system for surveillance of chronic hepatitis B virus (HBV) infection. The chronic HBV cohort from the Chronic Hepatitis Cohort Study was created based on electronic health records (EHR) of adult patients who accessed services from 2006 to 2008 from four healthcare systems in the USA. Using the gold standard of abstractor review to confirm HBV cases, we calculated the sensitivity, specificity, positive and negative predictive values using one qualifying ICD-9 code versus using two qualifying ICD-9 codes separated by 6 months or greater. Of 1 652 055 adult patients, 2202 (0.1%) were confirmed as having chronic HBV. Use of one ICD-9 code had a sensitivity of 83.9%, positive predictive value of 61.0%, and specificity and negative predictive values greater than 99%. Use of two hepatitis B-specific ICD-9 codes resulted in a sensitivity of 58.4% and a positive predictive value of 89.9%. Use of one or two hepatitis B ICD-9 codes can identify cases with chronic HBV infection with varying sensitivity and positive predictive values. As the USA increases the use of EHR, surveillance using ICD-9 codes may be reliable to determine the burden of chronic HBV infection and would be useful to improve reporting by state and local health departments.

Investigation of genesis of gallop sounds in dogs by quantitative phonocardiography and digital frequency analysis.

PubMed

Aubert, A E; Denys, B G; Meno, F; Reddy, P S

1985-05-01

Several investigators have noted external gallop sounds to be of higher amplitude than their corresponding internal sounds (S3 and S4). In this study we hoped to determine if S3 and S4 are transmitted in the same manner as S1. In 11 closed-chest dogs, external (apical) and left ventricular pressures and sounds were recorded simultaneously with transducers with identical sensitivity and frequency responses. Volume and pressure overload and positive and negative inotropic drugs were used to generate gallop sounds. Recordings were made in the control state and after the various interventions. S3 and S4 were recorded in 17 experiments each. The amplitude of the external S1 was uniformly higher than that of internal S1 and internal gallop sounds were inconspicuous. With use of Fourier transforms, the gain function was determined by comparing internal to external S1. By inverse transform, the amplitude of the internal gallop sounds was predicted from external sounds. The internal sounds of significant amplitude were predicted in many instances, but the actual recordings showed no conspicuous sounds. The absence of internal gallop sounds of expected amplitude as calculated from the external gallop sounds and the gain function derived from the comparison of internal and external S1 make it very unlikely that external gallop sounds are derived from internal sounds.

Differential misclassification between self-reported status and official HPV vaccination records in Japan: Implications for evaluating vaccine safety and effectiveness.

PubMed

Yamaguchi, Manako; Sekine, Masayuki; Kudo, Risa; Adachi, Sosuke; Ueda, Yutaka; Miyagi, Etsuko; Hara, Megumi; Hanley, Sharon J B; Enomoto, Takayuki

2018-05-25

Japan has no national vaccine registry and approximately 1700 municipalities manage the immunization records independently. In June 2013, proactive recommendations for the human papillomavirus (HPV) vaccine were suspended after unconfirmed reports of adverse events following immunization in the media, despite no vaccine safety signal having been raised. Furthermore, studies assessing HPV vaccine safety and effectiveness published post suspension are predominantly based on self-reported information. Our aim was to examine the accuracy of self-reported vaccination status compared with official municipal records. Participants were women aged 20-22 yrs, who were attending for cervical screening in Niigata city. Among the 1230 eligible registrants, vaccine uptake, defined as any dose, was 75.0% and 77.2% according to a self-reported questionnaire and municipal records, respectively. The accuracy rate of self-reported information was as follows: positive predictive value (PPV) was 87.7%; negative predictive value (NPV) was 54.5%; sensitivity was 85.2%; and specificity was 59.8%. The validity of self-reported information was only moderate (Kappa statistic = 0.44, 95% confidence interval 0.37-0.50). This combined with the low NPV may lead to reduced estimation of effectiveness and safety. A more reliable method, such as a national HPV vaccine registry, needs to be established for assessing HPV immunization status in Japan. Copyright © 2018. Published by Elsevier B.V.

Determination of saleable product in finished cattle and beef carcasses utilizing bioelectrical impedance technology.

PubMed

Marchello, M J; McLennan, J E; Dhuyvetter, D V; Slanger, W D

1999-11-01

Two experiments were performed to develop prediction equations of saleable beef and to validate the prediction equations. In Exp. 1, 50 beef cattle were finished to typical slaughter weights, and multiple linear regression equations were developed to predict kilograms of trimmed boneless, retail product of live cattle, and hot and cold carcasses. A four-terminal bioelectrical impedance analyzer (BIA) was used to measure resistance (Rs) and reactance (Xc) on each animal and processed carcass. The IMPS cuts plus trim were weighed and recorded. Distance between detector terminals (Lg) and carcass temperature (Tp) at time of BIA readings were recorded. Other variables included live weight (BW), hot carcass weight (HCW), cold carcass weight (CCW), and volume (Lg2/Rs). Regression equations for predicting kilograms of saleable product were [11.87 + (.409 x BW) - (.335 x Lg) + (.0518 x volume)] for live (R2 = .80); [-58.83 + (.589 x HCW) - (.846 x Rs) + (1.152 x Xc) + (.142 x Lg) + (2.608 x Tp)] for hot carcass (R2 = .95); and [32.15 + (.633 x CCW) + (.33 x Xc) - (.83 x Lg) + (.677 x volume)] for cold carcass (R2 = .93). In Exp. 2, 27 beef cattle were finished in a manner similar to Exp. 1, and the prediction equations from Exp. 1 were used to predict the saleable product of these animals. The Pearson correlations between actual saleable product and the predictions based on live and cold carcass data were .91 and .95, respectively. The Spearman and Kendall rank correlations were .95 and .83, respectively, for the cold carcass data. These results provide a practical application of bioelectrical impedance for market-based pricing. They complement previous studies that assessed fat-free mass.

Postoperative air leak grading is useful to predict prolonged air leak after pulmonary lobectomy.

PubMed

Oh, Sang Gi; Jung, Yochun; Jheon, Sanghoon; Choi, Yunhee; Yun, Ju Sik; Na, Kook Joo; Ahn, Byoung Hee

2017-01-23

Results of studies to predict prolonged air leak (PAL; air leak longer than 5 days) after pulmonary lobectomy have been inconsistent and are of limited use. We developed a new scale representing the amount of early postoperative air leak and determined its correlation with air leak duration and its potential as a predictor of PAL. We grade postoperative air leak using a 5-grade scale. All 779 lobectomies from January 2005 to December 2009 with available medical records were reviewed retrospectively. We devised six 'SUM' variables using air leak grades in the initial 72 h postoperatively. Excluding unrecorded cases and postoperative broncho-pleural fistulas, there were 720 lobectomies. PAL occurred in 135 cases (18.8%). Correlation analyses showed each SUM variable highly correlated with air leak duration, and the SUM 4to9 , which was the sum of six consecutive values of air leak grades for every 8 h record on postoperative days 2 and 3, was proved to be the most powerful predictor of PAL; PAL could be predicted with 75.7% and 77.7% positive and negative predictive value, respectively, when SUM 4to9  ≥ 16. When 4 predictors derived from multivariable logistic regression of perioperative variables were combined with SUM 4to9 , there was no significant increase in predictability compared with SUM 4to9 alone. This simple new method to predict PAL using SUM 4to9 showed that the amount of early postoperative air leak is the most powerful predictor of PAL, therefore, grading air leak after pulmonary lobectomy is a useful method to predict PAL.

Human Cortical θ during Free Exploration Encodes Space and Predicts Subsequent Memory

PubMed Central

Snider, Joseph; Plank, Markus; Lynch, Gary; Halgren, Eric

2013-01-01

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individual's STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration. PMID:24048836

High-throughput cardiac safety evaluation and multi-parameter arrhythmia profiling of cardiomyocytes using microelectrode arrays.

PubMed

Gilchrist, Kristin H; Lewis, Gregory F; Gay, Elaine A; Sellgren, Katelyn L; Grego, Sonia

2015-10-15

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak for field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5minute recordings at multiple time points (0.5, 1, 2 and 4h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. Copyright © 2015 Elsevier Inc. All rights reserved.

Low verbal ability predicts later violence in adolescent boys with serious conduct problems.

PubMed

Manninen, Marko; Lindgren, Maija; Huttunen, Matti; Ebeling, Hanna; Moilanen, Irma; Kalska, Hely; Suvisaari, Jaana; Therman, Sebastian

2013-10-01

Delinquent adolescents are a known high-risk group for later criminality. Cognitive deficits correlate with adult criminality, and specific cognitive deficits might predict later criminality in the high-risk adolescents. This study aimed to explore the neuropsychological performance and predictors of adult criminal offending in adolescents with severe behavioural problems. Fifty-three adolescents (33 boys and 20 girls), aged 15-18 years, residing in a reform school due to serious conduct problems, were examined for neuropsychological profile and psychiatric symptoms. Results were compared with a same-age general population control sample, and used for predicting criminality 5 years after the baseline testing. The reform school adolescents' neuropsychological performance was weak on many tasks, and especially on the verbal domain. Five years after the baseline testing, half of the reform school adolescents had obtained a criminal record. Males were overrepresented in both any criminality (75% vs. 10%) and in violent crime (50% vs. 5%). When cognitive variables, psychiatric symptoms and background factors were used as predictors for later offending, low verbal intellectual ability turned out to be the most significant predictor of a criminal record and especially a record of violent crime. Neurocognitive deficits, especially in the verbal and attention domains, are common among delinquent adolescents. Among males, verbal deficits are the best predictors for later criminal offending and violence. Assessing verbal abilities among adolescent population with conduct problems might prove useful as a screening method for inclusion in specific therapies for aggression management.

Human cortical θ during free exploration encodes space and predicts subsequent memory.

PubMed

Snider, Joseph; Plank, Markus; Lynch, Gary; Halgren, Eric; Poizner, Howard

2013-09-18

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individual's STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration.

A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

PubMed Central

Maturana, Matias I.; Apollo, Nicholas V.; Hadjinicolaou, Alex E.; Garrett, David J.; Cloherty, Shaun L.; Kameneva, Tatiana; Grayden, David B.; Ibbotson, Michael R.; Meffin, Hamish

2016-01-01

Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

A statistical approach based on accumulated degree-days to predict decomposition-related processes in forensic studies.

PubMed

Michaud, Jean-Philippe; Moreau, Gaétan

2011-01-01

Using pig carcasses exposed over 3 years in rural fields during spring, summer, and fall, we studied the relationship between decomposition stages and degree-day accumulation (i) to verify the predictability of the decomposition stages used in forensic entomology to document carcass decomposition and (ii) to build a degree-day accumulation model applicable to various decomposition-related processes. Results indicate that the decomposition stages can be predicted with accuracy from temperature records and that a reliable degree-day index can be developed to study decomposition-related processes. The development of degree-day indices opens new doors for researchers and allows for the application of inferential tools unaffected by climatic variability, as well as for the inclusion of statistics in a science that is primarily descriptive and in need of validation methods in courtroom proceedings. © 2010 American Academy of Forensic Sciences.

Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

NASA Astrophysics Data System (ADS)

Barré, Anthony; Suard, Frédéric; Gérard, Mathias; Montaru, Maxime; Riu, Delphine

2014-01-01

This paper describes the statistical analysis of recorded data parameters of electrical battery ageing during electric vehicle use. These data permit traditional battery ageing investigation based on the evolution of the capacity fade and resistance raise. The measured variables are examined in order to explain the correlation between battery ageing and operating conditions during experiments. Such study enables us to identify the main ageing factors. Then, detailed statistical dependency explorations present the responsible factors on battery ageing phenomena. Predictive battery ageing models are built from this approach. Thereby results demonstrate and quantify a relationship between variables and battery ageing global observations, and also allow accurate battery ageing diagnosis through predictive models.

Does High School Performance Predict College Math Placement?

ERIC Educational Resources Information Center

Kowski, Lynne E.

2013-01-01

Predicting student success has long been a question of interest for postsecondary admission counselors throughout the United States. Past research has examined the validity of several methods designed for predicting undergraduate success. High school record, standardized test scores, extracurricular activities, and combinations of all three have…

Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records.

PubMed

Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Dai, Hong-Jie; Hsu, Chien-Yeh

2015-01-01

Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.

Using Benford's law to investigate Natural Hazard dataset homogeneity.

PubMed

Joannes-Boyau, Renaud; Bodin, Thomas; Scheffers, Anja; Sambridge, Malcolm; May, Simon Matthias

2015-07-09

Working with a large temporal dataset spanning several decades often represents a challenging task, especially when the record is heterogeneous and incomplete. The use of statistical laws could potentially overcome these problems. Here we apply Benford's Law (also called the "First-Digit Law") to the traveled distances of tropical cyclones since 1842. The record of tropical cyclones has been extensively impacted by improvements in detection capabilities over the past decades. We have found that, while the first-digit distribution for the entire record follows Benford's Law prediction, specific changes such as satellite detection have had serious impacts on the dataset. The least-square misfit measure is used as a proxy to observe temporal variations, allowing us to assess data quality and homogeneity over the entire record, and at the same time over specific periods. Such information is crucial when running climatic models and Benford's Law could potentially be used to overcome and correct for data heterogeneity and/or to select the most appropriate part of the record for detailed studies.

Validation of CRIB II for prediction of mortality in premature babies.

PubMed

Rastogi, Pallav Kumar; Sreenivas, V; Kumar, Nirmal

2010-02-01

Validation of Clinical Risk Index for Babies (CRIB II) score in predicting the neonatal mortality in preterm neonates < or = 32 weeks gestational age. Prospective cohort study. Tertiary care neonatal unit. 86 consecutively born preterm neonates with gestational age < or = 32 weeks. The five variables related to CRIB II were recorded within the first hour of admission for data analysis. The receiver operating characteristics (ROC) curve was used to check the accuracy of the mortality prediction. HL Goodness of fit test was used to see the discrepancy between observed and expected outcomes. A total of 86 neonates (males 59.6% mean birthweight: 1228 +/- 398 grams; mean gestational age: 28.3 +/- 2.4 weeks) were enrolled in the study, of which 17 (19.8%) left hospital against medical advice (LAMA) before reaching the study end point. Among 69 neonates completing the study, 24 (34.8%) had adverse outcome during hospital stay and 45 (65.2%) had favorable outcome. CRIB II correctly predicted adverse outcome in 90.3% (Hosmer Lemeshow goodness of fit test P=0.6). Area under curve (AUC) for CRIB II was 0.9032. In intention to treat analysis with LAMA cases included as survivors, the mortality prediction was 87%. If these were included as having died then mortality prediction was 83.1%. The CRIB II score was found to be a good predictive instrument for mortality in preterm infants < or = 32 weeks gestation.

New predictive equations for Arias intensity from crustal earthquakes in New Zealand

NASA Astrophysics Data System (ADS)

Stafford, Peter J.; Berrill, John B.; Pettinga, Jarg R.

2009-01-01

Arias Intensity (Arias, MIT Press, Cambridge MA, pp 438-483, 1970) is an important measure of the strength of a ground motion, as it is able to simultaneously reflect multiple characteristics of the motion in question. Recently, the effectiveness of Arias Intensity as a predictor of the likelihood of damage to short-period structures has been demonstrated, reinforcing the utility of Arias Intensity for use in both structural and geotechnical applications. In light of this utility, Arias Intensity has begun to be considered as a ground-motion measure suitable for use in probabilistic seismic hazard analysis (PSHA) and earthquake loss estimation. It is therefore timely to develop predictive equations for this ground-motion measure. In this study, a suite of four predictive equations, each using a different functional form, is derived for the prediction of Arias Intensity from crustal earthquakes in New Zealand. The provision of a suite of models is included to allow for epistemic uncertainty to be considered within a PSHA framework. Coefficients are presented for four different horizontal-component definitions for each of the four models. The ground-motion dataset for which the equations are derived include records from New Zealand crustal earthquakes as well as near-field records from worldwide crustal earthquakes. The predictive equations may be used to estimate Arias Intensity for moment magnitudes between 5.1 and 7.5 and for distances (both rjb and rrup) up to 300 km.

Longitudinal predictors of high school completion.

PubMed

Barry, Melissa; Reschly, Amy L

2012-06-01

This longitudinal study examined predictors of dropout assessed in elementary school. Student demographic data, achievement, attendance, and ratings of behavior from the Behavior Assessment System for Children were used to predict dropout and completion. Two models, which varied on student sex and race, predicted dropout at rates ranging from 75% to 88%. Model A, which included the Behavioral Symptoms Index, School Problems composite, Iowa Tests of Basic Skills battery, and teacher ratings of student work habits, best predicted female and African American dropouts. Model B, which comprised the Adaptive Skills composite, the Externalizing composite, the School Problems composite, referral for a student support team meeting, and sex, was more accurate for predicting Caucasian dropouts. Both models demonstrated the same hit rates for predicting male dropouts. Recommendations for early warning indicators and linking predictors with interventions are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Predicting Active Users' Personality Based on Micro-Blogging Behaviors

PubMed Central

Hao, Bibo; Guan, Zengda; Zhu, Tingshao

2014-01-01

Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors. PMID:24465462

Assessing the validity of sales self-efficacy: a cautionary tale.

PubMed

Gupta, Nina; Ganster, Daniel C; Kepes, Sven

2013-07-01

We developed a focused, context-specific measure of sales self-efficacy and assessed its incremental validity against the broad Big 5 personality traits with department store salespersons, using (a) both a concurrent and a predictive design and (b) both objective sales measures and supervisory ratings of performance. We found that in the concurrent study, sales self-efficacy predicted objective and subjective measures of job performance more than did the Big 5 measures. Significant differences between the predictability of subjective and objective measures of performance were not observed. Predictive validity coefficients were generally lower than concurrent validity coefficients. The results suggest that there are different dynamics operating in concurrent and predictive designs and between broad and contextualized measures; they highlight the importance of distinguishing between these designs and measures in meta-analyses. The results also point to the value of focused, context-specific personality predictors in selection research. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Multiple frequency interference in photorefractive media

NASA Technical Reports Server (NTRS)

Cox, David E.; Welch, Sharon S.

1992-01-01

The paper describes the use of a numerical simulation to predict the dynamic behavior of a photorefractive crystal exposed to interfering light waves at two different frequencies. Unlike static recording media, photorefractive materials allow for the simultaneous diffraction from and generation of refractive index gratings. The grating properties are evaluated in terms of their effect on the performance of a dynamic distributed sensor which uses the crystal as a holographic recording medium. Experimental results are presented which support the behavior predicted by simulation.

Predictors of adolescents' consent to use health records for research and results from data collection in a Swedish twin cohort.

