Testing Expert-Based versus Student-Based Cognitive Models for a Grade 3 Diagnostic Mathematics Assessment

ERIC Educational Resources Information Center

Roduta Roberts, Mary; Alves, Cecilia B.; Chu, Man-Wai; Thompson, Margaret; Bahry, Louise M.; Gotzmann, Andrea

2014-01-01

The purpose of this study was to evaluate the adequacy of three cognitive models, one developed by content experts and two generated from student verbal reports for explaining examinee performance on a grade 3 diagnostic mathematics test. For this study, the items were developed to directly measure the attributes in the cognitive model. The…

Bayesian modeling and inference for diagnostic accuracy and probability of disease based on multiple diagnostic biomarkers with and without a perfect reference standard.

PubMed

Jafarzadeh, S Reza; Johnson, Wesley O; Gardner, Ian A

2016-03-15

The area under the receiver operating characteristic (ROC) curve (AUC) is used as a performance metric for quantitative tests. Although multiple biomarkers may be available for diagnostic or screening purposes, diagnostic accuracy is often assessed individually rather than in combination. In this paper, we consider the interesting problem of combining multiple biomarkers for use in a single diagnostic criterion with the goal of improving the diagnostic accuracy above that of an individual biomarker. The diagnostic criterion created from multiple biomarkers is based on the predictive probability of disease, conditional on given multiple biomarker outcomes. If the computed predictive probability exceeds a specified cutoff, the corresponding subject is allocated as 'diseased'. This defines a standard diagnostic criterion that has its own ROC curve, namely, the combined ROC (cROC). The AUC metric for cROC, namely, the combined AUC (cAUC), is used to compare the predictive criterion based on multiple biomarkers to one based on fewer biomarkers. A multivariate random-effects model is proposed for modeling multiple normally distributed dependent scores. Bayesian methods for estimating ROC curves and corresponding (marginal) AUCs are developed when a perfect reference standard is not available. In addition, cAUCs are computed to compare the accuracy of different combinations of biomarkers for diagnosis. The methods are evaluated using simulations and are applied to data for Johne's disease (paratuberculosis) in cattle. Copyright © 2015 John Wiley & Sons, Ltd.

Model Based Inference for Wire Chafe Diagnostics

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Wheeler, Kevin R.; Timucin, Dogan A.; Wysocki, Philip F.; Kowalski, Marc Edward

2009-01-01

Presentation for Aging Aircraft conference covering chafing fault diagnostics using Time Domain Reflectometry. Laboratory setup and experimental methods are presented, along with initial results that summarize fault modeling and detection capabilities.

A computational framework for converting textual clinical diagnostic criteria into the quality data model.

PubMed

Hong, Na; Li, Dingcheng; Yu, Yue; Xiu, Qiongying; Liu, Hongfang; Jiang, Guoqian

2016-10-01

Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria. To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM. We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation. We harvested 267 individual criteria with the datatypes of Symptom and Laboratory Test from 63 textual diagnostic criteria. We manually annotated attributes and values in 142 individual Laboratory Test criteria. The average performance of our rule-based approach was 0.84 of precision, 0.86 of recall, and 0.85 of f-measure; the performance of CRFs-based classification was 0.95 of precision, 0.88 of recall and 0.91 of f-measure. We also implemented a web-based tool that automatically translates textual Laboratory Test criteria into the QDM XML template format. The results indicated that our approaches leveraging cTAKES and CRFs are effective in facilitating diagnostic criteria annotation and classification. Our NLP-based computational framework is a feasible and useful solution in developing diagnostic criteria representation and computerization. Copyright © 2016 Elsevier Inc. All rights reserved.

Modeling companion diagnostics in economic evaluations of targeted oncology therapies: systematic review and methodological checklist.