PubMed

Ullemar, Vilhelmina; Lundholm, Cecilia; Örtqvist, Anne K; Gumpert, Clara Hellner; Anckarsäter, Henrik; Lundström, Sebastian; Almqvist, Catarina

2015-06-01

Non-random selection into a study population due to differences between consenters and non-consenters may introduce participation bias. Past investigations of factors predicting consent to collection of medical health records for research imply that age, sex, health status, and education are of importance for participation, but disagree on the direction of effects. Very little is known about influences on consent from adolescents. Two cohorts of Swedish 15-year-old twins (total n = 4,611) previously invited to the Child and Adolescent Twin Study in Sweden (CATSS) responded to a questionnaire with information on sex, individual's health, height, weight, and parental factors. The questionnaire included a question for consent to collection of medical health records. Predictors for consent were analyzed using logistic regression. Additionally, regional differences in the collection of health records of consenters were evaluated. Males were significantly less likely to consent compared to females (OR 0.74, 95% CI 0.64-0.85). The twin siblings' decision to consent was strongly associated with consent (OR 10.9, 95% CI 8.76-13.5), and individuals whose parents had responded to the original CATSS study were more likely to consent to record collection at age 15 (OR 2.2, 95% CI 1.81-2.75). Results of the subsequent collection of consenters' medical health records varied between geographical regions of Sweden. We identified several predictors for adolescents' consent to collection of their medical health records. Further selection was introduced through the subsequent record collection. Whether this will induce participation bias in future studies depends on the research questions' relationship to the identified predictors.

The development of an industrial-scale fed-batch fermentation simulation.

PubMed

Goldrick, Stephen; Ştefan, Andrei; Lovett, David; Montague, Gary; Lennox, Barry

2015-01-10

This paper describes a simulation of an industrial-scale fed-batch fermentation that can be used as a benchmark in process systems analysis and control studies. The simulation was developed using a mechanistic model and validated using historical data collected from an industrial-scale penicillin fermentation process. Each batch was carried out in a 100,000 L bioreactor that used an industrial strain of Penicillium chrysogenum. The manipulated variables recorded during each batch were used as inputs to the simulator and the predicted outputs were then compared with the on-line and off-line measurements recorded in the real process. The simulator adapted a previously published structured model to describe the penicillin fermentation and extended it to include the main environmental effects of dissolved oxygen, viscosity, temperature, pH and dissolved carbon dioxide. In addition the effects of nitrogen and phenylacetic acid concentrations on the biomass and penicillin production rates were also included. The simulated model predictions of all the on-line and off-line process measurements, including the off-gas analysis, were in good agreement with the batch records. The simulator and industrial process data are available to download at www.industrialpenicillinsimulation.com and can be used to evaluate, study and improve on the current control strategy implemented on this facility. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Admission Models for At-Risk Graduate Students in Different Academic Disciplines.

ERIC Educational Resources Information Center

Nelson, C. Van; Nelson, Jacquelyn S.; Malone, Bobby G.

In this study, models were constructed for eight academic areas, including applied sciences, communication sciences, education, physical sciences, life sciences, humanities and arts, psychology, and social sciences, to predict whether or not an at-risk graduate student would be successful in obtaining a master's degree. Records were available for…

Pathways to a STEMM Profession

ERIC Educational Resources Information Center

Miller, Jon D.; Kimmel, Linda G.

2012-01-01

The inadequate number of American young adults selecting a scientific or engineering profession continues to be a major national concern. Using data from the 23-year record of the Longitudinal Study of American Youth (LSAY) and working within the social learning paradigm, this analysis uses a set of 21 variables to predict young people's…

The Impact of Children's Social Adjustment on Academic Outcomes

ERIC Educational Resources Information Center

DeRosier, Melissa E.; Lloyd, Stacey W.

2011-01-01

This study tested whether social adjustment added to the prediction of academic outcomes above and beyond prior academic functioning. Researchers collected school records and peer-, teacher-, and self-report measures for 1,255 third-grade children in the fall and spring of the school year. Measures of social adjustment included social acceptance…

Maternal Reminiscing Style during Early Childhood Predicts the Age of Adolescents' Earliest Memories

ERIC Educational Resources Information Center

Jack, Fiona; MacDonald, Shelley; Reese, Elaine; Hayne, Harlene

2009-01-01

Individual differences in parental reminiscing style are hypothesized to have long-lasting effects on children's autobiographical memory development, including the age of their earliest memories. This study represents the first prospective test of this hypothesis. Conversations about past events between 17 mother-child dyads were recorded on…

Early Career Determinants of Research Productivity

ERIC Educational Resources Information Center

Clemente, Frank

1973-01-01

The publication records of 2,205 holders of the Ph.D. in sociology are examined for the period 1940-70. The predictive efficiency of six independent variables is assessed via regression analysis. A seventh variable is used as a control. Results of the study and directions for future research are presented and discussed. (SM)

Using the landmark method for creating prediction models in large datasets derived from electronic health records.

PubMed

Wells, Brian J; Chagin, Kevin M; Li, Liang; Hu, Bo; Yu, Changhong; Kattan, Michael W

2015-03-01

With the integration of electronic health records (EHRs), health data has become easily accessible and abounded. The EHR has the potential to provide important healthcare information to researchers by creating study cohorts. However, accessing this information comes with three major issues: 1) Predictor variables often change over time, 2) Patients have various lengths of follow up within the EHR, and 3) the size of the EHR data can be computationally challenging. Landmark analyses provide a perfect complement to EHR data and help to alleviate these three issues. We present two examples that utilize patient birthdays as landmark times for creating dynamic datasets for predicting clinical outcomes. The use of landmark times help to solve these three issues by incorporating information that changes over time, by creating unbiased reference points that are not related to a patient's exposure within the EHR, and reducing the size of a dataset compared to true time-varying analysis. These techniques are shown using two example cohort studies from the Cleveland Clinic that utilized 4.5 million and 17,787 unique patients, respectively.

Solar activity simulation and forecast with a flux-transport dynamo

NASA Astrophysics Data System (ADS)

Macario-Rojas, Alejandro; Smith, Katharine L.; Roberts, Peter C. E.

2018-06-01

We present the assessment of a diffusion-dominated mean field axisymmetric dynamo model in reproducing historical solar activity and forecast for solar cycle 25. Previous studies point to the Sun's polar magnetic field as an important proxy for solar activity prediction. Extended research using this proxy has been impeded by reduced observational data record only available from 1976. However, there is a recognised need for a solar dynamo model with ample verification over various activity scenarios to improve theoretical standards. The present study aims to explore the use of helioseismology data and reconstructed solar polar magnetic field, to foster the development of robust solar activity forecasts. The research is based on observationally inferred differential rotation morphology, as well as observed and reconstructed polar field using artificial neural network methods via the hemispheric sunspot areas record. Results show consistent reproduction of historical solar activity trends with enhanced results by introducing a precursor rise time coefficient. A weak solar cycle 25, with slow rise time and maximum activity -14.4% (±19.5%) with respect to the current cycle 24 is predicted.

Exploring the relationship between stride, stature and hand size for forensic assessment.

PubMed

Guest, Richard; Miguel-Hurtado, Oscar; Stevenage, Sarah; Black, Sue

2017-11-01

Forensic evidence often relies on a combination of accurately recorded measurements, estimated measurements from landmark data such as a subject's stature given a known measurement within an image, and inferred data. In this study a novel dataset is used to explore linkages between hand measurements, stature, leg length and stride. These three measurements replicate the type of evidence found in surveillance videos with stride being extracted from an automated gait analysis system. Through correlations and regression modelling, it is possible to generate accurate predictions of stature from hand size, leg length and stride length (and vice versa), and to predict leg and stride length from hand size with, or without, stature as an intermediary variable. The study also shows improved accuracy when a subject's sex is known a-priori. Our method and models indicate the possibility of calculating or checking relationships between a suspect's physical measurements, particularly when only one component is captured as an accurately recorded measurement. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

When leaving your ex, love yourself: observational ratings of self-compassion predict the course of emotional recovery following marital separation.

PubMed

Sbarra, David A; Smith, Hillary L; Mehl, Matthias R

2012-03-01

Divorce is a highly stressful event, and much remains to be learned about the factors that promote psychological resilience when marriages come to an end. In this study, divorcing adults (N = 109) completed a 4-min stream-of-consciousness recording about their marital separation at an initial laboratory visit. Four judges rated the degree to which participants exhibited self-compassion (defined by self-kindness, an awareness of one's place in shared humanity, and emotional equanimity) in their recordings. Judges evidenced considerable agreement in their ratings of participants' self-compassion, and these ratings demonstrated strong predictive utility: Higher levels of self-compassion at the initial visit were associated with less divorce-related emotional intrusion into daily life at the start of the study, and this effect persisted up to 9 months later. These effects held when we accounted for a number of competing predictors. Self-compassion is a modifiable variable, and if our findings can be replicated, they may have implications for improving the lives of divorcing adults.

Settling for less out of fear of being single.

PubMed

Spielmann, Stephanie S; MacDonald, Geoff; Maxwell, Jessica A; Joel, Samantha; Peragine, Diana; Muise, Amy; Impett, Emily A

2013-12-01

The present research demonstrates that fear of being single predicts settling for less in romantic relationships, even accounting for constructs typically examined in relationship research such as anxious attachment. Study 1 explored the content of people's thoughts about being single. Studies 2A and 2B involved the development and validation of the Fear of Being Single Scale. Study 2C provided preliminary support for the hypothesis that fear of being single predicts settling for less in ongoing relationships, as evidenced by greater dependence in unsatisfying relationships. Study 3 replicated this effect in a longitudinal study demonstrating that fear of being single predicts lower likelihood of initiating the dissolution of a less satisfying relationship. Studies 4A and 4B explored the predictive ability of fear of being single for self-reported dating standards. Across both samples, fear of being single was unrelated to self-reported standards for a mate, with the exception of consistently higher standards for parenting. Studies 5 and 6 explored romantic interest in targets that were manipulated to vary in responsiveness and physical attractiveness. These studies found that fear of being single consistently predicted romantic interest in less responsive and less attractive dating targets. Study 7 explored fear of being single during a speed-dating event. We found that fear of being single predicted being less selective in expressing romantic interest but did not predict other daters' romantic interest. Taken together, the present research suggests that fear of being single is a meaningful predictor of settling for less in relationships. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Predicting local field potentials with recurrent neural networks.

PubMed

Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

2016-08-01

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

Modeling the performance of a photon counting x-ray detector for CT: energy response and pulse pileup effects.

PubMed

Taguchi, Katsuyuki; Zhang, Mengxi; Frey, Eric C; Wang, Xiaolan; Iwanczyk, Jan S; Nygard, Einar; Hartsough, Neal E; Tsui, Benjamin M W; Barber, William C

2011-02-01

Recently, photon counting x-ray detectors (PCXDs) with energy discrimination capabilities have been developed for potential use in clinical computed tomography (CT) scanners. These PCXDs have great potential to improve the quality of CT images due to the absence of electronic noise and weights applied to the counts and the additional spectral information. With high count rates encountered in clinical CT, however, coincident photons are recorded as one event with a higher or lower energy due to the finite speed of the PCXD. This phenomenon is called a "pulse pileup event" and results in both a loss of counts (called "deadtime losses") and distortion of the recorded energy spectrum. Even though the performance of PCXDs is being improved, it is essential to develop algorithmic methods based on accurate models of the properties of detectors to compensate for these effects. To date, only one PCXD (model DXMCT-1, DxRay, Inc., Northridge, CA) has been used for clinical CT studies. The aim of that study was to evaluate the agreement between data measured by DXMCT-1 and those predicted by analytical models for the energy response, the deadtime losses, and the distorted recorded spectrum caused by pulse pileup effects. An energy calibration was performed using 99mTc (140 keV), 57Co (122 keV), and an x-ray beam obtained with four x-ray tube voltages (35, 50, 65, and 80 kVp). The DXMCT-1 was placed 150 mm from the x-ray focal spot; the count rates and the spectra were recorded at various tube current values from 10 to 500 microA for a tube voltage of 80 kVp. Using these measurements, for each pulse height comparator we estimated three parameters describing the photon energy-pulse height curve, the detector deadtime tau, a coefficient k that relates the x-ray tube current I to an incident count rate a by a = k x I, and the incident spectrum. The mean pulse shape of all comparators was acquired in a separate study and was used in the model to estimate the distorted recorded spectrum. The agreement between data measured by the DXMCT-1 and those predicted by the models was quantified by the coefficient of variation (COV), i.e., the root mean square difference divided by the mean of the measurement. Photon energy versus pulse height curves calculated with an analytical model and those measured using the DXMCT-1 were in agreement within 0.2% in terms of the COV. The COV between the output count rates measured and those predicted by analytical models was 2.5% for deadtime losses of up to 60%. The COVs between spectra measured and those predicted by the detector model were within 3.7%-7.2% with deadtime losses of 19%-46%. It has been demonstrated that the performance of the DXMCT-1 agreed exceptionally well with the analytical models regarding the energy response, the count rate, and the recorded spectrum with pulse pileup effects. These models will be useful in developing methods to compensate for these effects in PCXD-based clinical CT systems.

[Evaluation of the capacity of the APR-DRG classification system to predict hospital mortality].

PubMed

De Marco, Maria Francesca; Lorenzoni, Luca; Addari, Piero; Nante, Nicola

2002-01-01

Inpatient mortality has increasingly been used as an hospital outcome measure. Comparing mortality rates across hospitals requires adjustment for patient risks before making inferences about quality of care based on patient outcomes. Therefore it is essential to dispose of well performing severity measures. The aim of this study is to evaluate the ability of the All Patient Refined DRG system to predict inpatient mortality for congestive heart failure, myocardial infarction, pneumonia and ischemic stroke. Administrative records were used in this analysis. We used two statistics methods to assess the ability of the APR-DRG to predict mortality: the area under the receiver operating characteristics curve (referred to as the c-statistic) and the Hosmer-Lemeshow test. The database for the study included 19,212 discharges for stroke, pneumonia, myocardial infarction and congestive heart failure from fifteen hospital participating in the Italian APR-DRG Project. A multivariate analysis was performed to predict mortality for each condition in study using age, sex and APR-DRG risk mortality subclass as independent variables. Inpatient mortality rate ranges from 9.7% (pneumonia) to 16.7% (stroke). Model discrimination, calculated using the c-statistic, was 0.91 for myocardial infarction, 0.68 for stroke, 0.78 for pneumonia and 0.71 for congestive heart failure. The model calibration assessed using the Hosmer-Leme-show test was quite good. The performance of the APR-DRG scheme when used on Italian hospital activity records is similar to that reported in literature and it seems to improve by adding age and sex to the model. The APR-DRG system does not completely capture the effects of these variables. In some cases, the better performance might be due to the inclusion of specific complications in the risk-of-mortality subclass assignment.

Neurophysiologic predictors of response to atomoxetine in young adults with attention deficit hyperactivity disorder: a pilot project.

PubMed

Leuchter, Andrew F; McGough, James J; Korb, Alexander S; Hunter, Aimee M; Glaser, Paul E A; Deldar, Ahmed; Durell, Todd M; Cook, Ian A

2014-07-01

Atomoxetine is a non-stimulant medication with sustained benefit throughout the day, and is a useful pharmacologic treatment option for young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). It is difficult to determine, however, those patients for whom atomoxetine will be both effective and advantageous. Patients may need to take the medication for several weeks before therapeutic benefit is apparent, so a biomarker that could predict atomoxetine effectiveness early in the course of treatment could be clinically useful. There has been increased interest in the study of thalamocortical oscillatory activity using quantitative electroencephalography (qEEG) as a biomarker in ADHD. In this study, we investigated qEEG absolute power, relative power, and cordance, which have been shown to predict response to reuptake inhibitor antidepressants in Major Depressive Disorder (MDD), as potential predictors of response to atomoxetine. Forty-four young adults with ADHD (ages 18-30) enrolled in a multi-site, double-blind placebo-controlled study of the effectiveness of atomoxetine and underwent serial qEEG recordings at pretreatment baseline and one week after the start of medication. qEEG measures were calculated from a subset of the sample (N = 29) that provided useable qEEG recordings. Left temporoparietal cordance in the theta frequency band after one week of treatment was associated with ADHD symptom improvement and quality of life measured at 12 weeks in atomoxetine-treated subjects, but not in those treated with placebo. Neither absolute nor relative power measures selectively predicted improvement in medication-treated subjects. Measuring theta cordance after one week of treatment could be useful in predicting atomoxetine treatment response in adult ADHD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Accessing primary care Big Data: the development of a software algorithm to explore the rich content of consultation records.

PubMed

MacRae, J; Darlow, B; McBain, L; Jones, O; Stubbe, M; Turner, N; Dowell, A

2015-08-21

To develop a natural language processing software inference algorithm to classify the content of primary care consultations using electronic health record Big Data and subsequently test the algorithm's ability to estimate the prevalence and burden of childhood respiratory illness in primary care. Algorithm development and validation study. To classify consultations, the algorithm is designed to interrogate clinical narrative entered as free text, diagnostic (Read) codes created and medications prescribed on the day of the consultation. Thirty-six consenting primary care practices from a mixed urban and semirural region of New Zealand. Three independent sets of 1200 child consultation records were randomly extracted from a data set of all general practitioner consultations in participating practices between 1 January 2008-31 December 2013 for children under 18 years of age (n=754,242). Each consultation record within these sets was independently classified by two expert clinicians as respiratory or non-respiratory, and subclassified according to respiratory diagnostic categories to create three 'gold standard' sets of classified records. These three gold standard record sets were used to train, test and validate the algorithm. Sensitivity, specificity, positive predictive value and F-measure were calculated to illustrate the algorithm's ability to replicate judgements of expert clinicians within the 1200 record gold standard validation set. The algorithm was able to identify respiratory consultations in the 1200 record validation set with a sensitivity of 0.72 (95% CI 0.67 to 0.78) and a specificity of 0.95 (95% CI 0.93 to 0.98). The positive predictive value of algorithm respiratory classification was 0.93 (95% CI 0.89 to 0.97). The positive predictive value of the algorithm classifying consultations as being related to specific respiratory diagnostic categories ranged from 0.68 (95% CI 0.40 to 1.00; other respiratory conditions) to 0.91 (95% CI 0.79 to 1.00; throat infections). A software inference algorithm that uses primary care Big Data can accurately classify the content of clinical consultations. This algorithm will enable accurate estimation of the prevalence of childhood respiratory illness in primary care and resultant service utilisation. The methodology can also be applied to other areas of clinical care. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Prediction of cognitive outcome based on the progression of auditory discrimination during coma.