PubMed

Doble, Brett; Tan, Marcus; Harris, Anthony; Lorgelly, Paula

2015-02-01

The successful use of a targeted therapy is intrinsically linked to the ability of a companion diagnostic to correctly identify patients most likely to benefit from treatment. The aim of this study was to review the characteristics of companion diagnostics that are of importance for inclusion in an economic evaluation. Approaches for including these characteristics in model-based economic evaluations are compared with the intent to describe best practice methods. Five databases and government agency websites were searched to identify model-based economic evaluations comparing a companion diagnostic and subsequent treatment strategy to another alternative treatment strategy with model parameters for the sensitivity and specificity of the companion diagnostic (primary synthesis). Economic evaluations that limited model parameters for the companion diagnostic to only its cost were also identified (secondary synthesis). Quality was assessed using the Quality of Health Economic Studies instrument. 30 studies were included in the review (primary synthesis n = 12; secondary synthesis n = 18). Incremental cost-effectiveness ratios may be lower when the only parameter for the companion diagnostic included in a model is the cost of testing. Incorporating the test's accuracy in addition to its cost may be a more appropriate methodological approach. Altering the prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests can all impact overall model results. The impact of altering a test's threshold for positivity is unknown as it was not addressed in any of the included studies. Additional quality criteria as outlined in our methodological checklist should be considered due to the shortcomings of standard quality assessment tools in differentiating studies that incorporate important test-related characteristics and those that do not. There is a need to refine methods for incorporating the characteristics of companion diagnostics into model-based economic evaluations to ensure consistent and transparent reimbursement decisions are made.

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

Risk-adjusted capitation based on the Diagnostic Cost Group Model: an empirical evaluation with health survey information.

PubMed Central

Lamers, L M

1999-01-01

OBJECTIVE: To evaluate the predictive accuracy of the Diagnostic Cost Group (DCG) model using health survey information. DATA SOURCES/STUDY SETTING: Longitudinal data collected for a sample of members of a Dutch sickness fund. In the Netherlands the sickness funds provide compulsory health insurance coverage for the 60 percent of the population in the lowest income brackets. STUDY DESIGN: A demographic model and DCG capitation models are estimated by means of ordinary least squares, with an individual's annual healthcare expenditures in 1994 as the dependent variable. For subgroups based on health survey information, costs predicted by the models are compared with actual costs. Using stepwise regression procedures a subset of relevant survey variables that could improve the predictive accuracy of the three-year DCG model was identified. Capitation models were extended with these variables. DATA COLLECTION/EXTRACTION METHODS: For the empirical analysis, panel data of sickness fund members were used that contained demographic information, annual healthcare expenditures, and diagnostic information from hospitalizations for each member. In 1993, a mailed health survey was conducted among a random sample of 15,000 persons in the panel data set, with a 70 percent response rate. PRINCIPAL FINDINGS: The predictive accuracy of the demographic model improves when it is extended with diagnostic information from prior hospitalizations (DCGs). A subset of survey variables further improves the predictive accuracy of the DCG capitation models. The predictable profits and losses based on survey information for the DCG models are smaller than for the demographic model. Most persons with predictable losses based on health survey information were not hospitalized in the preceding year. CONCLUSIONS: The use of diagnostic information from prior hospitalizations is a promising option for improving the demographic capitation payment formula. This study suggests that diagnostic information from outpatient utilization is complementary to DCGs in predicting future costs. PMID:10029506

Validation of N-glycan markers that improve the performance of CA19-9 in pancreatic cancer.

PubMed

Zhao, Yun-Peng; Zhou, Ping-Ting; Ji, Wei-Ping; Wang, Hao; Fang, Meng; Wang, Meng-Meng; Yin, Yue-Peng; Jin, Gang; Gao, Chun-Fang

2017-02-01

Pancreatic cancer (PC) has a high mortality rate because it is usually diagnosed late. Glycosylation of proteins is known to change in tumor cells during the development of PC. The objectives of this study were to identify and validate the diagnostic value of novel biomarkers based on N-glycomic profiling for PC. In total, 217 individuals including subjects with PC, pancreatitis, and healthy controls were divided randomly into a training group (n = 164) and validation groups (n = 53). Serum N-glycomic profiling was analyzed by DSA-FACE. The diagnostic model was constructed based on N-glycan markers with logistic stepwise regression. The diagnostic performance of the model was assessed further in validation cohort. The level of total core fucose residues was increased significantly in PC. Two diagnostic models designated GlycoPCtest and PCmodel (combining GlycoPCtest and CA19-9) were constructed to differentiate PC from normal. The area under the receiver operating characteristic curve (AUC) of PCmodel was higher than that of CA19-9 (0.925 vs. 0.878). The diagnostic models based on N-glycans are new, valuable, noninvasive alternatives for identifying PC. The diagnostic efficacy is improved by combined GlycoPCtest and CA19-9 for the discrimination of patients with PC from healthy controls.

Operational modelling: the mechanisms influencing TB diagnostic yield in an Xpert® MTB/RIF-based algorithm.

PubMed

Dunbar, R; Naidoo, P; Beyers, N; Langley, I

2017-04-01

Cape Town, South Africa. To compare the diagnostic yield for smear/culture and Xpert® MTB/RIF algorithms and to investigate the mechanisms influencing tuberculosis (TB) yield. We developed and validated an operational model of the TB diagnostic process, first with the smear/culture algorithm and then with the Xpert algorithm. We modelled scenarios by varying TB prevalence, adherence to diagnostic algorithms and human immunodeficiency virus (HIV) status. This enabled direct comparisons of diagnostic yield in the two algorithms to be made. Routine data showed that diagnostic yield had decreased over the period of the Xpert algorithm roll-out compared to the yield when the smear/culture algorithm was in place. However, modelling yield under identical conditions indicated a 13.3% increase in diagnostic yield from the Xpert algorithm compared to smear/culture. The model demonstrated that the extensive use of culture in the smear/culture algorithm and the decline in TB prevalence are the main factors contributing to not finding an increase in diagnostic yield in the routine data. We demonstrate the benefits of an operational model to determine the effect of scale-up of a new diagnostic algorithm, and recommend that policy makers use operational modelling to make appropriate decisions before new diagnostic algorithms are scaled up.

Evidence base and future research directions in the management of low back pain.

PubMed

Abbott, Allan

2016-03-18

Low back pain (LBP) is a prevalent and costly condition. Awareness of valid and reliable patient history taking, physical examination and clinical testing is important for diagnostic accuracy. Stratified care which targets treatment to patient subgroups based on key characteristics is reliant upon accurate diagnostics. Models of stratified care that can potentially improve treatment effects include prognostic risk profiling for persistent LBP, likely response to specific treatment based on clinical prediction models or suspected underlying causal mechanisms. The focus of this editorial is to highlight current research status and future directions for LBP diagnostics and stratified care.

Estimation and Q-Matrix Validation for Diagnostic Classification Models

ERIC Educational Resources Information Center

Feng, Yuling

2013-01-01

Diagnostic classification models (DCMs) are structured latent class models widely discussed in the field of psychometrics. They model subjects' underlying attribute patterns and classify subjects into unobservable groups based on their mastery of attributes required to answer the items correctly. The effective implementation of DCMs depends…

A stochastic model to determine the economic value of changing diagnostic test characteristics for identification of cattle for treatment of bovine respiratory disease.

PubMed

Theurer, M E; White, B J; Larson, R L; Schroeder, T C

2015-03-01

Bovine respiratory disease is an economically important syndrome in the beef industry, and diagnostic accuracy is important for optimal disease management. The objective of this study was to determine whether improving diagnostic sensitivity or specificity was of greater economic value at varied levels of respiratory disease prevalence by using Monte Carlo simulation. Existing literature was used to populate model distributions of published sensitivity, specificity, and performance (ADG, carcass weight, yield grade, quality grade, and mortality risk) differences among calves based on clinical respiratory disease status. Data from multiple cattle feeding operations were used to generate true ranges of respiratory disease prevalence and associated mortality. Input variables were combined into a single model that calculated estimated net returns for animals by diagnostic category (true positive, false positive, false negative, and true negative) based on the prevalence, sensitivity, and specificity for each iteration. Net returns for each diagnostic category were multiplied by the proportion of animals in each diagnostic category to determine group profitability. Apparent prevalence was categorized into low (<15%) and high (≥15%) groups. For both apparent prevalence categories, increasing specificity created more rapid, positive change in net returns than increasing sensitivity. Improvement of diagnostic specificity, perhaps through a confirmatory test interpreted in series or pen-level diagnostics, can increase diagnostic value more than improving sensitivity. Mortality risk was the primary driver for net returns. The results from this study are important for determining future research priorities to analyze diagnostic techniques for bovine respiratory disease and provide a novel way for modeling diagnostic tests.

Intercomparisons of Prognostic, Diagnostic, and Inversion Modeling Approaches for Estimation of Net Ecosystem Exchange over the Pacific Northwest Region

NASA Astrophysics Data System (ADS)

Turner, D. P.; Jacobson, A. R.; Nemani, R. R.