PubMed

Juan, Elsa; De Lucia, Marzia; Tzovara, Athina; Beaud, Valérie; Oddo, Mauro; Clarke, Stephanie; Rossetti, Andrea O

2016-09-01

To date, no clinical test is able to predict cognitive and functional outcome of cardiac arrest survivors. Improvement of auditory discrimination in acute coma indicates survival with high specificity. Whether the degree of this improvement is indicative of recovery remains unknown. Here we investigated if progression of auditory discrimination can predict cognitive and functional outcome. We prospectively recorded electroencephalography responses to auditory stimuli of post-anoxic comatose patients on the first and second day after admission. For each recording, auditory discrimination was quantified and its evolution over the two recordings was used to classify survivors as "predicted" when it increased vs. "other" if not. Cognitive functions were tested on awakening and functional outcome was assessed at 3 months using the Cerebral Performance Categories (CPC) scale. Thirty-two patients were included, 14 "predicted survivors" and 18 "other survivors". "Predicted survivors" were more likely to recover basic cognitive functions shortly after awakening (ability to follow a standardized neuropsychological battery: 86% vs. 44%; p=0.03 (Fisher)) and to show a very good functional outcome at 3 months (CPC 1: 86% vs. 33%; p=0.004 (Fisher)). Moreover, progression of auditory discrimination during coma was strongly correlated with cognitive performance on awakening (phonemic verbal fluency: rs=0.48; p=0.009 (Spearman)). Progression of auditory discrimination during coma provides early indication of future recovery of cognitive functions. The degree of improvement is informative of the degree of functional impairment. If confirmed in a larger cohort, this test would be the first to predict detailed outcome at the single-patient level. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Seeking a fingerprint: analysis of point processes in actigraphy recording

NASA Astrophysics Data System (ADS)

Gudowska-Nowak, Ewa; Ochab, Jeremi K.; Oleś, Katarzyna; Beldzik, Ewa; Chialvo, Dante R.; Domagalik, Aleksandra; Fąfrowicz, Magdalena; Marek, Tadeusz; Nowak, Maciej A.; Ogińska, Halszka; Szwed, Jerzy; Tyburczyk, Jacek

2016-05-01

Motor activity of humans displays complex temporal fluctuations which can be characterised by scale-invariant statistics, thus demonstrating that structure and fluctuations of such kinetics remain similar over a broad range of time scales. Previous studies on humans regularly deprived of sleep or suffering from sleep disorders predicted a change in the invariant scale parameters with respect to those for healthy subjects. In this study we investigate the signal patterns from actigraphy recordings by means of characteristic measures of fractional point processes. We analyse spontaneous locomotor activity of healthy individuals recorded during a week of regular sleep and a week of chronic partial sleep deprivation. Behavioural symptoms of lack of sleep can be evaluated by analysing statistics of duration times during active and resting states, and alteration of behavioural organisation can be assessed by analysis of power laws detected in the event count distribution, distribution of waiting times between consecutive movements and detrended fluctuation analysis of recorded time series. We claim that among different measures characterising complexity of the actigraphy recordings and their variations implied by chronic sleep distress, the exponents characterising slopes of survival functions in resting states are the most effective biomarkers distinguishing between healthy and sleep-deprived groups.

Prediction of Recidivism in Juvenile Offenders Based on Discriminant Analysis.

ERIC Educational Resources Information Center

Proefrock, David W.

The recent development of strong statistical techniques has made accurate predictions of recidivism possible. To investigate the utility of discriminant analysis methodology in making predictions of recidivism in juvenile offenders, the court records of 271 male and female juvenile offenders, aged 12-16, were reviewed. A cross validation group…

The Prediction of Success in Intensive Foreign Language Training.

ERIC Educational Resources Information Center

Carroll, John B.

After a review of the problem of predicting foreign language success, this booklet describes the development, refinement, and validation of a battery of psychological tests, some involving tape-recorded auditory stimuli, for predicting rate of progress in learning a foreign language. Although the battery was developed for more general application…

Correlation of predicted and measured sonic boom characteristics from the reentry of STS-1 orbiter

NASA Technical Reports Server (NTRS)

Garcia, F., Jr.; Jones, J. H.; Henderson, H. R.

1985-01-01

Characteristics from sonic boom pressure signatures recorded at 11 locations during reentry of the Space Shuttle Orbiter Columbia are correlated with characteristics of wind tunnel signatures extrapolated from flight altitudes for Mach numbers ranging from 1.23 to 5.87. The flight pressure signature were recorded by microphones positioned at two levels near the descent groundtrack along the California corridor. The wind tunnel signatures used in theoretical predictions were measured using a 0.0041-scale model Orbiter. The mean difference between all measured and predicted overpressures is 12 percent from measured levels. With one exception, the flight signatures are very similar to theoretical n-waves.

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

PubMed Central

Gupta, Sunil; Tran, Truyen; Luo, Wei; Phung, Dinh; Kennedy, Richard Lee; Broad, Adam; Campbell, David; Kipp, David; Singh, Madhu; Khasraw, Mustafa; Matheson, Leigh; Ashley, David M; Venkatesh, Svetha

2014-01-01

Objectives Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. Setting A regional cancer centre in Australia. Participants Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. Primary and secondary outcome measures Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). Results The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. Conclusions Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems. PMID:24643167

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.

PubMed

Gupta, Sunil; Tran, Truyen; Luo, Wei; Phung, Dinh; Kennedy, Richard Lee; Broad, Adam; Campbell, David; Kipp, David; Singh, Madhu; Khasraw, Mustafa; Matheson, Leigh; Ashley, David M; Venkatesh, Svetha

2014-03-17

Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. A regional cancer centre in Australia. Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.

Automated detection of follow-up appointments using text mining of discharge records.

PubMed

Ruud, Kari L; Johnson, Matthew G; Liesinger, Juliette T; Grafft, Carrie A; Naessens, James M

2010-06-01

To determine whether text mining can accurately detect specific follow-up appointment criteria in free-text hospital discharge records. Cross-sectional study. Mayo Clinic Rochester hospitals. Inpatients discharged from general medicine services in 2006 (n = 6481). Textual hospital dismissal summaries were manually reviewed to determine whether the records contained specific follow-up appointment arrangement elements: date, time and either physician or location for an appointment. The data set was evaluated for the same criteria using SAS Text Miner software. The two assessments were compared to determine the accuracy of text mining for detecting records containing follow-up appointment arrangements. Agreement of text-mined appointment findings with gold standard (manual abstraction) including sensitivity, specificity, positive predictive and negative predictive values (PPV and NPV). About 55.2% (3576) of discharge records contained all criteria for follow-up appointment arrangements according to the manual review, 3.2% (113) of which were missed through text mining. Text mining incorrectly identified 3.7% (107) follow-up appointments that were not considered valid through manual review. Therefore, the text mining analysis concurred with the manual review in 96.6% of the appointment findings. Overall sensitivity and specificity were 96.8 and 96.3%, respectively; and PPV and NPV were 97.0 and 96.1%, respectively. of individual appointment criteria resulted in accuracy rates of 93.5% for date, 97.4% for time, 97.5% for physician and 82.9% for location. Text mining of unstructured hospital dismissal summaries can accurately detect documentation of follow-up appointment arrangement elements, thus saving considerable resources for performance assessment and quality-related research.

Does tooth wear status predict ongoing sleep bruxism in 30-year-old Japanese subjects?

PubMed

Baba, Kazuyoshi; Haketa, Tadasu; Clark, Glenn T; Ohyama, Takashi

2004-01-01

This study investigated whether tooth wear status can predict bruxism level. Sixteen Japanese subjects (eight bruxers and eight age- and gender-matched controls; mean age 30 years) participated in this study. From dental casts of these subjects, the tooth wear was scored by Murphy's method. Bruxism level in these subjects was also recorded for 5 consecutive nights in the subject's home environment using a force-based bruxism detecting system. The relationship between the tooth wear score and bruxism data was evaluated statistically. Correlation analysis between the Murphy's scores of maxillary and mandibular dental arch and bruxism event duration score revealed no significant relationship between tooth wear and current bruxism. Tooth wear status is not predictive of ongoing bruxism level as measured by the force-based bruxism detection system in 30-year-old Japanese subjects.

Mortality Prediction Model of Septic Shock Patients Based on Routinely Recorded Data

PubMed Central

Carrara, Marta; Baselli, Giuseppe; Ferrario, Manuela

2015-01-01

We studied the problem of mortality prediction in two datasets, the first composed of 23 septic shock patients and the second composed of 73 septic subjects selected from the public database MIMIC-II. For each patient we derived hemodynamic variables, laboratory results, and clinical information of the first 48 hours after shock onset and we performed univariate and multivariate analyses to predict mortality in the following 7 days. The results show interesting features that individually identify significant differences between survivors and nonsurvivors and features which gain importance only when considered together with the others in a multivariate regression model. This preliminary study on two small septic shock populations represents a novel contribution towards new personalized models for an integration of multiparameter patient information to improve critical care management of shock patients. PMID:26557154

Evolution of language: An empirical study at eBay Big Data Lab

PubMed Central

Bodoff, David; Dai, Julie

2017-01-01

The evolutionary theory of language predicts that a language will tend towards fewer synonyms for a given object. We subject this and related predictions to empirical tests, using data from the eBay Big Data Lab which let us access all records of the words used by eBay vendors in their item titles, and by consumers in their searches. We find support for the predictions of the evolutionary theory of language. In particular, the mapping from object to words sharpens over time on both sides of the market, i.e. among consumers and among vendors. In addition, the word mappings used on the two sides of the market become more similar over time. Our research contributes to the literature on language evolution by reporting results of a truly unique large-scale empirical study. PMID:29261686

Evolution of language: An empirical study at eBay Big Data Lab.

PubMed

Bodoff, David; Bekkerman, Ron; Dai, Julie

2017-01-01

The evolutionary theory of language predicts that a language will tend towards fewer synonyms for a given object. We subject this and related predictions to empirical tests, using data from the eBay Big Data Lab which let us access all records of the words used by eBay vendors in their item titles, and by consumers in their searches. We find support for the predictions of the evolutionary theory of language. In particular, the mapping from object to words sharpens over time on both sides of the market, i.e. among consumers and among vendors. In addition, the word mappings used on the two sides of the market become more similar over time. Our research contributes to the literature on language evolution by reporting results of a truly unique large-scale empirical study.

Prediction of human dietary δ15N intake from standardised food records: validity and precision of single meal and 24-h diet data.

PubMed

Hülsemann, Frank; Koehler, Karsten; Wittsiepe, Jürgen; Wilhelm, Michael; Hilbig, Annett; Kersting, Mathilde; Braun, Hans; Flenker, Ulrich; Schänzer, Wilhelm

2017-08-01

Natural stable isotope ratios (δ 15 N) of humans can be used for nutritional analyses and dietary reconstruction of modern and historic individuals and populations. Information about an individual's metabolic state can be obtained by comparison of tissue and dietary δ 15 N. Different methods have been used to estimate dietary δ 15 N in the past; however, the validity of such predictions has not been compared to experimental values. For a total of 56 meals and 21 samples of 24-h diets, predicted and experimental δ 15 N values were compared. The δ 15 N values were predicted from self-recorded food intake and compared with experimental δ 15 N values. Predicted and experimental δ 15 N values were in good agreement for meals and preparations (r = 0.89, p < .001) as well as for the 24-h diets (r = 0.76, p < .001). Dietary δ 15 N was mainly determined by the amount of fish, whereas the contribution of meat to dietary δ 15 N values was less pronounced. Prediction of human dietary δ 15 N values using standardised food records and representative δ 15 N data sets yields reliable data for dietary δ 15 N intake. A differentiated analysis of the primary protein sources is necessary when relating the proportion of animal-derived protein in the diet by δ 15 N analysis.

The natural mathematics of behavior analysis.

PubMed

Li, Don; Hautus, Michael J; Elliffe, Douglas

2018-04-19

Models that generate event records have very general scope regarding the dimensions of the target behavior that we measure. From a set of predicted event records, we can generate predictions for any dependent variable that we could compute from the event records of our subjects. In this sense, models that generate event records permit us a freely multivariate analysis. To explore this proposition, we conducted a multivariate examination of Catania's Operant Reserve on single VI schedules in transition using a Markov Chain Monte Carlo scheme for Approximate Bayesian Computation. Although we found systematic deviations between our implementation of Catania's Operant Reserve and our observed data (e.g., mismatches in the shape of the interresponse time distributions), the general approach that we have demonstrated represents an avenue for modelling behavior that transcends the typical constraints of algebraic models. © 2018 Society for the Experimental Analysis of Behavior.

Self-regulating the effortful "social dos".

PubMed

Cortes, Kassandra; Kammrath, Lara K; Scholer, Abigail A; Peetz, Johanna

2014-03-01

In the current research, we explored differences in the self-regulation of the personal dos (i.e., engaging in active and effortful behaviors that benefit the self) and in the self-regulation of the social dos (engaging in those same effortful behaviors to benefit someone else). In 6 studies, we examined whether the same trait self-control abilities that predict task persistence on personal dos would also predict task persistence on social dos. That is, would the same behavior, such as persisting through a tedious and attentionally demanding task, show different associations with trait self-control when it is framed as benefitting the self versus someone else? In Studies 1-3, we directly compared the personal and social dos and found that trait self-control predicted self-reported and behavioral personal dos but not social dos, even when the behaviors were identical and when the incentives were matched. Instead, trait agreeableness--a trait linked to successful self-regulation within the social domain--predicted the social dos. Trait self-control did not predict the social dos even when task difficulty increased (Study 4), but it did predict the social don'ts, consistent with past research (Studies 5-6). The current studies provide support for the importance of distinguishing different domains of self-regulated behaviors and suggest that social dos can be successfully performed through routes other than traditional self-control abilities. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

The End of Theory? Does the Data Deluge Make the Scientific Method Obsolete?

NASA Astrophysics Data System (ADS)

Kreinovich, Vladik; McClure, John; Symons, John

2008-10-01

Why do we need theory? One of the purposes of science is to predict: e.g., how a complex material behaves in different situations. There are a lot of records describing how different materials behave in different situations. In the past, it was not possible to find a similar record and simply recall what happened then. The only possibility was to extract, from the data, a simple dependence, and then use this dependence for predictions. For example, we can use Ohm's law V=I.R to predict the voltage V based on the current I and the resistance R. Nowadays, computer searches are so fast that there seems to be no need for any theoretical laws anymore: if we want to predict, we can simply search through all the records and find what happened in a similar situation. So maybe we do not need theory at all. This was the argument developed in a recent (June 2008) article in a popular Wired magazine. In our presentation, we will describe this argument in detail, and give our opinion on whether the computer progress will indeed lead to the end of the theory as we know it.

Statistics of return intervals between long heartbeat intervals and their usability for online prediction of disorders

NASA Astrophysics Data System (ADS)

Bogachev, Mikhail I.; Kireenkov, Igor S.; Nifontov, Eugene M.; Bunde, Armin

2009-06-01

We study the statistics of return intervals between large heartbeat intervals (above a certain threshold Q) in 24 h records obtained from healthy subjects. We find that both the linear and the nonlinear long-term memory inherent in the heartbeat intervals lead to power-laws in the probability density function PQ(r) of the return intervals. As a consequence, the probability WQ(t; Δt) that at least one large heartbeat interval will occur within the next Δt heartbeat intervals, with an increasing elapsed number of intervals t after the last large heartbeat interval, follows a power-law. Based on these results, we suggest a method of obtaining a priori information about the occurrence of the next large heartbeat interval, and thus to predict it. We show explicitly that the proposed method, which exploits long-term memory, is superior to the conventional precursory pattern recognition technique, which focuses solely on short-term memory. We believe that our results can be straightforwardly extended to obtain more reliable predictions in other physiological signals like blood pressure, as well as in other complex records exhibiting multifractal behaviour, e.g. turbulent flow, precipitation, river flows and network traffic.

Mortality in Code Blue; can APACHE II and PRISM scores be used as markers for prognostication?

PubMed

Bakan, Nurten; Karaören, Gülşah; Tomruk, Şenay Göksu; Keskin Kayalar, Sinem

2018-03-01

Code blue (CB) is an emergency call system developed to respond to cardiac and respiratory arrest in hospitals. However, in literature, no scoring system has been reported that can predict mortality in CB procedures. In this study, we aimed to investigate the effectiveness of estimated APACHE II and PRISM scores in the prediction of mortality in patients assessed using CB to retrospectively analyze CB calls. We retrospectively examined 1195 patients who were evaluated by the CB team at our hospital between 2009 and 2013. The demographic data of the patients, diagnosis and relevant de-partments, reasons for CB, cardiopulmonary resuscitation duration, mortality calculated from the APACHE II and PRISM scores, and the actual mortality rates were retrospectively record-ed from CB notification forms and the hospital database. In all age groups, there was a significant difference between actual mortality rate and the expected mortality rate as estimated using APACHE II and PRISM scores in CB calls (p<0.05). The actual mortality rate was significantly lower than the expected mortality. APACHE and PRISM scores with the available parameters will not help predict mortality in CB procedures. Therefore, novels scoring systems using different parameters are needed.

A multichannel model-based methodology for extubation readiness decision of patients on weaning trials.

PubMed

Casaseca-de-la-Higuera, Pablo; Simmross-Wattenberg, Federico; Martín-Fernández, Marcos; Alberola-López, Carlos

2009-07-01

Discontinuation of mechanical ventilation is a challenging task that involves a number of subtle clinical issues. The gradual removal of the respiratory support (referred to as weaning) should be performed as soon as autonomous respiration can be sustained. However, the prediction rate of successful extubation is still below 25% based on previous studies. Construction of an automatic system that provides information on extubation readiness is thus desirable. Recent works have demonstrated that the breathing pattern variability is a useful extubation readiness indicator, with improving performance when multiple respiratory signals are jointly processed. However, the existing methods for predictor extraction present several drawbacks when length-limited time series are to be processed in heterogeneous groups of patients. In this paper, we propose a model-based methodology for automatic readiness prediction. It is intended to deal with multichannel, nonstationary, short records of the breathing pattern. Results on experimental data yield an 87.27% of successful readiness prediction, which is in line with the best figures reported in the literature. A comparative analysis shows that our methodology overcomes the shortcomings of so far proposed methods when applied to length-limited records on heterogeneous groups of patients.

Predicting space climate change

NASA Astrophysics Data System (ADS)

Balcerak, Ernie

2011-10-01

Galactic cosmic rays and solar energetic particles can be hazardous to humans in space, damage spacecraft and satellites, pose threats to aircraft electronics, and expose aircrew and passengers to radiation. A new study shows that these threats are likely to increase in coming years as the Sun approaches the end of the period of high solar activity known as “grand solar maximum,” which has persisted through the past several decades. High solar activity can help protect the Earth by repelling incoming galactic cosmic rays. Understanding the past record can help scientists predict future conditions. Barnard et al. analyzed a 9300-year record of galactic cosmic ray and solar activity based on cosmogenic isotopes in ice cores as well as on neutron monitor data. They used this to predict future variations in galactic cosmic ray flux, near-Earth interplanetary magnetic field, sunspot number, and probability of large solar energetic particle events. The researchers found that the risk of space weather radiation events will likely increase noticeably over the next century compared with recent decades and that lower solar activity will lead to increased galactic cosmic ray levels. (Geophysical Research Letters, doi:10.1029/2011GL048489, 2011)

Baseline dental plaque activity, mutans streptococci culture, and future caries experience in children.

PubMed

Hallett, Kerrod B; O'Rourke, Peter K

2013-01-01

The purpose of this study was to evaluate a chairside caries risk assessment protocol utilizing a caries prediction instrument, adenosine triphosphate (ATP) activity in dental plaque, mutans streptococci (MS) culture, and routine dental examination in five- to 10-year-old children at two regional Australian schools with high caries experience. Clinical indicators for future caries were assessed at baseline examination using a standardized prediction instrument. Plaque ATP activity was measured directly in relative light units (RLU) using a bioluminescence meter, and MS culture data were recorded. Each child's dentition was examined clinically and radiographically, and caries experience was recorded using enamel white spot lesions and decayed, missing, and filled surfaces for primary and permanent teeth indices. Univariate one-way analysis of variance between selected clinical indicators, ATP activity, MS count at baseline, and future new caries activity was performed, and a generalized linear model for prediction of new caries activity at 24 months was constructed. Future new caries activity was significantly associated with the presence of visible cavitations, reduced saliva flow, and orthodontic appliances at baseline (R(2)=0.2, P<.001). Baseline plaque adenosine triphosphate activity and mutans streptococci counts were not significantly associated with caries activity at 24 months.

How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)?