2013-12-01

The recent development of large spatially-explicit datasets for multiple variables relevant to monitoring terrestrial carbon flux offers the opportunity to estimate the terrestrial land flux using several alternative, potentially complimentary, approaches. Here we developed and compared regional estimates of net ecosystem exchange (NEE) over the Pacific Northwest region of the U.S. using three approaches. In the prognostic modeling approach, the process-based Biome-BGC model was driven by distributed meteorological station data and was informed by Landsat-based coverages of forest stand age and disturbance regime. In the diagnostic modeling approach, the quasi-mechanistic CFLUX model estimated net ecosystem production (NEP) by upscaling eddy covariance flux tower observations. The model was driven by distributed climate data and MODIS FPAR (the fraction of incident PAR that is absorbed by the vegetation canopy). It was informed by coarse resolution (1 km) data about forest stand age. In both the prognostic and diagnostic modeling approaches, emissions estimates for biomass burning, harvested products, and river/stream evasion were added to model-based NEP to get NEE. The inversion model (CarbonTracker) relied on observations of atmospheric CO2 concentration to optimize prior surface carbon flux estimates. The Pacific Northwest is heterogeneous with respect to land cover and forest management, and repeated surveys of forest inventory plots support the presence of a strong regional carbon sink. The diagnostic model suggested a stronger carbon sink than the prognostic model, and a much larger sink that the inversion model. The introduction of Landsat data on disturbance history served to reduce uncertainty with respect to regional NEE in the diagnostic and prognostic modeling approaches. The FPAR data was particularly helpful in capturing the seasonality of the carbon flux using the diagnostic modeling approach. The inversion approach took advantage of a global network of CO2 observation stations, but had difficulty resolving regional fluxes such as that in the PNW given the still sparse nature of the CO2 measurement network.

A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means

ERIC Educational Resources Information Center

Polak, Marike; De Rooij, Mark; Heiser, Willem J.

2012-01-01

In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) "criterion…

Model-based diagnostics for Space Station Freedom

NASA Technical Reports Server (NTRS)

Fesq, Lorraine M.; Stephan, Amy; Martin, Eric R.; Lerutte, Marcel G.

1991-01-01

An innovative approach to fault management was recently demonstrated for the NASA LeRC Space Station Freedom (SSF) power system testbed. This project capitalized on research in model-based reasoning, which uses knowledge of a system's behavior to monitor its health. The fault management system (FMS) can isolate failures online, or in a post analysis mode, and requires no knowledge of failure symptoms to perform its diagnostics. An in-house tool called MARPLE was used to develop and run the FMS. MARPLE's capabilities are similar to those available from commercial expert system shells, although MARPLE is designed to build model-based as opposed to rule-based systems. These capabilities include functions for capturing behavioral knowledge, a reasoning engine that implements a model-based technique known as constraint suspension, and a tool for quickly generating new user interfaces. The prototype produced by applying MARPLE to SSF not only demonstrated that model-based reasoning is a valuable diagnostic approach, but it also suggested several new applications of MARPLE, including an integration and testing aid, and a complement to state estimation.

Diagnosing Alzheimer's disease: a systematic review of economic evaluations.

PubMed

Handels, Ron L H; Wolfs, Claire A G; Aalten, Pauline; Joore, Manuela A; Verhey, Frans R J; Severens, Johan L

2014-03-01

The objective of this study is to systematically review the literature on economic evaluations of interventions for the early diagnosis of Alzheimer's disease (AD) and related disorders and to describe their general and methodological characteristics. We focused on the diagnostic aspects of the decision models to assess the applicability of existing decision models for the evaluation of the recently revised diagnostic research criteria for AD. PubMed and the National Institute for Health Research Economic Evaluation database were searched for English-language publications related to economic evaluations on diagnostic technologies. Trial-based economic evaluations were assessed using the Consensus on Health Economic Criteria list. Modeling studies were assessed using the framework for quality assessment of decision-analytic models. The search retrieved 2109 items, from which eight decision-analytic modeling studies and one trial-based economic evaluation met all eligibility criteria. Diversity among the study objective and characteristics was considerable and, despite considerable methodological quality, several flaws were indicated. Recommendations were focused on diagnostic aspects and the applicability of existing models for the evaluation of recently revised diagnostic research criteria for AD. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin

USDA-ARS?s Scientific Manuscript database

Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance, or with spatially distributed prognostic models that simultaneously balance both the energy and water budgets over landscapes using predictive equations for land...

Internet gaming disorder: Inadequate diagnostic criteria wrapped in a constraining conceptual model.