PubMed

Padalia, Hitendra; Srivastava, Vivek; Kushwaha, S P S

2015-04-01

Invasive species and climate change are considered as the most serious global environmental threats. In this study, we investigated the influence of projected global climate change on the potential distribution of one of the world's most successful invader weed, bushmint (Hyptis suaveolens (L.) Poit.). We used spatial data on 20 environmental variables at a grid resolution of 5 km, and 564 presence records of bushmint from its native and introduced range. The climatic profiles of the native and invaded sites were analyzed in a multi-variate space in order to examine the differences in the position of climatic niches. Maximum Entropy (MaxEnt) model was used to predict the potential distribution of bushmint using presence records from entire range (invaded and native) along with 14 eco-physiologically relevant predictor variables. Subsequently, the trained MaxEnt model was fed with Hadley Centre Coupled Model (HadCM3) climate projections to predict potential distribution of bushmint by the year 2050 under A2a and B2a emission scenarios. MaxEnt predictions were very accurate with an Area Under Curve (AUC) value of 0.95. The results of Principal Component Analysis (PCA) indicated that climatic niche of bushmint on the invaded sites is not entirely similar to its climatic niche in the native range. A vast area spread between 34 ° 02' north and 28 ° 18' south latitudes in tropics was predicted climatically suitable for bushmint. West and middle Africa, tropical southeast Asia, and northern Australia were predicted at high invasion risk. Study indicates enlargement, retreat, or shift across bushmint's invasion range under the influence of climate change. Globally, bushmint's potential distribution might shrink in future with more shrinkage for A2a scenario than B2a. The study outcome has immense potential for undertaking effective preventive/control measures and long-term management strategies for regions/countries, which are at higher risk of bushmint's invasion.

Relationships between milk mid-IR predicted gastro-enteric methane production and the technical and financial performance of commercial dairy herds.

PubMed

Delhez, P; Wyzen, B; Dalcq, A-C; Colinet, F G; Reding, E; Vanlierde, A; Dehareng, F; Gengler, N; Soyeurt, H

2017-12-22

Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4) production predicted from milk mid-IR (MIR) spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk MIR spectra (MIR-CH4 (g/day)) of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1024 herd and year records from 2007 to 2014. Pearson correlation coefficients (r) were used to assess significant relationships (P<0.05). Low HYMIR-CH4 was significantly associated with, amongst others, lower fat and protein corrected milk (FPCM) yield (r=0.18), lower milk fat and protein content (r=0.38 and 0.33, respectively), lower quantity of milk produced from forages (r=0.12) and suboptimal reproduction and health performance (e.g. longer calving interval (r=-0.21) and higher culling rate (r=-0.15)). Concerning economic results, low HYMIR-CH4 was significantly associated with lower gross margin per cow (r=0.19) and per litre FPCM (r=0.09). To conclude, this study suggested that low lactating dairy cow gastro-enteric CH4 production tended to be associated with more extensive or suboptimal management practices, which could lead to lower profitability. The observed low correlations suggest complex interactions between variables due to the use of on-farm data with large variability in technical and management practices.

Cardiac Auscultation Using Smartphones: Pilot Study.

PubMed

Kang, Si-Hyuck; Joe, Byunggill; Yoon, Yeonyee; Cho, Goo-Yeong; Shin, Insik; Suh, Jung-Won

2018-02-28

Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits. The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage. We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values. A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices. Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge. ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu). ©Si-Hyuck Kang, Byunggill Joe, Yeonyee Yoon, Goo-Yeong Cho, Insik Shin, Jung-Won Suh. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 28.02.2018.

Cardiac Auscultation Using Smartphones: Pilot Study

PubMed Central

Kang, Si-Hyuck; Joe, Byunggill; Yoon, Yeonyee; Cho, Goo-Yeong; Shin, Insik

2018-01-01

Background Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits. Objective The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage. Methods We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values. Results A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices. Conclusions Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge. Trial Registration ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu) PMID:29490899

Can the presence of an infection be predicted before a revision total hip arthroplasty? Preliminary study to establish an infection score.

PubMed

Jenny, J-Y; Adamczewski, B; De Thomasson, E; Godet, J; Bonfait, H; Delaunay, C

2016-04-01

The diagnosis of periprosthetic joint infection can be challenging, in part because there is no universal diagnostic test. Current recommendations include several diagnostic criteria, and are mainly based on the results of deep microbiological samples; however, these only provide a diagnosis after surgery. A predictive infection score would improve the management of revision arthroplasty cases. The purpose of this study was to define a composite infection score using standard clinical, radiological and laboratory data that can be used to predict whether an infection is present before a total hip arthroplasty (THA) revision procedure. The infection score will make it possible to differentiate correctly between infected and non-infected patients in 75% of cases. One hundred and four records from patients who underwent THA revision for any reason were analysed retrospectively: 43 with infection and 61 without infection. There were 54 men and 50 women with an average age of 70±12 years (range 30-90). A univariate analysis was performed to look for individual discriminating factors between the data in the medical records of infected and non-infected patients. A multivariate analysis subsequently integrated these factors together. A composite score was defined and its diagnostic effectiveness was evaluated as the percentage of correctly classified records, along with its sensitivity and specificity. The score consisted of the following individually weighed factors: body mass index, presence of diabetes, mechanical complication, wound healing disturbance and fever. This composite infection score was able to distinguish correctly between the infected patients (positive score) and non-infected patients (negative score) in 78% of cases; the sensitivity was 57% and the specificity 93%. Once this score is evaluated prospectively, it could be an important tool for defining the medical - surgical strategy during THA revision, no matter the reason for revision. Level IV - retrospective study. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Development and validation of an electronic phenotyping algorithm for chronic kidney disease

PubMed Central

Nadkarni, Girish N; Gottesman, Omri; Linneman, James G; Chase, Herbert; Berg, Richard L; Farouk, Samira; Nadukuru, Rajiv; Lotay, Vaneet; Ellis, Steve; Hripcsak, George; Peissig, Peggy; Weng, Chunhua; Bottinger, Erwin P

2014-01-01

Twenty-six million Americans are estimated to have chronic kidney disease (CKD) with increased risk for cardiovascular disease and end stage renal disease. CKD is frequently undiagnosed and patients are unaware, hampering intervention. A tool for accurate and timely identification of CKD from electronic medical records (EMR) could improve healthcare quality and identify patients for research. As members of eMERGE (electronic medical records and genomics) Network, we developed an automated phenotyping algorithm that can be deployed to identify rapidly diabetic and/or hypertensive CKD cases and controls in health systems with EMRs It uses diagnostic codes, laboratory results, medication and blood pressure records, and textual information culled from notes. Validation statistics demonstrated positive predictive values of 96% and negative predictive values of 93.3. Similar results were obtained on implementation by two independent eMERGE member institutions. The algorithm dramatically outperformed identification by ICD-9-CM codes with 63% positive and 54% negative predictive values, respectively. PMID:25954398

The FIM instrument to identify patients at risk of falling in geriatric wards: a 10-year retrospective study.

PubMed

Petitpierre, Nicolas Julien; Trombetti, Andrea; Carroll, Iain; Michel, Jean-Pierre; Herrmann, François Richard

2010-05-01

the main objective was to evaluate if the admission functional independence measure (FIM) score could be used to predict the risk of falls in geriatric inpatients. a 10-year retrospective study was performed. the study was conducted in a 298-bed geriatric teaching hospital in Geneva, Switzerland. all patients discharged from the hospital from 1 January 1997 to 31 December 2006 were selected. measures used were FIM scores at admission using the FIM instrument and number of falls extracted from the institution's fall report forms. during the study period, there were 23,966 hospital stays. A total of 8,254 falls occurred. Of these, 7,995 falls were linked to 4,651 stays. Falls were recorded in 19.4% of hospital stays, with a mean incidence of 7.84 falls per 1,000 patients-days. Although there was a statistically significant relationship between total FIM score, its subscales, and the risk of falling, the sensitivity, specificity, positive predictive value and negative predictive value obtained with receiver operating characteristic curves were insufficient to permit fall prediction. This might be due in part to a non-linear relationship between FIM score and fall risk. in this study, the FIM instrument was found to be unable to predict risk of falls in general geriatric wards.

The Perfect Burrow, but for What? Identifying Local Habitat Conditions Promoting the Presence of the Host and Vector Species in the Kazakh Plague System

PubMed Central

Wilschut, Liesbeth; Addink, Elisabeth; Ageyev, Vladimir; Yeszhanov, Aidyn; Sapozhnikov, Valerij; Belayev, Alexander; Davydova, Tania; Eagle, Sally; Begon, Mike

2015-01-01

Introduction The wildlife plague system in the Pre-Balkhash desert of Kazakhstan has been a subject of study for many years. Much progress has been made in generating a method of predicting outbreaks of the disease (infection by the gram negative bacterium Yersinia pestis) but existing methods are not yet accurate enough to inform public health planning. The present study aimed to identify characteristics of individual mammalian host (Rhombomys opimus) burrows related to and potentially predictive of the presence of R.opimus and the dominant flea vectors (Xenopsylla spp.). Methods Over four seasons, burrow characteristics, their current occupancy status, and flea and tick burden of the occupants were recorded in the field. A second data set was generated of long term occupancy trends by recording the occupancy status of specific burrows over multiple occasions. Generalised linear mixed models were constructed to identify potential burrow properties predictive of either occupancy or flea burden. Results At the burrow level, it was identified that a burrow being occupied by Rhombomys, and remaining occupied, were both related to the characteristics of the sediment in which the burrow was constructed. The flea burden of Rhombomys in a burrow was found to be related to the tick burden. Further larger scale properties were also identified as being related to both Rhombomys and flea presence, including latitudinal position and the season. Conclusions Therefore, in advancing our current predictions of plague in Kazakhstan, we must consider the landscape at this local level to increase our accuracy in predicting the dynamics of gerbil and flea populations. Furthermore this demonstrates that in other zoonotic systems, it may be useful to consider the distribution and location of suitable habitat for both host and vector species at this fine scale to accurately predict future epizootics. PMID:26325073

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study.

PubMed

Hu, Zhongkai; Hao, Shiying; Jin, Bo; Shin, Andrew Young; Zhu, Chunqing; Huang, Min; Wang, Yue; Zheng, Le; Dai, Dorothy; Culver, Devore S; Alfreds, Shaun T; Rogow, Todd; Stearns, Frank; Sylvester, Karl G; Widen, Eric; Ling, Xuefeng

2015-09-22

The increasing rate of health care expenditures in the United States has placed a significant burden on the nation's economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. In the HealthInfoNet, Maine's health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient's next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree-based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes.

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study

PubMed Central

Hu, Zhongkai; Hao, Shiying; Jin, Bo; Shin, Andrew Young; Zhu, Chunqing; Huang, Min; Wang, Yue; Zheng, Le; Dai, Dorothy; Culver, Devore S; Alfreds, Shaun T; Rogow, Todd; Stearns, Frank

2015-01-01

Background The increasing rate of health care expenditures in the United States has placed a significant burden on the nation’s economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. Objective This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. Methods In the HealthInfoNet, Maine’s health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient’s next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree–based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. Results Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. Conclusions The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes. PMID:26395541

Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients.

PubMed

Cazzola, Mario; Calzetta, Luigino; Matera, Maria Gabriella; Muscoli, Saverio; Rogliani, Paola; Romeo, Francesco

2015-08-01

Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients. We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed. In training population the variables gender (men/women OR: 1.7, 95%CI: 1.237-2.5, P < 0.05), dyslipidemia (OR: 1.8, 95%CI: 1.2-2.5, P < 0.01) and smoking habit (OR: 1.5, 95%CI: 1.2-1.9, P < 0.001) were significantly associated with CAD in COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%CI: 77.78-84.71) and an AUC of 0.81 (95%CI: 0.78-0.85) for the validation set. The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

Physicians' acceptance of electronic medical records exchange: an extension of the decomposed TPB model with institutional trust and perceived risk.

PubMed

Hsieh, Pi-Jung

2015-01-01

Electronic medical records (EMRs) exchange improves clinical quality and reduces medical costs. However, few studies address the antecedent factors of physicians' intentions to use EMR exchange. Based on institutional trust and perceived risk integrated with the decomposed theory of planned behavior (TPB) model, we propose a theoretical model to explain the intention of physicians to use an EMR exchange system. We conducted a field survey in Taiwan to collect data from physicians who had experience using the EMR exchange systems. A valid sample of 191 responses was collected for data analysis. To test the proposed research model, we employed structural equation modeling using the partial least squares method. The study findings show that the following five factors have a significant influence on the physicians' intentions to use EMR exchange systems: (a) attitude; (b) subjective norm; (c) perceived behavior control; (d) institutional trust; and (e) perceived risk. These five factors are predictable by perceived usefulness, perceived ease of use, and compatibility, interpersonal and governmental influence, facilitating conditions and self-efficacy, situational normality and structural assurance, and institutional trust, respectively. The results also indicate that institutional trust and perceived risk integrated with the decomposed TPB model improve the prediction of physician's intentions to use EMR exchange. The results of this study indicate that our research model effectively predicts the intention of physicians to use EMR exchange, and provides valuable implications for academics and practitioners. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Interobserver Reliability of the Respiratory Physical Examination in Premature Infants: A Multicenter Study

PubMed Central

Jensen, Erik A.; Panitch, Howard; Feng, Rui; Moore, Paul E.; Schmidt, Barbara

2017-01-01

Objective To measure the inter-rater reliability of 7 visual and 3 auscultatory respiratory physical examination findings at 36–40 weeks’ postmenstrual age in infants born less than 29 weeks’ gestation. Physicians also estimated the probability that each infant would remain hospitalized for 3 months after the examination or be readmitted for a respiratory illness during that time. Study design Prospective, multicenter, inter-rater reliability study using standardized audio-video recordings of respiratory physical examinations. Results We recorded the respiratory physical examination of 30 infants at 2 centers and invited 32 physicians from 9 centers to review the examinations. The intraclass correlation values for physician agreement ranged from 0.73 (95% CI 0.57–0.85) for subcostal retractions to 0.22 (95% CI 0.11–0.41) for expiratory abdominal muscle use. Eight (27%) infants remained hospitalized or were readmitted within 3 months after the examination. The area under the receiver operating characteristic curve for prediction of this outcome was 0.82 (95% CI 0.78–0.86). Physician predictive accuracy was greater for infants receiving supplemental oxygen (0.90, 95% CI 0.86–0.95) compared with those breathing in room air (0.71, 95% CI 0.66–0.75). Conclusions Physicians often do not agree on respiratory physical examination findings in premature infants. Physician prediction of short-term respiratory morbidity was more accurate for infants receiving supplemental oxygen compared with those breathing in room air. PMID:27567413

No Associations between Interindividual Differences in Sleep Parameters and Episodic Memory Consolidation.

PubMed

Ackermann, Sandra; Hartmann, Francina; Papassotiropoulos, Andreas; de Quervain, Dominique J-F; Rasch, Björn

2015-06-01

Sleep and memory are stable and heritable traits that strongly differ between individuals. Sleep benefits memory consolidation, and the amount of slow wave sleep, sleep spindles, and rapid eye movement sleep have been repeatedly identified as reliable predictors for the amount of declarative and/or emotional memories retrieved after a consolidation period filled with sleep. These studies typically encompass small sample sizes, increasing the probability of overestimating the real association strength. In a large sample we tested whether individual differences in sleep are predictive for individual differences in memory for emotional and neutral pictures. Between-subject design. Cognitive testing took place at the University of Basel, Switzerland. Sleep was recorded at participants' homes, using portable electroencephalograph-recording devices. Nine hundred-twenty-nine healthy young participants (mean age 22.48 ± 3.60 y standard deviation). None. In striking contrast to our expectations as well as numerous previous findings, we did not find any significant correlations between sleep and memory consolidation for pictorial stimuli. Our results indicate that individual differences in sleep are much less predictive for pictorial memory processes than previously assumed and suggest that previous studies using small sample sizes might have overestimated the association strength between sleep stage duration and pictorial memory performance. Future studies need to determine whether intraindividual differences rather than interindividual differences in sleep stage duration might be more predictive for the consolidation of emotional and neutral pictures during sleep. © 2015 Associated Professional Sleep Societies, LLC.

Detection of Temporally and Spatially Limited Periodic Earthquake Recurrence in Synthetic Seismic Records

NASA Astrophysics Data System (ADS)

Zielke, O.; Arrowsmith, R. J.

2005-12-01

The nonlinear dynamics of fault behavior are dominated by complex interactions among the multiple processes controlling the system. For example, temporal and spatial variations in pore pressure, healing effects, and stress transfer cause significant heterogeneities in fault properties and the stress-field at the sub-fault level. Numerical and laboratory fault models show that the interaction of large systems of fault elements causes the entire system to develop into a state of self-organized criticality. Once in this state, small perturbations of the system may result in chain reactions (i.e., earthquakes) which can affect any number of fault segments. This sensitivity to small perturbations is strong evidence for chaotic fault behavior, which implies that exact event prediction is not possible. However, earthquake prediction with a useful accuracy is nevertheless possible. Studies of other natural chaotic systems have shown that they may enter states of metastability, in which the system's behavior is predictable. Applying this concept to earthquake faults, these windows of metastable behavior should be characterized by periodic earthquake recurrence. The observed periodicity of the Parkfield, CA (M= 6) events may resemble such a window of metastability. I am statistically analyzing numerically generated seismic records to study these phases of periodic behavior. In this preliminary study, seismic records were generated using a model introduced by Nakanishi [Phys. Rev. A, 43, 6613-6621, 1991]. It consists of a one-dimensional chain of blocks (interconnected by springs) with a relaxation function that mimics velocity-weakened frictional behavior. The earthquakes occurring in this model show generally a power-law frequency-size distribution. However, for large events the distribution has a shoulder where the frequency of events is higher than expected from the power law. I have analyzed time-series of single block motions within the system. These time-series include noticeable periodicity during certain intervals in an otherwise aperiodic record. The observed periodic signal is not equally distributed over the range of offsets but shows a multi-modal distribution with increased periodicity for the smallest events and for large events that show a specific offset. These large events also form a shoulder in the frequency-size distribution. Apparently, the model exhibits characteristic earthquakes (defined by similar coseismic slip) that occur more frequently than expected from a power law distribution, and also are significantly more periodic. The wavelength of the periodic signal generally equals the minimum loading time, which is related to the loading velocity and the amount of coseismic slip (i.e., stress drop). No significant event occurs between the characteristic events as long as the system stays in a window of periodic behavior. Within the windows of periodic behavior, earthquake prediction is straightforward. Therefore, recognition of these windows not only in synthetic data but also in real seismic records, may improve the intra-window forecast of earthquakes. Further studies will attempt to determine the characteristics of onset, duration, and end of these windows of periodic earthquake recurrence. Only the motion of a single block within a bigger system was analyzed so far. Going from a zero dimensional scenario to a two dimensional case where the offsets not only of a single block but the displacement patterns caused by a certain event are analyzed will increase the verisimilitude of the detection of periodic earthquake recurrence within an otherwise chaotic seismic record.

Comparing a medical records-based and a claims-based index for measuring comorbidity in patients with lung or colon cancer.

PubMed

Kehl, Kenneth L; Lamont, Elizabeth B; McNeil, Barbara J; Bozeman, Samuel R; Kelley, Michael J; Keating, Nancy L

2015-05-01

Ascertaining comorbid conditions in cancer patients is important for research and clinical quality measurement, and is particularly important for understanding care and outcomes for older patients and those with multi-morbidity. We compared the medical records-based ACE-27 index and the claims-based Charlson index in predicting receipt of therapy and survival for lung and colon cancer patients. We calculated the Charlson index using administrative data and the ACE-27 score using medical records for Veterans Affairs patients diagnosed with stage I/II non-small cell lung or stage III colon cancer from January 2003 to December 2004. We compared the proportion of patients identified by each index as having any comorbidity. We used multivariable logistic regression to ascertain the predictive power of each index regarding delivery of guideline-recommended therapies and two-year survival, comparing the c-statistic and the Akaike information criterion (AIC). Overall, 97.2% of lung and 90.9% of colon cancer patients had any comorbidity according to the ACE-27 index, versus 59.5% and 49.7%, respectively, according to the Charlson. Multivariable models including the ACE-27 index outperformed Charlson-based models when assessing receipt of guideline-recommended therapies, with higher c-statistics and lower AICs. Neither index was clearly superior in prediction of two-year survival. The ACE-27 index measured using medical records captured more comorbidity and outperformed the Charlson index measured using administrative data for predicting receipt of guideline-recommended therapies, demonstrating the potential value of more detailed comorbidity data. However, the two indices had relatively similar performance when predicting survival. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting length of stay from an electronic patient record system: a primary total knee replacement example.

PubMed

Carter, Evelene M; Potts, Henry W W

2014-04-04

To investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay. Data were extracted from the electronic patient record system for discharges from primary total knee operations from January 2007 to December 2011 (n=2,130) at one UK hospital and analysed for their effect on length of stay using Mann-Whitney and Kruskal-Wallis tests for discrete data and Spearman's correlation coefficient for continuous data. Models for predicting length of stay for primary total knee replacements were tested using the Poisson regression and the negative binomial modelling techniques. Factors found to have a significant effect on length of stay were age, gender, consultant, discharge destination, deprivation and ethnicity. Applying a negative binomial model to these variables was successful. The model predicted the length of stay of those patients who stayed 4-6 days (~50% of admissions) with 75% accuracy within 2 days (model data). Overall, the model predicted the total days stayed over 5 years to be only 88 days more than actual, a 6.9% uplift (test data). Valuable information can be found about length of stay from the analysis of variables easily extracted from an electronic patient record system. Models can be successfully created to help improve resource planning and from which a simple decision support system can be produced to help patient expectation on their length of stay.

Altruism in the wild: when affiliative motives to help positive people overtake empathic motives to help the distressed.

PubMed

Hauser, David J; Preston, Stephanie D; Stansfield, R Brent

2014-06-01

Psychological theories of human altruism suggest that helping results from an evolved tendency in caregiving mammals to respond to distress or need with empathy and sympathy. However, theories from biology, economics, and social psychology demonstrate that social animals also evolved to affiliate with and help desirable social partners. These models make different predictions about the affect of those we should prefer to help. Empathic models predict a preference to help sad, distressed targets in need, while social affiliative models predict a preference for happy, positive, successful targets. We compared these predictions in 3 field studies that measured the tendency to help sad, happy, and neutral confederates in a real-world, daily context: holding the door for a stranger in public. People consistently held the door more for happy over sad or neutral targets. To allow empathic motivations to compete more strongly against social affiliative ones, a 4th study examined a more consequential form of aid for hypothetical hospital patients in clear need. These conditions enhanced the preference to help a sad over a happy patient, because sadness made the patient appear sicker and in greater need. However, people still preferred the happy patient when the aid required a direct social interaction, attesting to the strength of social affiliation motives, even for sick patients. Theories of prosocial behavior should place greater emphasis on the role of social affiliation in motivating aid, particularly in everyday interpersonal contexts. (PsycINFO Database Record (c) 2014 APA, all rights reserved). PsycINFO Database Record (c) 2014 APA, all rights reserved.

"When does making detailed predictions make predictions worse?": Correction to Kelly and Simmons (2016).

PubMed

2016-10-01

Reports an error in "When Does Making Detailed Predictions Make Predictions Worse" by Theresa F. Kelly and Joseph P. Simmons ( Journal of Experimental Psychology: General , Advanced Online Publication, Aug 8, 2016, np). In the article, the symbols in Figure 2 were inadvertently altered in production. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2016-37952-001.) In this article, we investigate whether making detailed predictions about an event worsens other predictions of the event. Across 19 experiments, 10,896 participants, and 407,045 predictions about 724 professional sports games, we find that people who made detailed predictions about sporting events (e.g., how many hits each baseball team would get) made worse predictions about more general outcomes (e.g., which team would win). We rule out that this effect is caused by inattention or fatigue, thinking too hard, or a differential reliance on holistic information about the teams. Instead, we find that thinking about game-relevant details before predicting winning teams causes people to give less weight to predictive information, presumably because predicting details makes useless or redundant information more accessible and thus more likely to be incorporated into forecasts. Furthermore, we show that this differential use of information can be used to predict what kinds of events will and will not be susceptible to the negative effect of making detailed predictions. PsycINFO Database Record (c) 2016 APA, all rights reserved

Using Electrically-evoked Compound Action Potentials to Estimate Perceptive Levels in Experienced Adult Cochlear Implant Users.

PubMed

Joly, Charles-Alexandre; Péan, Vincent; Hermann, Ruben; Seldran, Fabien; Thai-Van, Hung; Truy, Eric

2017-10-01

The cochlear implant (CI) fitting level prediction accuracy of electrically-evoked compound action potential (ECAP) should be enhanced by the addition of demographic data in models. No accurate automated fitting of CI based on ECAP has yet been proposed. We recorded ECAP in 45 adults who had been using MED-EL CIs for more than 11 months and collected the most comfortable loudness level (MCL) used for CI fitting (prog-MCL), perception thresholds (meas-THR), and MCL (meas-MCL) measured with the stimulation used for ECAP recording. Linear mixed models taking into account cochlear site factors were computed to explain prog-MCL, meas-MCL, and meas-THR. Cochlear region and ECAP threshold were predictors of the three levels. In addition, significant predictors were the ECAP amplitude for the prog-MCL and the duration of deafness for the prog-MCL and the meas-THR. Estimations were more accurate for the meas-THR, then the meas-MCL, and finally the prog-MCL. These results show that 1) ECAP thresholds are more closely related to perception threshold than to comfort level, 2) predictions are more accurate when the inter-subject and cochlear regions variations are considered, and 3) differences between the stimulations used for ECAP recording and for CI fitting make it difficult to accurately predict the prog-MCL from the ECAP recording. Predicted prog-MCL could be used as bases for fitting but should be used with care to avoid any uncomfortable or painful stimulation.

Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes.

PubMed

Brondeel, Ruben; Pannier, Bruno; Chaix, Basile

2015-12-01

Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests. The RECORD GPS study collected real-life trip data from 236 participants over 7 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of a limited number of participants/trips to predict transportation modes for a large number of trips. The full model had a correct prediction rate of 90%. A simpler model of global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants. This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.

Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a prediction model to recognize high imaging utilizers based on their initial imaging reports. The prediction model uses a machine learning text classification framework. In this study, we used radiology reports from 18,384 patients with at least one abdomen computed tomography study in their imaging record at Stanford Health Care as the training set. We modeled the radiology reports in a vector space and trained a support vector machine classifier for this prediction task. We evaluated our model on a separate test set of 4791 patients. In addition to high prediction accuracy, in our method, we aimed at achieving high specificity to identify patients at high risk for high imaging utilization. Our results (accuracy: 94.0%, sensitivity: 74.4%, specificity: 97.9%, positive predictive value: 87.3%, negative predictive value: 95.1%) show that a prediction model can enable healthcare providers to identify in advance patients who are likely to be high utilizers of imaging services. Machine learning classifiers developed from narrative radiology reports are feasible methods to predict imaging utilization. Such systems can be used to identify high utilizers, inform future image ordering behavior, and encourage judicious use of imaging. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Prediction of Cerebral Hyperperfusion Syndrome with Velocity Blood Pressure Index.

PubMed

Lai, Zhi-Chao; Liu, Bao; Chen, Yu; Ni, Leng; Liu, Chang-Wei

2015-06-20

Cerebral hyperperfusion syndrome is an important complication of carotid endarterectomy (CEA). An >100% increase in middle cerebral artery velocity (MCAV) after CEA is used to predict the cerebral hyperperfusion syndrome (CHS) development, but the accuracy is limited. The increase in blood pressure (BP) after surgery is a risk factor of CHS, but no study uses it to predict CHS. This study was to create a more precise parameter for prediction of CHS by combined the increase of MCAV and BP after CEA. Systolic MCAV measured by transcranial Doppler and systematic BP were recorded preoperatively; 30 min postoperatively. The new parameter velocity BP index (VBI) was calculated from the postoperative increase ratios of MCAV and BP. The prediction powers of VBI and the increase ratio of MCAV (velocity ratio [VR]) were compared for predicting CHS occurrence. Totally, 6/185 cases suffered CHS. The best-fit cut-off point of 2.0 for VBI was identified, which had 83.3% sensitivity, 98.3% specificity, 62.5% positive predictive value and 99.4% negative predictive value for CHS development. This result is significantly better than VR (33.3%, 97.2%, 28.6% and 97.8%). The area under the curve (AUC) of receiver operating characteristic: AUC(VBI) = 0.981, 95% confidence interval [CI] 0.949-0.995; AUC(VR) = 0.935, 95% CI 0.890-0.966, P = 0.02. The new parameter VBI can more accurately predict patients at risk of CHS after CEA. This observation needs to be validated by larger studies.

Evaluating predictive modeling’s potential to improve teleretinal screening participation in urban safety net clinics

PubMed Central

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Introduction Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Methods Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. Results The predictive models were modestly predictive with the best model having an AUC of 0.71. Discussion Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics. PMID:23920536

Evaluating predictive modeling's potential to improve teleretinal screening participation in urban safety net clinics.

PubMed

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. The predictive models were modestly predictive with the best model having an AUC of 0.71. Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics.

Impulsivity facets' predictive relations with DSM-5 PTSD symptom clusters.

PubMed

Roley, Michelle E; Contractor, Ateka A; Weiss, Nicole H; Armour, Cherie; Elhai, Jon D

2017-01-01

Posttraumatic stress disorder (PTSD) has a well-established theoretical and empirical relation with impulsivity. Prior research has not used a multidimensional approach for measuring both PTSD and impulsivity constructs when assessing their relationship. The current study assessed the unique relationship of impulsivity facets on PTSD symptom clusters among a nonclinical sample of 412 trauma-exposed adults. Linear regression analyses revealed that impulsivity facets best accounted for PTSD's arousal symptoms. The negative urgency facet of impulsivity was most predictive, because it was associated with all of PTSD's symptom clusters. Sensation seeking did not predict PTSD's intrusion symptoms, but did predict the other symptom clusters of PTSD. Lack of perseverance only predicted intrusion symptoms, while lack of premeditation only predicted PTSD's mood/cognition symptoms. Results extend theoretical and empirical research on the impulsivity-PTSD relationship, suggesting that impulsivity facets may serve as both risk and protective factors for PTSD symptoms. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

PubMed

Reddy, Bhargava K; Delen, Dursun; Agrawal, Rupesh K

2018-01-01

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Ground motions recorded in Rome during the April 2009 L’Aquila seismic sequence: site response and comparison with ground‐motion predictions based on a global dataset

USGS Publications Warehouse

Caserta, Arrigo; Boore, David; Rovelli, Antonio; Govoni, Aladino; Marra, Fabrizio; Monica, Gieseppe Della; Boschi, Enzo

2013-01-01

The mainshock and moderate‐magnitude aftershocks of the 6 April 2009 M 6.3 L’Aquila seismic sequence, about 90 km northeast of Rome, provided the first earthquake ground‐motion recordings in the urban area of Rome. Before those recordings were obtained, the assessments of the seismic hazard in Rome were based on intensity observations and theoretical considerations. The L’Aquila recordings offer an unprecedented opportunity to calibrate the city response to central Apennine earthquakes—earthquakes that have been responsible for the largest damage to Rome in historical times. Using the data recorded in Rome in April 2009, we show that (1) published theoretical predictions of a 1 s resonance in the Tiber valley are confirmed by observations showing a significant amplitude increase in response spectra at that period, (2) the empirical soil‐transfer functions inferred from spectral ratios are satisfactorily fit through 1D models using the available geological, geophysical, and laboratory data, but local variability can be large for individual events, (3) response spectra for the motions recorded in Rome from the L’Aquila earthquakes are significantly amplified in the radial component at periods near 1 s, even at a firm site on volcanic rocks, and (4) short‐period response spectra are smaller than expected when compared to ground‐motion predictions from equations based on a global dataset, whereas the observed response spectra are higher than expected for periods near 1 s.

On microseisms recorded near the Ligurian coast (Italy) and their relationship with sea wave height

NASA Astrophysics Data System (ADS)

Ferretti, G.; Zunino, A.; Scafidi, D.; Barani, S.; Spallarossa, D.

2013-07-01

In this study, microseism recordings from a near coast seismic station and concurrent significant sea wave heights (H_{1/3}) are analysed to calibrate an empirical relation for predicting sea wave height in the Ligurian Sea. The study stems from the investigation of the damaging sea storms occurred in the Ligurian Sea between 2008 October and November. Analysing data collected in this time frame allows identification of two types of microseism signal, one associated to the local sea wave motion and one attributable to a remote source area. The former is dominated by frequencies greater than 0.2 Hz and the latter by frequencies between 0.07 and 0.14 Hz. Moreover, comparison of microseism spectrogram and significant sea wave heights reveals a strong correlation in that the spectral energy content of microseism results proportional to the sea wave height observed in the same time window. Hence, an extended data set including also observations from January to December 2011 is used to calibrate an empirical predictive relation for sea wave height whose functional form is a modified version of the classical definition of H_{1/3}. By means of a Markov chain Monte Carlo algorithm we set up a procedure to investigate the inverse problem and to find a set of parameter values for predicting sea wave heights from microseism.

First-day newborn weight loss predicts in-hospital weight nadir for breastfeeding infants.

PubMed

Flaherman, Valerie J; Bokser, Seth; Newman, Thomas B

2010-08-01

Exclusive breastfeeding reduces infant infectious disease. Losing > or =10% birth weight may lead to formula use. The predictive value of first-day weight loss for subsequent weight loss has not been studied. The objective of the present study was to evaluate the relationship between weight loss at <24 hours and subsequent in-hospital weight loss > or =10%. For 1,049 infants, we extracted gestational age, gender, delivery method, feeding type, and weights from medical records. Weight nadir was defined as the lowest weight recorded during birth hospitalization. We used multivariate logistic regression to assess the effect of first-day weight loss on subsequent in-hospital weight loss. Mean in-hospital weight nadir was 6.0 +/- 2.6%, and mean age at in-hospital weight nadir was 38.7 +/- 18.5 hours. While in the hospital 6.4% of infants lost > or =10% of birth weight. Infants losing > or =4.5% birth weight at <24 hours had greater risk of eventual in-hospital weight loss > or =10% (adjusted odds ratio 3.57 [1.75, 7.28]). In this cohort, 798 (76.1%) infants did not have documented weight gain while in the hospital. Early weight loss predicts higher risk of > or =10% in-hospital weight loss. Infants with high first-day weight loss could be targeted for further research into improved interventions to promote breastfeeding.

Role of subdural electrocorticography in prediction of long-term seizure outcome in epilepsy surgery

PubMed Central

Juhász, Csaba; Shah, Aashit; Sood, Sandeep; Chugani, Harry T.

2009-01-01

Since prediction of long-term seizure outcome using preoperative diagnostic modalities remains suboptimal in epilepsy surgery, we evaluated whether interictal spike frequency measures obtained from extraoperative subdural electrocorticography (ECoG) recording could predict long-term seizure outcome. This study included 61 young patients (age 0.4–23.0 years), who underwent extraoperative ECoG recording prior to cortical resection for alleviation of uncontrolled focal seizures. Patient age, frequency of preoperative seizures, neuroimaging findings, ictal and interictal ECoG measures were preoperatively obtained. The seizure outcome was prospectively measured [follow-up period: 2.5–6.4 years (mean 4.6 years)]. Univariate and multivariate logistic regression analyses determined how well preoperative demographic and diagnostic measures predicted long-term seizure outcome. Following the initial cortical resection, Engel Class I, II, III and IV outcomes were noted in 35, 6, 12 and 7 patients, respectively. One child died due to disseminated intravascular coagulation associated with pseudomonas sepsis 2 days after surgery. Univariate regression analyses revealed that incomplete removal of seizure onset zone, higher interictal spike-frequency in the preserved cortex and incomplete removal of cortical abnormalities on neuroimaging were associated with a greater risk of failing to obtain Class I outcome. Multivariate logistic regression analysis revealed that incomplete removal of seizure onset zone was the only independent predictor of failure to obtain Class I outcome. The goodness of regression model fit and the predictive ability of regression model were greatest in the full regression model incorporating both ictal and interictal measures [R2 0.44; Area under the receiver operating characteristic (ROC) curve: 0.81], slightly smaller in the reduced model incorporating ictal but not interictal measures (R2 0.40; Area under the ROC curve: 0.79) and slightly smaller again in the reduced model incorporating interictal but not ictal measures (R2 0.27; Area under the ROC curve: 0.77). Seizure onset zone and interictal spike frequency measures on subdural ECoG recording may both be useful in predicting the long-term seizure outcome of epilepsy surgery. Yet, the additive clinical impact of interictal spike frequency measures to predict long-term surgical outcome may be modest in the presence of ictal ECoG and neuroimaging data. PMID:19286694

QRS analysis using wavelet transformation for the prediction of response to cardiac resynchronization therapy: a prospective pilot study.

PubMed

Vassilikos, Vassilios P; Mantziari, Lilian; Dakos, Georgios; Kamperidis, Vasileios; Chouvarda, Ioanna; Chatzizisis, Yiannis S; Kalpidis, Panagiotis; Theofilogiannakos, Efstratios; Paraskevaidis, Stelios; Karvounis, Haralambos; Mochlas, Sotirios; Maglaveras, Nikolaos; Styliadis, Ioannis H

2014-01-01

Wider QRS and left bundle branch block morphology are related to response to cardiac resynchronization therapy (CRT). A novel time-frequency analysis of the QRS complex may provide additional information in predicting response to CRT. Signal-averaged electrocardiograms were prospectively recorded, before CRT, in orthogonal leads and QRS decomposition in three frequency bands was performed using the Morlet wavelet transformation. Thirty eight patients (age 65±10years, 31 males) were studied. CRT responders (n=28) had wider baseline QRS compared to non-responders and lower QRS energies in all frequency bands. The combination of QRS duration and mean energy in the high frequency band had the best predicting ability (AUC 0.833, 95%CI 0.705-0.962, p=0.002) followed by the maximum energy in the high frequency band (AUC 0.811, 95%CI 0.663-0.960, p=0.004). Wavelet transformation of the QRS complex is useful in predicting response to CRT. © 2013.

Spatial working memory capacity predicts bias in estimates of location.

PubMed

Crawford, L Elizabeth; Landy, David; Salthouse, Timothy A

2016-09-01

Spatial memory research has attributed systematic bias in location estimates to a combination of a noisy memory trace with a prior structure that people impose on the space. Little is known about intraindividual stability and interindividual variation in these patterns of bias. In the current work, we align recent empirical and theoretical work on working memory capacity limits and spatial memory bias to generate the prediction that those with lower working memory capacity will show greater bias in memory of the location of a single item. Reanalyzing data from a large study of cognitive aging, we find support for this prediction. Fitting separate models to individuals' data revealed a surprising variety of strategies. Some were consistent with Bayesian models of spatial category use, however roughly half of participants biased estimates outward in a way not predicted by current models and others seemed to combine these strategies. These analyses highlight the importance of studying individuals when developing general models of cognition. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Predicting ICU mortality: a comparison of stationary and nonstationary temporal models.

PubMed Central

Kayaalp, M.; Cooper, G. F.; Clermont, G.

2000-01-01

OBJECTIVE: This study evaluates the effectiveness of the stationarity assumption in predicting the mortality of intensive care unit (ICU) patients at the ICU discharge. DESIGN: This is a comparative study. A stationary temporal Bayesian network learned from data was compared to a set of (33) nonstationary temporal Bayesian networks learned from data. A process observed as a sequence of events is stationary if its stochastic properties stay the same when the sequence is shifted in a positive or negative direction by a constant time parameter. The temporal Bayesian networks forecast mortalities of patients, where each patient has one record per day. The predictive performance of the stationary model is compared with nonstationary models using the area under the receiver operating characteristics (ROC) curves. RESULTS: The stationary model usually performed best. However, one nonstationary model using large data sets performed significantly better than the stationary model. CONCLUSION: Results suggest that using a combination of stationary and nonstationary models may predict better than using either alone. PMID:11079917

Mothers' depressive symptoms and children's cognitive and social agency: Predicting first-grade cognitive functioning.

PubMed

Yan, Ni; Dix, Theodore

2016-08-01

Using data from the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (N = 1,364), the present study supports an agentic perspective; it demonstrates that mothers' depressive symptoms in infancy predict children's poor first-grade cognitive functioning because depressive symptoms predict children's low social and cognitive agency-low motivation to initiate social interaction and actively engage in activities. When mothers' depressive symptoms were high in infancy, children displayed poor first-grade cognitive functioning due to (a) tendencies to become socially withdrawn by 36 months and low in mastery motivation by 54 months and (b) tendencies for children's low agency to predict declines in mothers' sensitivity and cognitive stimulation. Findings suggest that mothers' depressive symptoms undermine cognitive development through bidirectional processes centered on children's low motivation to engage in social interaction and initiate and persist at everyday tasks. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Validation of the Framingham general cardiovascular risk score in a multiethnic Asian population: a retrospective cohort study

PubMed Central

Gray, Sarah Yu Weng; Ching, Siew Mooi; Lim, Hooi Min; Chinna, Karuthan

2015-01-01

Objective This study aims to examine the validity of the Framingham general cardiovascular disease (CVD) risk chart in a primary care setting. Design This is a 10-year retrospective cohort study. Setting A primary care clinic in a teaching hospital in Malaysia. Participants 967 patients’ records were randomly selected from patients who were attending follow-up in the clinic. Main outcome measures Baseline demographic data, history of diabetes and smoking, blood pressure (BP), and serum lipids were captured from patient records in 1998. Each patient's Framingham CVD score was computed from these parameters. All atherosclerotic CVD events occurring between 1998 and 2007 were counted. Results In 1998, mean age was 57 years with 33.8% men, 6.1% smokers, 43.3% diabetics and 59.7% hypertensive. Median BP was 140/80 mm Hg and total cholesterol 6.0 mmol/L (1.3). The predicted median Framingham general CVD risk score for the study population was 21.5% (IQR 1.2–30.0) while the actual CVD events that occurred in the 10 years was 13.1% (127/967). The median CVD points for men was 30.0, giving them a CVD risk of more than 30%; for women it is 18.5, a CVD risk of 21.5%. Our study found that the Framingham general CVD risk score to have moderate discrimination with an area under the receiver operating characteristic curve (AUC) of 0.63. It also discriminates well for Malay (AUC 0.65, p=0.01), Chinese (AUC 0.60, p=0.03), and Indians (AUC 0.65, p=0.001). There was good calibration with Hosmer-Lemeshow test χ2=3.25, p=0.78. Conclusions Taking into account that this cohort of patients were already on treatment, the Framingham General CVD Risk Prediction Score predicts fairly accurately for men and overestimates somewhat for women. In the absence of local risk prediction charts, the Framingham general CVD risk prediction chart is a reasonable alternative for use in a multiethnic group in a primary care setting. PMID:25991451

HDSC/OWP

Science.gov Websites

Maximum Precipitation Documents Miscellaneous Publications Storm Analysis Record Precipitation Contact Us ; - Probability analysis for selected historical storm events learn more > - Record point precipitation for the Oceanic and Atmospheric Administration National Weather Service Office of Water Prediction (OWP) 1325 East

Can Creatinine Height Index Predict Weaning and Survival Outcomes in Patients on Prolonged Mechanical Ventilation After Critical Illness?

PubMed

Datta, Debapriya; Foley, Raymond; Wu, Rong; Grady, James; Scalise, Paul

2018-02-01

Malnutrition is common in chronic critically ill patients on prolonged mechanical ventilation (PMV) and may affect weaning. The creatinine height index (CHI), which reflects lean muscle mass, is regarded as the most accurate indicator of malnutrition. The objective of this study was to determine the impact of CHI in comparison with other traditional nutritional indices on successful weaning and survival in patients on PMV after critical illness. Records of 167 patients on PMV following critical illness, admitted for weaning, were reviewed. Parameters studied included age, gender, body mass index (BMI), percentage ideal body weight (%IBW), total protein, albumin, prealbumin, hemoglobin (Hb), and cause of respiratory failure. Number successfully weaned and number discharged alive and time to wean and time to discharge alive were determined from records. The CHI was calculated from 24-hour urine creatinine using a standard formula. Unpaired 2-sample t test was performed to determine the association between the studied nutritional parameters and outcomes. Predictive value of studied parameters for successful weaning and survival was determined by multivariate logistic regression analysis to model dichotomous outcome of successful weaning and survival. Mean age was 68 ± 14 years, 49% were males, 64% were successfully weaned, and 65.8% survived. Total protein, Hb, and CHI had a significant impact on successful weaning. Weight, %IBW, BMI, and CHI had a significant effect on survival. Of all parameters, CHI was most strongly predictive of successful weaning and survival. The CHI is a strong predictor of successful weaning and survival in patients on PMV.

Study of the Factors Leading to Fetal and Neonatal Dysthyroidism in Children of Patients With Graves Disease

PubMed Central

Estellat, Candice; Biran, Valerie; Desfrere, Luc; Champion, Valerie; Benachi, Alexandra; Ville, Yves; Dommergues, Marc; Jarreau, Pierre-Henri; Mokhtari, Mostafa; Boithias, Claire; Brioude, Frederic; Mandelbrot, Laurent; Ceccaldi, Pierre-François; Mitanchez, Delphine; Polak, Michel; Luton, Dominique

2017-01-01

Context: Neonatal hyperthyroidism was first described in 1912 and in 1964 was shown to be linked to transplacental passage of maternal antibodies. Few multicenter studies have described the perinatal factors leading to fetal and neonatal dysthyroidism. Objective: To show how fetal dysthyroidism (FD) and neonatal dysthyroidism (ND) can be predicted from perinatal variables, in particular, the levels of anti-thyrotropin receptor antibodies (TRAbs) circulating in the mother and child. Design and Patients: This was a retrospective multicenter study of data from the medical records of all patients monitored for pregnancy from 2007 to 2014. Setting: Among 280,000 births, the medical records of 2288 women with thyroid dysfunction were selected and screened, and 417 women with Graves disease and positive for TRAbs during pregnancy were included. Results: Using the maternal TRAb levels, the cutoff value of 2.5 IU/L best predicted for FD, with a sensitivity of 100% and specificity of 64%. Using the newborn TRAb levels, the cutoff value of 6.8 IU/L best predicted for ND, with a sensitivity of 100% and a specificity of 94%. In our study, 65% of women with a history of Graves disease did not receive antithyroid drugs during pregnancy but still had infants at risk of ND. Conclusions: In pregnant women with TRAb levels ≥2.5 IU/L, fetal ultrasound monitoring is essential until delivery. All newborns with TRAb levels ≥6.8 IU/L should be examined by a pediatrician with special attention for thyroid dysfunction and treated, if necessary. PMID:29130077

On the predictive ability of mechanistic models for the Haitian cholera epidemic.

PubMed

Mari, Lorenzo; Bertuzzo, Enrico; Finger, Flavio; Casagrandi, Renato; Gatto, Marino; Rinaldo, Andrea

2015-03-06

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Adolescents' implicit theories predict desire for vengeance after peer conflicts: correlational and experimental evidence.

PubMed

Yeager, David S; Trzesniewski, Kali H; Tirri, Kirsi; Nokelainen, Petri; Dweck, Carol S

2011-07-01

Why do some adolescents respond to interpersonal conflicts vengefully, whereas others seek more positive solutions? Three studies investigated the role of implicit theories of personality in predicting violent or vengeful responses to peer conflicts among adolescents in Grades 9 and 10. They showed that a greater belief that traits are fixed (an entity theory) predicted a stronger desire for revenge after a variety of recalled peer conflicts (Study 1) and after a hypothetical conflict that specifically involved bullying (Study 2). Study 3 experimentally induced a belief in the potential for change (an incremental theory), which resulted in a reduced desire to seek revenge. This effect was mediated by changes in bad-person attributions about the perpetrators, feelings of shame and hatred, and the belief that vengeful ideation is an effective emotion-regulation strategy. Together, the findings illuminate the social-cognitive processes underlying reactions to conflict and suggest potential avenues for reducing violent retaliation in adolescents. PsycINFO Database Record (c) 2011 APA, all rights reserved

Using weather data to determine dry and wet periods relative to ethnographic records

NASA Astrophysics Data System (ADS)

Felzer, B. S.; Jiang, M.; Cheng, R.; Ember, C. R.

2017-12-01

Ethnographers record flood or drought events that affect a society's food supply and can be interpreted in terms of a society's ability to adapt to extreme events. Using daily weather station data from the Global Historical Climatology Network for wet events, and monthly gridded climatic data from the Climatic Research Unit for drought events, we determine if it is possible to relate these measured data to the ethnographic records. We explore several drought and wetness indices based on temperature and precipitation, as well as the Colwell method to determine the predictability, seasonality, and variability of these extreme indices. Initial results indicate that while it is possible to capture the events recorded in the ethnographic records, there are many more "false" captures of events that are not recorded in these records. Although extreme precipitation is a poor indicator of floods due to antecedent moisture conditions, even using streamflow for selected sites produces false captures. Relating drought indices to actual food supply as measured in crop yield only related to minimum crop yield in half the cases. Further mismatches between extreme precipitation and drought indices and ethnographic records may relate to the fact that only extreme events that affect food supply are recorded in the ethnographic records or that not all events are recorded by the ethnographers. We will present new results on how predictability measures relate to the ethnographic disasters. Despite the highlighted technical challenges, our results provide a historic perspective linking environmental stressors with socio-economic impacts, which in turn, will underpin the current efforts of risk assessment in a changing environment.

Individual differences in the recognition of facial expressions: an event-related potentials study.

PubMed

Tamamiya, Yoshiyuki; Hiraki, Kazuo

2013-01-01

Previous studies have shown that early posterior components of event-related potentials (ERPs) are modulated by facial expressions. The goal of the current study was to investigate individual differences in the recognition of facial expressions by examining the relationship between ERP components and the discrimination of facial expressions. Pictures of 3 facial expressions (angry, happy, and neutral) were presented to 36 young adults during ERP recording. Participants were asked to respond with a button press as soon as they recognized the expression depicted. A multiple regression analysis, where ERP components were set as predictor variables, assessed hits and reaction times in response to the facial expressions as dependent variables. The N170 amplitudes significantly predicted for accuracy of angry and happy expressions, and the N170 latencies were predictive for accuracy of neutral expressions. The P2 amplitudes significantly predicted reaction time. The P2 latencies significantly predicted reaction times only for neutral faces. These results suggest that individual differences in the recognition of facial expressions emerge from early components in visual processing.

Beyond School Records: The Value of Cognitive and Affective Engagement in Predicting Dropout and On-Time Graduation

ERIC Educational Resources Information Center

Lovelace, Matthew D.; Reschly, Amy L.; Appleton, James J.

2018-01-01

Early warning systems use school record data--such as attendance rate, behavior records, and course performance--to identify students at risk of dropping out. These are useful predictors of graduation-related outcomes, in large part because they indicate a student's level of engagement with school. However, these data do not indicate how invested…

Analysis of data on large explosive eruptions of stratovolcanoes to constrain under-recording and eruption rates

NASA Astrophysics Data System (ADS)

Rougier, Jonty; Cashman, Kathy; Sparks, Stephen

2016-04-01

We have analysed the Large Magnitude Explosive Volcanic Eruptions database (LaMEVE) for volcanoes that classify as stratovolcanoes. A non-parametric statistical approach is used to assess the global recording rate for large (M4+). The approach imposes minimal structure on the shape of the recording rate through time. We find that the recording rates have declined rapidly, going backwards in time. Prior to 1600 they are below 50%, and prior to 1100 they are below 20%. Even in the recent past, e.g. the 1800s, they are likely to be appreciably less than 100%.The assessment for very large (M5+) eruptions is more uncertain, due to the scarcity of events. Having taken under-recording into account the large-eruption rates of stratovolcanoes are modelled exchangeably, in order to derive an informative prior distribution as an input into a subsequent volcano-by-volcano hazard assessment. The statistical model implies that volcano-by-volcano predictions can be grouped by the number of recorded large eruptions. Further, it is possible to combine all volcanoes together into a global large eruption prediction, with an M4+ rate computed from the LaMEVE database of 0.57/yr.

Estimation of flood-frequency characteristics of small urban streams in North Carolina

USGS Publications Warehouse

Robbins, J.C.; Pope, B.F.

1996-01-01

A statewide study was conducted to develop methods for estimating the magnitude and frequency of floods of small urban streams in North Carolina. This type of information is critical in the design of bridges, culverts and water-control structures, establishment of flood-insurance rates and flood-plain regulation, and for other uses by urban planners and engineers. Concurrent records of rainfall and runoff data collected in small urban basins were used to calibrate rainfall-runoff models. Historic rain- fall records were used with the calibrated models to synthesize a long- term record of annual peak discharges. The synthesized record of annual peak discharges were used in a statistical analysis to determine flood- frequency distributions. These frequency distributions were used with distributions from previous investigations to develop a database for 32 small urban basins in the Blue Ridge-Piedmont, Sand Hills, and Coastal Plain hydrologic areas. The study basins ranged in size from 0.04 to 41.0 square miles. Data describing the size and shape of the basin, level of urban development, and climate and rural flood charac- teristics also were included in the database. Estimation equations were developed by relating flood-frequency char- acteristics to basin characteristics in a generalized least-squares regression analysis. The most significant basin characteristics are drainage area, impervious area, and rural flood discharge. The model error and prediction errors for the estimating equations were less than those for the national flood-frequency equations previously reported. Resulting equations, which have prediction errors generally less than 40 percent, can be used to estimate flood-peak discharges for 2-, 5-, 10-, 25-, 50-, and 100-year recurrence intervals for small urban basins across the State assuming negligible, sustainable, in- channel detention or basin storage.

Sensitivity of Claims-Based Algorithms to Ascertain Smoking Status More Than Doubled with Meaningful Use.

PubMed

Huo, Jinhai; Yang, Ming; Tina Shih, Ya-Chen

2018-03-01

The "meaningful use of certified electronic health record" policy requires eligible professionals to record smoking status for more than 50% of all individuals aged 13 years or older in 2011 to 2012. To explore whether the coding to document smoking behavior has increased over time and to assess the accuracy of smoking-related diagnosis and procedure codes in identifying previous and current smokers. We conducted an observational study with 5,423,880 enrollees from the year 2009 to 2014 in the Truven Health Analytics database. Temporal trends of smoking coding, sensitivity, specificity, positive predictive value, and negative predictive value were measured. The rate of coding of smoking behavior improved significantly by the end of the study period. The proportion of patients in the claims data recorded as current smokers increased 2.3-fold and the proportion of patients recorded as previous smokers increased 4-fold during the 6-year period. The sensitivity of each International Classification of Diseases, Ninth Revision, Clinical Modification code was generally less than 10%. The diagnosis code of tobacco use disorder (305.1X) was the most sensitive code (9.3%) for identifying smokers. The specificities of these codes and the Current Procedural Terminology codes were all more than 98%. A large improvement in the coding of current and previous smoking behavior has occurred since the inception of the meaningful use policy. Nevertheless, the use of diagnosis and procedure codes to identify smoking behavior in administrative data is still unreliable. This suggests that quality improvements toward medical coding on smoking behavior are needed to enhance the capability of claims data for smoking-related outcomes research. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Combinations of early signs of critical illness predict in-hospital death-the SOCCER study (signs of critical conditions and emergency responses).

PubMed

Harrison, Gordon A; Jacques, Theresa; McLaws, Mary-Louise; Kilborn, Gabrielle

2006-12-01

Medical emergency team (MET) call criteria are late signs of a deteriorating clinical condition. Some early signs predict in-hospital death but have a high prevalence so their use as single sign call criteria could be wasteful of resources. This study searched a large database to explore the association of combinations of recordings of early signs (ES), or early with late signs (LS) with in-hospital death. A cross-sectional survey was undertaken of 3046 non-do not attempt resuscitation adult admissions in 5 hospitals without MET over 14 days. The medical records were reviewed for recordings of 26 ES and 21 LS and in-hospital death. Combinations of ES with or without LS were examined as predictors of death. Global modified early warning scores (GMEWS) were calculated. ES with LS, plus LS only, had higher odd ratios than ES alone. Four combinations of ES were strongly associated with death: cardiovascular plus respiratory with decrease in urinary output, cardiovascular plus respiratory with a decrease in consciousness, respiratory with decrease in urinary output, and cardiovascular plus respiratory. In other combinations, recordings of SpO2 90-95%, systolic blood pressure 80-100 mmHg or decrease in urinary output in turn occurring with one or more disturbed blood gas variable were associated with death. Compared with admissions whose GMEWS were 0-2, admissions with GMEWS 5-15 were 27.1 times more likely to die while those with GMEWS 3-4 were 6.5 times more likely. The results support the inclusion of early signs of a deteriorating clinical condition in sets of call criteria.

Lameness Prevalence and Risk Factors in Large Dairy Farms in Upstate New York. Model Development for the Prediction of Claw Horn Disruption Lesions

PubMed Central

Foditsch, Carla; Oikonomou, Georgios; Machado, Vinícius Silva; Bicalho, Marcela Luccas; Ganda, Erika Korzune; Lima, Svetlana Ferreira; Rossi, Rodolfo; Ribeiro, Bruno Leonardo; Kussler, Arieli; Bicalho, Rodrigo Carvalho

2016-01-01

The main objectives of this prospective cohort study were a) to describe lameness prevalence at drying off in large high producing New York State herds based on visual locomotion score (VLS) and identify potential cow and herd level risk factors, and b) to develop a model that will predict the probability of a cow developing claw horn disruption lesions (CHDL) in the subsequent lactation using cow level variables collected at drying off and/or available from farm management software. Data were collected from 23 large commercial dairy farms located in upstate New York. A total of 7,687 dry cows, that were less than 265 days in gestation, were enrolled in the study. Farms were visited between May 2012 and March 2013, and cows were assessed for body condition score (BCS) and VLS. Data on the CHDL events recorded by the farm employees were extracted from the Dairy-Comp 305 database, as well as information regarding the studied cows’ health events, milk production, and reproductive records throughout the previous and subsequent lactation period. Univariable analyses and mixed multivariable logistic regression models were used to analyse the data at the cow level. The overall average prevalence of lameness (VLS > 2) at drying off was 14%. Lactation group, previous CHDL, mature equivalent 305-d milk yield (ME305), season, BCS at drying off and sire PTA for strength were all significantly associated with lameness at the drying off (cow-level). Lameness at drying off was associated with CHDL incidence in the subsequent lactation, as well as lactation group, previous CHDL and ME305. These risk factors for CHDL in the subsequent lactation were included in our predictive model and adjusted predicted probabilities for CHDL were calculated for all studied cows. ROC analysis identified an optimum cut-off point for these probabilities and using this cut-off point we could predict CHDL incidence in the subsequent lactation with an overall specificity of 75% and sensitivity of 59%. Using this approach, we would have detected 33% of the studied population as being at risk, eventually identifying 59% of future CHDL cases. Our predictive model could help dairy producers focusing their efforts on CHDL reduction by implementing aggressive preventive measures for high risk cows. PMID:26795970

Modified obstetric early warning scoring systems (MOEWS): validating the diagnostic performance for severe sepsis in women with chorioamnionitis.

PubMed

Edwards, Sian E; Grobman, William A; Lappen, Justin R; Winter, Cathy; Fox, Robert; Lenguerrand, Erik; Draycott, Timothy

2015-04-01

We sought to compare the predictive power of published modified obstetric early warning scoring systems (MOEWS) for the development of severe sepsis in women with chorioamnionitis. This was a retrospective cohort study using prospectively collected clinical observations at a single tertiary unit (Chicago, IL). Hospital databases and patient records were searched to identify and verify cases with clinically diagnosed chorioamnionitis during the study period (June 2006 through November 2007). Vital sign data (heart rate, respiratory rate, blood pressure, temperature, mental state) for these cases were extracted from an electronic database and the single worst composite recording was identified for analysis. Global literature databases were searched (2014) to identify examples of MOEWS. Scores for each identified MOEWS were derived from each set of vital sign recordings during the presentation with chorioamnionitis. The performance of these MOEWS (the primary outcome) was then analyzed and compared using their sensitivity, specificity, positive and negative predictive values, and receiver-operating characteristic curve for severe sepsis. Six MOEWS were identified. There was wide variation in design and pathophysiological thresholds used for clinical alerts. In all, 913 women with chorioamnionitis were identified from the clinical database. In all, 364 cases with complete data for all physiological indicators were included in analysis. Five women developed severe sepsis, including 1 woman who died. The sensitivities of the MOEWS in predicting the severe deterioration ranged from 40-100% and the specificities varied even more ranging from 4-97%. The positive predictive values were low for all MOEWS ranging from <2-15%. The MOEWS with simpler designs tended to be more sensitive, whereas the more complex MOEWS were more specific, but failed to identify some of the women who developed severe sepsis. Currently used MOEWS vary widely in terms of alert thresholds, format, and accuracy. Most MOEWS have not been validated. The MOEWS generally performed poorly in predicting severe sepsis in obstetric patients; in general severe sepsis was overdetected. Simple MOEWS with high sensitivity followed with more specific secondary testing is likely to be the best way forward. Further research is required to develop early warning systems for use in this setting. Copyright © 2015 Elsevier Inc. All rights reserved.

Exploring the Potential Relationship between Eye Gaze and English L2 Speakers' Responses to Recasts

ERIC Educational Resources Information Center

McDonough, Kim; Crowther, Dustin; Kielstra, Paula; Trofimovich, Pavel

2015-01-01

This exploratory study investigated whether joint attention through eye gaze was predictive of second language (L2) speakers' responses to recasts. L2 English learners (N = 20) carried out communicative tasks with research assistants who provided feedback in response to non-targetlike (non-TL) forms. Their interaction was audio-recorded and their…

The Dynamics of Narrative Writing in Primary Grade Children: Writing Process Factors Predict Story Quality

ERIC Educational Resources Information Center

von Koss Torkildsen, Janne; Morken, Frøydis; Helland, Wenche A.; Helland, Turid

2016-01-01

In this study of third grade school children, we investigated the association between writing process measures recorded with key stroke logging and the final written product. Moreover, we examined the cognitive predictors of writing process and product measures. Analyses of key strokes showed that while most children spontaneously made local…

A Comparison Between the Effects of Hexamethonium and Atropine in Combination and of Vagotomy with Gastrojejunostomy on Human Gastric Secretion

PubMed Central

McArthur, J.; Tankel, H. I.; Kay, A. W.

1960-01-01

This study records the gastric secretory response, using the Kay augmented histamine test, and compares “medical” vagotomy with atropine and hexamethonium with “surgical” vagotomy. The results suggest that it may be of value in predicting the effect of vagotomy with gastrojejunostomy. PMID:13773727

Predicting Dispositions toward Inclusion of Students with Disabilities: The Role of Conservative Ideology and Discomfort with Disability

ERIC Educational Resources Information Center

Brandes, Joyce A.; Crowson, H. Michael

2009-01-01

Within the published empirical record, a limited number of investigations exist that study the association between socio-political ideologies of preservice teachers and their attitudes toward disability-related matters within schools. To the extent that individual socio-political ideology and discomfort with disability remain mostly unexplored,…

Do guilt- and shame-proneness differentially predict prosocial, aggressive, and withdrawn behaviors during early adolescence?

PubMed

Roos, Sanna; Hodges, Ernest V E; Salmivalli, Christina

2014-03-01

In this short-term longitudinal study, we systematically examined the distinctiveness of guilt- and shame-proneness in early adolescents (N = 395, mean age = 11.8 years) in terms of differential relations with peer reported prosocial behavior, withdrawal, and aggression. Results from structural equation modeling indicated that guilt-proneness concurrently predicted more aggressive and less prosocial behavior as well as subsequent increases in prosocial behavior. Shame-proneness predicted subsequent decreases in prosocial behavior. Although girls reported a greater proneness to experience guilt and shame than boys, the associations between the two dispositional emotions and social behaviors were found to be similar across time and gender. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Childhood Peer Status and Adult Susceptibility to Anxiety and Depression. A 30-Year Hospital Follow-Up

ERIC Educational Resources Information Center

Modin, Bitte; Ostberg, Viveca; Almquist, Ylva

2011-01-01

This study examined the extent to which sixth grade peer status could predict anxiety and/or depression in 5,242 women and 5,004 men who were born in 1953 and whose hospital records were followed up from 1973-2003. The data used was the Stockholm Birth Cohort Study. While no association could be established for men, results indicated that women…

A 15,000 year record of vegetation and climate change from a treeline lake in the Rocky Mountains, Wyoming, USA

Treesearch

Scott A. Mensing; John L. Korfmacher; Thomas Minckley; Robert C. Musselman

2012-01-01

Future climate projections predict warming at high elevations that will impact treeline species, but complex topographic relief in mountains complicates ecologic response, and we have a limited number of long-term studies examining vegetation change related to climate. In this study, pollen and conifer stomata were analyzed from a 2.3 m sediment core extending to 15,...

A Study in a New Test Facility on Indoor Annoyance Caused by Sonic Booms

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Loubeau, Alexandra; Klos, Jacob

2012-01-01

A sonic-boom simulator at NASA Langley Research Center has been constructed to research the indoor human response to low-amplitude sonic booms. The research goal is the development of a psychoacoustic model for individual sonic booms to be validated by future community studies. The study in this report assessed the suitability of existing noise metrics for predicting indoor human annoyance. The test signals included a wide range of synthesized and recorded sonic-boom waveforms. Results indicated that no noise metric predicts indoor annoyance to sonic-boom sounds better than Perceived Level, PL. During the study it became apparent that structural vibrations induced by the test signals were contributing to annoyance, so the relationship between sound and vibration at levels of equivalent annoyance has been quantified.

A prospective analysis of physical examination findings in the diagnosis of facial fractures: Determining predictive value.

PubMed

Timashpolsky, Alisa; Dagum, Alexander B; Sayeed, Syed M; Romeiser, Jamie L; Rosenfeld, Elisheva A; Conkling, Nicole

2016-01-01

There are >150,000 patient visits per year to emergency rooms for facial trauma. The reliability of a computed tomography (CT) scan has made it the primary modality for diagnosing facial skeletal injury, with the physical examination playing more a cursory role. Knowing the predictive value of physical findings in facial skeletal injuries may enable more appropriate use of imaging and health care resources. A blinded prospective study was undertaken to assess the predictive value of physical examination findings in detecting maxillofacial fracture in trauma patients, and in determining whether a patient will require surgical intervention. Over a four-month period, the authors' team examined patients admitted with facial trauma to the emergency department of their hospital. The evaluating physician completed a standardized physical examination evaluation form indicating the physical findings. Corresponding CT scans and surgical records were then reviewed, and the results recorded by a plastic surgeon who was blinded to the results of the physical examination. A total of 57 patients met the inclusion criteria; there were 44 male and 13 female patients. The sensitivity, specificity, positive predictive value and negative predictive value of grouped physical examination findings were determined in major areas. In further analysis, specific examination findings with n≥9 (15%) were also reported. The data demonstrated a high negative predictive value of at least 90% for orbital floor, zygomatic, mandibular and nasal bone fractures compared with CT scan. Furthermore, none of the patients who did not have a physical examination finding for a particular facial fracture required surgery for that fracture. Thus, the instrument performed well at ruling out fractures in these areas when there were none. Ultimately, these results may help reduce unnecessary radiation and costly imaging in patients with facial trauma without facial fractures.

Neonatal Candidiasis: Epidemiology, Risk Factors, and Clinical Judgment

PubMed Central

Benjamin, Daniel K.; Stoll, Barbara J.; Gantz, Marie G.; Walsh, Michele C.; Sanchez, Pablo J.; Das, Abhik; Shankaran, Seetha; Higgins, Rosemary D.; Auten, Kathy J.; Miller, Nancy A.; Walsh, Thomas J.; Laptook, Abbot R.; Carlo, Waldemar A.; Kennedy, Kathleen A.; Finer, Neil N.; Duara, Shahnaz; Schibler, Kurt; Chapman, Rachel L.; Van Meurs, Krisa P.; Frantz, Ivan D.; Phelps, Dale L.; Poindexter, Brenda B.; Bell, Edward F.; O’Shea, T. Michael; Watterberg, Kristi L.; Goldberg, Ronald N.

2011-01-01

OBJECTIVE Invasive candidiasis is a leading cause of infection-related morbidity and mortality in extremely low-birth-weight (<1000 g) infants. We quantify risk factors predicting infection in high-risk premature infants and compare clinical judgment with a prediction model of invasive candidiasis. METHODS The study involved a prospective observational cohort of infants <1000 g birth weight at 19 centers of the NICHD Neonatal Research Network. At each sepsis evaluation, clinical information was recorded, cultures obtained, and clinicians prospectively recorded their estimate of the probability of invasive candidiasis. Two models were generated with invasive candidiasis as their outcome: 1) potentially modifiable risk factors and 2) a clinical model at time of blood culture to predict candidiasis. RESULTS Invasive candidiasis occurred in 137/1515 (9.0%) infants and was documented by positive culture from ≥ 1 of these sources: blood (n=96), cerebrospinal fluid (n=9), urine obtained by catheterization (n=52), or other sterile body fluid (n=10). Mortality was not different from infants who had positive blood culture compared to those with isolated positive urine culture. Incidence varied from 2–28% at the 13 centers enrolling ≥ 50 infants. Potentially modifiable risk factors (model 1) included central catheter, broad-spectrum antibiotics (e.g., third-generation cephalosporins), intravenous lipid emulsion, endotracheal tube, and antenatal antibiotics. The clinical prediction model (model 2) had an area under the receiver operating characteristic curve of 0.79, and was superior to clinician judgment (0.70) in predicting subsequent invasive candidiasis. Performance of clinical judgment did not vary significantly with level of training. CONCLUSION Prior antibiotics, presence of a central catheter, endotracheal tube, and center were strongly associated with invasive candidiasis. Modeling was more accurate in predicting invasive candidiasis than clinical judgment. PMID:20876174

A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

PubMed

Sideris, Costas; Alshurafa, Nabil; Pourhomayoun, Mohammad; Shahmohammadi, Farhad; Samy, Lauren; Sarrafzadeh, Majid

2015-01-01

In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies. We then utilize these clusters as features to predict patient severity of condition. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 patients. We compare our cluster-based feature set with another that incorporates the Charlson comorbidity score as a feature and demonstrate an accuracy improvement of up to 14% in the predictability of the severity of condition.

Directivity in NGA earthquake ground motions: Analysis using isochrone theory

USGS Publications Warehouse

Spudich, P.; Chiou, B.S.J.

2008-01-01

We present correction factors that may be applied to the ground motion prediction relations of Abrahamson and Silva, Boore and Atkinson, Campbell and Bozorgnia, and Chiou and Youngs (all in this volume) to model the azimuthally varying distribution of the GMRotI50 component of ground motion (commonly called 'directivity') around earthquakes. Our correction factors may be used for planar or nonplanar faults having any dip or slip rake (faulting mechanism). Our correction factors predict directivity-induced variations of spectral acceleration that are roughly half of the strike-slip variations predicted by Somerville et al. (1997), and use of our factors reduces record-to-record sigma by about 2-20% at 5 sec or greater period. ?? 2008, Earthquake Engineering Research Institute.

Neurophysiological prediction of neurological good and poor outcome in post-anoxic coma.

PubMed

Grippo, A; Carrai, R; Scarpino, M; Spalletti, M; Lanzo, G; Cossu, C; Peris, A; Valente, S; Amantini, A

2017-06-01

Investigation of the utility of association between electroencephalogram (EEG) and somatosensory-evoked potentials (SEPs) for the prediction of neurological outcome in comatose patients resuscitated after cardiac arrest (CA) treated with therapeutic hypothermia, according to different recording times after CA. Glasgow Coma Scale, EEG and SEPs performed at 12, 24 and 48-72 h after CA were assessed in 200 patients. Outcome was evaluated by Cerebral Performance Category 6 months after CA. Within 12 h after CA, grade 1 EEG predicted good outcome and bilaterally absent (BA) SEPs predicted poor outcome. Because grade 1 EEG and BA-SEPs were never found in the same patient, the recording of both EEG and SEPs allows us to correctly prognosticate a greater number of patients with respect to the use of a single test within 12 h after CA. At 48-72 h after CA, both grade 2 EEG and BA-SEPs predicted poor outcome with FPR=0.0%. When these neurophysiological patterns are both present in the same patient, they confirm and strengthen their prognostic value, but because they also occurred independently in eight patients, poor outcome is predictable in a greater number of patients. The combination of EEG/SEP findings allows prediction of good and poor outcome (within 12 h after CA) and of poor outcome (after 48-72 h). Recording of EEG and SEPs in the same patients allows always an increase in the number of cases correctly classified, and an increase of the reliability of prognostication in a single patient due to concordance of patterns. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

When Preferences Are in the Way: Children's Predictions of Goal-Directed Behaviors.

PubMed

Yang, Fan; Frye, Douglas

2017-12-18

Across three studies, we examined 4- to 7-year-olds' predictions of goal-directed behaviors when goals conflict with preferences. In Study 1, when presented with stories in which a character had to act against basic preferences to achieve an interpersonal goal (e.g., playing with a partner), 6- and 7-year-olds were more likely than 4- and 5-year-olds to predict the actor would act in accordance with the goal to play with the partner, instead of fulfilling the basic preference of playing a favored activity. Similar results were obtained in Study 2 with scenarios that each involved a single individual pursuing intrapersonal goals that conflicted with his or her basic preferences. In Study 3, younger children's predictions of goal-directed behaviors did not increase for novel goals and preferences, when the influences of their own preferences, future thinking, or a lack of impulse control were minimized. The results suggest that between ages 4 and 7, children increasingly integrate and give more weight to other sources of motivational information (e.g., goals) in addition to preferences when predicting people's behaviors. This increasing awareness may have implications for children's self-regulatory and goal pursuit behaviors. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Characterization and prediction of meandering channel migration in the GIS environment: a case study of the Sabine River in the USA.

PubMed

Heo, Joon; Duc, Trinh Anh; Cho, Hyung-Sik; Choi, Sung-Uk

2009-05-01

This study focused on the prediction of a 22 km meandering channel migration of the Sabine River between the states of Texas and Louisiana. The meander characteristics of 12 bends, identified from seven orthophotos taken between 1974 and 2004, were acquired in a GIS environment. Based on that earlier years' data acquisition, channel prediction was performed for the two years 1996 and 2004 using least squares estimation and linear extrapolations, yielding a satisfactory agreement with the observations (the median predicted and observed migration rates were 3.1 and 3.6 [m/year], respectively). The best-predicted migration rate was found to be associated with the longest orthophoto-recorded interval. The study confirmed that channel migration is strongly correlated with bend curvature and that the maximum migration rate of the bend corresponded to a radius of curvature [bend radius (R(C))/channel width (W(C))] of 2.5. In tight bends of a smaller radius of curvature than 1.6, secondary flow scouring near the bend apex increases bend curvature. The stability index of the dimensionless bend radius was determined to be 2.45. Overall, this study proves the effectiveness of least squares estimation with historical orthophotography for characterization of meandering channel migration.

Analysis of cardiovascular oscillations: A new approach to the early prediction of pre-eclampsia

NASA Astrophysics Data System (ADS)

Malberg, H.; Bauernschmitt, R.; Voss, A.; Walther, T.; Faber, R.; Stepan, H.; Wessel, N.

2007-03-01

Pre-eclampsia (PE) is a serious disorder with high morbidity and mortality occurring during pregnancy; 3%-5% of all pregnant women are affected. Early prediction is still insufficient in clinical practice. Although most pre-eclamptic patients show pathological uterine perfusion in the second trimester, this parameter has a positive predictive accuracy of only 30%, which makes it unsuitable for early, reliable prediction. The study is based on the hypothesis that alterations in cardiovascular regulatory behavior can be used to predict PE. Ninety-six pregnant women in whom Doppler investigation detected perfusion disorders of the uterine arteries were included in the study. Twenty-four of these pregnant women developed PE after the 30th week of gestation. During pregnancy, additional several noninvasive continuous blood pressure recordings were made over 30 min under resting conditions by means of a finger cuff. The time series extracted of systolic as well as diastolic beat-to-beat pressures and the heart rate were studied by variability and coupling analysis to find predictive factors preceding genesis of the disease. In the period between the 18th and 26th weeks of pregnancy, three special variability and baroreflex parameters were able to predict PE several weeks before clinical manifestation. Discriminant function analysis of these parameters was able to predict PE with a sensitivity and specificity of 87.5% and a positive predictive value of 70%. The combined clinical assessment of uterine perfusion and cardiovascular variability demonstrates the best current prediction several weeks before clinical manifestation of PE.

Rheumatoid arthritis patient perceptions on the value of predictive testing for treatments: a qualitative study.

PubMed

Kumar, Kanta; Peters, Sarah; Barton, Anne

2016-11-08

Rheumatoid arthritis (RA) is a long term condition that requires early treatment to control symptoms and improve long-term outcomes. Lack of response to RA treatments is not only a waste of healthcare resources, but also causes disability and distress to patients. Identifying biomarkers predictive of treatment response offers an opportunity to improve clinical decisions about which treatment to recommend in patients and could ultimately lead to better patient outcomes. The aim of this study was to explore the understanding of and factors affecting Rheumatoid Arthritis (RA) patients' decisions around predictive treatment testing. A qualitative study was conducted with a purposive sample of 16 patients with RA from three major UK cities. Four focus groups explored patient perceptions of the use of biomarker tests to predict response to treatments. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis by three researchers. Data were organised within three interlinking themes: [1] Perceptions of predictive tests and patient preference of tests; [2] Utility of the test to manage expectations; [3] The influence of the disease duration on take up of predictive testing. During consultations for predictive testing, patients felt they would need, first, careful explanations detailing the consequences of untreated RA and delayed treatment response and, second, support to balance the risks of tests, which might be invasive and/or only moderately accurate, with the potential benefits of better management of symptoms. This study provides important insights into predictive testing. Besides supporting clinical decision making, the development of predictive testing in RA is largely supported by patients. Developing strategies which communicate risk information about predictive testing effectively while reducing the psychological burden associated with this information will be essential to maximise uptake.

The role of facial appearance on CEO selection after firm misconduct.

PubMed

Gomulya, David; Wong, Elaine M; Ormiston, Margaret E; Boeker, Warren

2017-04-01

[Correction Notice: An Erratum for this article was reported in Vol 102(4) of Journal of Applied Psychology (see record 2017-10684-001). The wrong figure files were used. All versions of this article have been corrected.] We investigate a particular aspect of CEO successor trustworthiness that may be critically important after a firm has engaged in financial misconduct. Specifically, drawing on prior research that suggests that facial appearance is one critical way in which trustworthiness is signaled, we argue that leaders who convey integrity, a component of trustworthiness, will be more likely to be selected as successors after financial restatement. We predict that such appointments garner more positive reactions by external observers such as investment analysts and the media because these CEOs are perceived as having greater integrity. In an archival study of firms that have announced financial restatements, we find support for our predictions. These findings have implications for research on CEO succession, leadership selection, facial appearance, and firm misconduct. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus

NASA Astrophysics Data System (ADS)

Krusienski, D. J.; Shih, J. J.

2011-04-01

A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. In this study, event-related potentials (ERPs) recorded from bilateral stereotactic depth electrodes implanted in and adjacent to the hippocampus were used to control a P300 Speller paradigm. The ERPs were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in the two subjects tested. Our results demonstrate that ERPs from hippocampal and hippocampal adjacent depth electrodes can be used to reliably control the P300 Speller BCI paradigm.

Characteristics of infants at risk of hypoglycaemia secondary to being 'infant of a diabetic mother'.

PubMed

VanHaltren, Karen; Malhotra, Atul

2013-01-01

Infants of diabetic mothers (IDMs) are at risk of hypoglycaemia in the neonatal period. The prediction of which of these infants are at higher risk of developing hypoglycaemia is complex. To determine the characteristics of infants of diabetic mothers who are more likely to need an admission to the neonatal intensive care unit to manage their hypoglycaemia. Retrospective chart review of maternal and infant characteristics of 'at-risk' infants. Electronic patient records and neonatal and obstetric database accessed to obtain data. A total of 326 infants were identified in a study period accessible to electronic patient records. Macrosomia was present in 15% of the infants. Hypoglycaemic episodes occurred in 109 (33.4%) infants. Maternal diabetes type, HbA1c, prematurity, macrosomia, and temperature instability were identified as risk factors most commonly associated in infants who actually went on to develop hypoglycaemia. A weighted risk score to predict hypoglycaemia in this at-risk population may serve to rationalise admission to the neonatal unit and management of IDMs.

Fitting Neuron Models to Spike Trains

PubMed Central

Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

2011-01-01

Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

A stage-normalized function for the synthesis of stage-discharge relations for the Colorado River in Grand Canyon, Arizona

USGS Publications Warehouse

Wiele, Stephen M.; Torizzo, Margaret

2003-01-01

A method was developed to construct stage-discharge rating curves for the Colorado River in Grand Canyon, Arizona, using two stage-discharge pairs and a stage-normalized rating curve. Stage-discharge rating curves formulated with the stage-normalized curve method are compared to (1) stage-discharge rating curves for six temporary stage gages and two streamflow-gaging stations developed by combining stage records with modeled unsteady flow; (2) stage-discharge rating curves developed from stage records and discharge measurements at three streamflow-gaging stations; and (3) stages surveyed at known discharges at the Northern Arizona Sand Bar Studies sites. The stage-normalized curve method shows good agreement with field data when the discharges used in the construction of the rating curves are at least 200 cubic meters per second apart. Predictions of stage using the stage-normalized curve method are also compared to predictions of stage from a steady-flow model.

Examining the job search-turnover relationship: the role of embeddedness, job satisfaction, and available alternatives.

PubMed

Swider, Brian W; Boswell, Wendy R; Zimmerman, Ryan D

2011-03-01

This study examined factors that may help explain under what conditions employee job search effort may most strongly (or weakly) predict subsequent turnover. As predicted, the job search-turnover relationship was stronger when employees had lower levels of job embeddedness and job satisfaction and higher levels of available alternatives. These findings suggest that there may be a number of factors interacting to influence employees' turnover decisions, indicating greater complexity to the process than described in prominent sequential turnover models. PsycINFO Database Record (c) 2011 APA, all rights reserved.

Linked Records of Children with Traumatic Brain Injury. Probabilistic Linkage without Use of Protected Health Information.

PubMed

Bennett, T D; Dean, J M; Keenan, H T; McGlincy, M H; Thomas, A M; Cook, L J

2015-01-01

Record linkage may create powerful datasets with which investigators can conduct comparative effectiveness studies evaluating the impact of tests or interventions on health. All linkages of health care data files to date have used protected health information (PHI) in their linkage variables. A technique to link datasets without using PHI would be advantageous both to preserve privacy and to increase the number of potential linkages. We applied probabilistic linkage to records of injured children in the National Trauma Data Bank (NTDB, N = 156,357) and the Pediatric Health Information Systems (PHIS, N = 104,049) databases from 2007 to 2010. 49 match variables without PHI were used, many of them administrative variables and indicators for procedures recorded as International Classification of Diseases, 9th revision, Clinical Modification codes. We validated the accuracy of the linkage using identified data from a single center that submits to both databases. We accurately linked the PHIS and NTDB records for 69% of children with any injury, and 88% of those with severe traumatic brain injury eligible for a study of intervention effectiveness (positive predictive value of 98%, specificity of 99.99%). Accurate linkage was associated with longer lengths of stay, more severe injuries, and multiple injuries. In populations with substantial illness or injury severity, accurate record linkage may be possible in the absence of PHI. This methodology may enable linkages and, in turn, comparative effectiveness studies that would be unlikely or impossible otherwise.

Organismal climatology: analyzing environmental variability at scales relevant to physiological stress.

PubMed

Helmuth, Brian; Broitman, Bernardo R; Yamane, Lauren; Gilman, Sarah E; Mach, Katharine; Mislan, K A S; Denny, Mark W

2010-03-15

Predicting when, where and with what magnitude climate change is likely to affect the fitness, abundance and distribution of organisms and the functioning of ecosystems has emerged as a high priority for scientists and resource managers. However, even in cases where we have detailed knowledge of current species' range boundaries, we often do not understand what, if any, aspects of weather and climate act to set these limits. This shortcoming significantly curtails our capacity to predict potential future range shifts in response to climate change, especially since the factors that set range boundaries under those novel conditions may be different from those that set limits today. We quantitatively examine a nine-year time series of temperature records relevant to the body temperatures of intertidal mussels as measured using biomimetic sensors. Specifically, we explore how a 'climatology' of body temperatures, as opposed to long-term records of habitat-level parameters such as air and water temperatures, can be used to extrapolate meaningful spatial and temporal patterns of physiological stress. Using different metrics that correspond to various aspects of physiological stress (seasonal means, cumulative temperature and the return time of extremes) we show that these potential environmental stressors do not always occur in synchrony with one another. Our analysis also shows that patterns of animal temperature are not well correlated with simple, commonly used metrics such as air temperature. Detailed physiological studies can provide guidance to predicting the effects of global climate change on natural ecosystems but only if we concomitantly record, archive and model environmental signals at appropriate scales.

Predicting intraoperative feasibility of combined TES-mMEP and cSSEP monitoring during scoliosis surgery based on preoperative neurophysiological assessment.

PubMed

Azabou, Eric; Manel, Véronique; Abelin-Genevois, Kariman; Andre-Obadia, Nathalie; Cunin, Vincent; Garin, Christophe; Kohler, Remi; Berard, Jérôme; Ulkatan, Sedat

2014-07-01

Combined monitoring of muscle motor evoked potentials elicited by transcranial electric stimulation (TES-mMEP) and cortical somatosensory evoked potentials (cSSEPs) is safe and effective for spinal cord monitoring during scoliosis surgery. However, TES-mMEP/cSSEP is not always feasible. Predictors of feasibility would help to plan the monitoring strategy. To identify predictors of the feasibility of TES-mMEP/cSSEP during scoliosis surgery. Prospective cohort study in a clinical neurophysiology unit and pediatric orthopedic department of a French university hospital. A total of 103 children aged 2 to 19 years scheduled for scoliosis surgery. Feasibility rate of intraoperative TES-mMEP/cSSEP monitoring. All patients underwent a preoperative neurological evaluation and preoperative mMEP and cSSEP recordings at both legs. For each factor associated with feasibility, we computed sensitivity, specificity, positive predictive value (PPV), and negative predictive value. A decision tree was designed. Presence of any of the following factors was associated with 100% feasibility, 100% specificity, and 100% PPV: idiopathic scoliosis, normal preoperative neurological findings, and normal preoperative mMEP and cSSEP recordings. Feasibility was 0% in the eight patients with no recordable mMEPs or cSSEPs during preoperative testing. A decision tree involving three screening steps can be used to identify patients in whom intraoperative TES-mMEP/cSSEP is feasible. Preoperative neurological and neurophysiological assessments are helpful for identifying patients who can be successfully monitored by TES-mMEP/cSSEP during scoliosis surgery. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System.

PubMed

Norouzi, Jamshid; Yadollahpour, Ali; Mirbagheri, Seyed Ahmad; Mazdeh, Mitra Mahdavi; Hosseini, Seyed Ahmad

2016-01-01

Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m(2) of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.

The use of artificial neural networks to predict the muscle behavior

NASA Astrophysics Data System (ADS)

Kutilek, Patrik; Viteckova, Slavka; Svoboda, Zdenĕk; Smrcka, Pavel

2013-09-01

The aim of this article is to introduce methods of prediction of muscle behavior of the lower extremities based on artificial neural networks, which can be used for medical purposes. Our work focuses on predicting muscletendon forces and moments during human gait with the use of angle-time diagram. A group of healthy children and children with cerebral palsy were measured using a Vicon MoCap system. The kinematic data was recorded and the OpenSim software system was used to identify the joint angles, muscle-tendon forces and joint muscle moment, which are presented graphically with time diagrams. The musculus gastrocnemius medialis that is often studied in the context of cerebral palsy have been chosen to study the method of prediction. The diagrams of mean muscle-tendon force and mean moment are plotted and the data about the force-time and moment-time dependencies are used for training neural networks. The new way of prediction of muscle-tendon forces and moments based on neural networks was tested. Neural networks predicted the muscle forces and moments of healthy children and children with cerebral palsy. The designed method of prediction by neural networks could help to identify the difference between muscle behavior of healthy subjects and diseased subjects.

On the Number of Neurons and Time Scale of Integration Underlying the Formation of Percepts in the Brain

PubMed Central

Wohrer, Adrien; Machens, Christian K.

2015-01-01

All of our perceptual experiences arise from the activity of neural populations. Here we study the formation of such percepts under the assumption that they emerge from a linear readout, i.e., a weighted sum of the neurons’ firing rates. We show that this assumption constrains the trial-to-trial covariance structure of neural activities and animal behavior. The predicted covariance structure depends on the readout parameters, and in particular on the temporal integration window w and typical number of neurons K used in the formation of the percept. Using these predictions, we show how to infer the readout parameters from joint measurements of a subject’s behavior and neural activities. We consider three such scenarios: (1) recordings from the complete neural population, (2) recordings of neuronal sub-ensembles whose size exceeds K, and (3) recordings of neuronal sub-ensembles that are smaller than K. Using theoretical arguments and artificially generated data, we show that the first two scenarios allow us to recover the typical spatial and temporal scales of the readout. In the third scenario, we show that the readout parameters can only be recovered by making additional assumptions about the structure of the full population activity. Our work provides the first thorough interpretation of (feed-forward) percept formation from a population of sensory neurons. We discuss applications to experimental recordings in classic sensory decision-making tasks, which will hopefully provide new insights into the nature of perceptual integration. PMID:25793393

Identification of major cardiovascular events in patients with diabetes using primary care data.

PubMed

Pouwels, Koen Bernardus; Voorham, Jaco; Hak, Eelko; Denig, Petra

2016-04-02

Routine primary care data are increasingly being used for evaluation and research purposes but there are concerns about the completeness and accuracy of diagnoses and events captured in such databases. We evaluated how well patients with major cardiovascular disease (CVD) can be identified using primary care morbidity data and drug prescriptions. The study was conducted using data from 17,230 diabetes patients of the GIANTT database and Dutch Hospital Data register. To estimate the accuracy of the different measures, we analyzed the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to hospitalizations and/or records with a diagnosis indicating major CVD, including ischaemic heart diseases and cerebrovascular events. Using primary care morbidity data, 43% of major CVD hospitalizations could be identified. Adding drug prescriptions to the search increased the sensitivity up to 94%. A proxy of at least one prescription of either a platelet aggregation inhibitor, vitamin k antagonist or nitrate could identify 85% of patients with a history of major CVD recorded in primary care, with an NPV of 97%. Using the same proxy, 57% of incident major CVD recorded in primary or hospital care could be identified, with an NPV of 99%. A substantial proportion of major CVD hospitalizations was not recorded in primary care morbidity data. Drug prescriptions can be used in addition to diagnosis codes to identify more patients with major CVD, and also to identify patients without a history of major CVD.

Market Confidence Predicts Stock Price: Beyond Supply and Demand

PubMed Central

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price. PMID:27391816

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources for early drought warning.

A new model for early Earth: heat-pipe cooling

NASA Astrophysics Data System (ADS)

Webb, A. G.; Moore, W. B.

2013-12-01

In the study of heat transport and lithospheric dynamics of early Earth, current models depend upon plate tectonic and vertical tectonic concepts. Plate tectonic models adequately account for regions with diverse lithologies juxtaposed along ancient shear zones, as seen at the famous Eoarchean Isua supracrustal belt of West Greenland. Vertical tectonic models to date have involved volcanism, sub- and intra-lithospheric diapirism, and sagduction, and can explain the geology of the best-preserved low-grade ancient terranes, such as the Paleoarchean Barberton and Pilbara greenstone belts. However, these models do not offer a globally-complete framework consistent with the geologic record. Plate tectonics models suggest that paired metamorphic belts and passive margins are among the most likely features to be preserved, but the early rock record shows no evidence of these terranes. Existing vertical tectonics models account for the >300 million years of semi-continuous volcanism and diapirism at Barberton and Pilbara, but when they explain the shearing record at Isua, they typically invoke some horizontal motion that cannot be differentiated from plate motion and is not a salient feature of the lengthy Barberton and Pilbara records. Despite the strengths of these models, substantial uncertainty remains about how early Earth evolved from magma ocean to plate tectonics. We have developed a new model, based on numerical simulations and analysis of the geologic record, that provides a coherent, global geodynamic framework for Earth's evolution from magma ocean to subduction tectonics. We hypothesize that heat-pipe cooling offers a viable mechanism for the lithospheric dynamics of early Earth. Our numerical simulations of heat-pipe cooling on early Earth indicate that a cold, thick, single-plate lithosphere developed as a result of frequent volcanic eruptions that advected surface materials downward. The constant resurfacing and downward advection caused compression as the surface rocks were forced radially inward, resulting in uplift, exhumation, and shortening. Declining heat sources over time led to an abrupt, dynamically spontaneous transition to plate tectonics. The model predicts a geological record with rapid, semi-continuous volcanic resurfacing; contractional deformation; a low geothermal gradient across the bulk of the lithosphere; and a rapid decrease in heat-pipe volcanism after the initiation of plate tectonics. Review of data from ancient cratons and the detrital zircon record is consistent with these predictions. In this presentation, we review these findings with a focus on comparison of the model predictions with the geologic record. This comparison suggests that Earth cooled via heat pipes until a ~3.2 Ga subduction initiation episode. The Isua record reflects long-lived contractional deformation, and the Barberton and Pilbara records preserve heat-pipe lithospheric development in regions without significant contraction. In summary, the heat-pipe model provides a view of early Earth that is more globally applicable than existing plate and vertical tectonic models.

Core self-evaluations and Snyder's hope theory in persons with spinal cord injuries.

PubMed

Smedema, Susan Miller; Chan, Jacob Yuichung; Phillips, Brian N

2014-11-01

The objective of the study was to evaluate a motivational model of core self-evaluations (CSE), hope (agency and pathways thinking), participation, and life satisfaction in persons with spinal cord injuries. A cross-sectional, correlational design with path analysis was used to evaluate the model. 187 adults with spinal cord injuries participated in this study. The results indicated an excellent fit between the data and the proposed model. Specifically, CSE was found to directly predict agency and pathways thinking, participation, and life satisfaction. CSE was also found to indirectly predict participation and life satisfaction through agency thinking. Although CSE contributes directly to participation and life satisfaction, it also has a unique role in increasing individuals' motivation to pursue goals, which also predicts participation and life satisfaction. Counseling interventions should be multifaceted and address the components of CSE to increase hope, participation, and life satisfaction. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Mining nutrigenetics patterns related to obesity: use of parallel multifactor dimensionality reduction.

PubMed

Karayianni, Katerina N; Grimaldi, Keith A; Nikita, Konstantina S; Valavanis, Ioannis K

2015-01-01

This paper aims to enlighten the complex etiology beneath obesity by analysing data from a large nutrigenetics study, in which nutritional and genetic factors associated with obesity were recorded for around two thousand individuals. In our previous work, these data have been analysed using artificial neural network methods, which identified optimised subsets of factors to predict one's obesity status. These methods did not reveal though how the selected factors interact with each other in the obtained predictive models. For that reason, parallel Multifactor Dimensionality Reduction (pMDR) was used here to further analyse the pre-selected subsets of nutrigenetic factors. Within pMDR, predictive models using up to eight factors were constructed, further reducing the input dimensionality, while rules describing the interactive effects of the selected factors were derived. In this way, it was possible to identify specific genetic variations and their interactive effects with particular nutritional factors, which are now under further study.