PubMed

Starcevic, Vladan

2017-06-01

Background and aims The paper "Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder: Issues, concerns, and recommendations for clarity in the field" by Kuss, Griffiths, and Pontes (in press) critically examines the DSM-5 diagnostic criteria for Internet gaming disorder (IGD) and addresses the issue of whether IGD should be reconceptualized as gaming disorder, regardless of whether video games are played online or offline. This commentary provides additional critical perspectives on the concept of IGD. Methods The focus of this commentary is on the addiction model on which the concept of IGD is based, the nature of the DSM-5 criteria for IGD, and the inclusion of withdrawal symptoms and tolerance as the diagnostic criteria for IGD. Results The addiction framework on which the DSM-5 concept of IGD is based is not without problems and represents only one of multiple theoretical approaches to problematic gaming. The polythetic, non-hierarchical DSM-5 diagnostic criteria for IGD make the concept of IGD unacceptably heterogeneous. There is no support for maintaining withdrawal symptoms and tolerance as the diagnostic criteria for IGD without their substantial revision. Conclusions The addiction model of IGD is constraining and does not contribute to a better understanding of the various patterns of problematic gaming. The corresponding diagnostic criteria need a thorough overhaul, which should be based on a model of problematic gaming that can accommodate its disparate aspects.

A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests

PubMed Central

Chen, Yong; Liu, Yulun; Ning, Jing; Cormier, Janice; Chu, Haitao

2014-01-01

Systematic reviews of diagnostic tests often involve a mixture of case-control and cohort studies. The standard methods for evaluating diagnostic accuracy only focus on sensitivity and specificity and ignore the information on disease prevalence contained in cohort studies. Consequently, such methods cannot provide estimates of measures related to disease prevalence, such as population averaged or overall positive and negative predictive values, which reflect the clinical utility of a diagnostic test. In this paper, we propose a hybrid approach that jointly models the disease prevalence along with the diagnostic test sensitivity and specificity in cohort studies, and the sensitivity and specificity in case-control studies. In order to overcome the potential computational difficulties in the standard full likelihood inference of the proposed hybrid model, we propose an alternative inference procedure based on the composite likelihood. Such composite likelihood based inference does not suffer computational problems and maintains high relative efficiency. In addition, it is more robust to model mis-specifications compared to the standard full likelihood inference. We apply our approach to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma. PMID:25897179

Using a web-based application to define the accuracy of diagnostic tests when the gold standard is imperfect.

PubMed

Lim, Cherry; Wannapinij, Prapass; White, Lisa; Day, Nicholas P J; Cooper, Ben S; Peacock, Sharon J; Limmathurotsakul, Direk

2013-01-01

Estimates of the sensitivity and specificity for new diagnostic tests based on evaluation against a known gold standard are imprecise when the accuracy of the gold standard is imperfect. Bayesian latent class models (LCMs) can be helpful under these circumstances, but the necessary analysis requires expertise in computational programming. Here, we describe open-access web-based applications that allow non-experts to apply Bayesian LCMs to their own data sets via a user-friendly interface. Applications for Bayesian LCMs were constructed on a web server using R and WinBUGS programs. The models provided (http://mice.tropmedres.ac) include two Bayesian LCMs: the two-tests in two-population model (Hui and Walter model) and the three-tests in one-population model (Walter and Irwig model). Both models are available with simplified and advanced interfaces. In the former, all settings for Bayesian statistics are fixed as defaults. Users input their data set into a table provided on the webpage. Disease prevalence and accuracy of diagnostic tests are then estimated using the Bayesian LCM, and provided on the web page within a few minutes. With the advanced interfaces, experienced researchers can modify all settings in the models as needed. These settings include correlation among diagnostic test results and prior distributions for all unknown parameters. The web pages provide worked examples with both models using the original data sets presented by Hui and Walter in 1980, and by Walter and Irwig in 1988. We also illustrate the utility of the advanced interface using the Walter and Irwig model on a data set from a recent melioidosis study. The results obtained from the web-based applications were comparable to those published previously. The newly developed web-based applications are open-access and provide an important new resource for researchers worldwide to evaluate new diagnostic tests.

Evidence base and future research directions in the management of low back pain

PubMed Central

Abbott, Allan

2016-01-01

Low back pain (LBP) is a prevalent and costly condition. Awareness of valid and reliable patient history taking, physical examination and clinical testing is important for diagnostic accuracy. Stratified care which targets treatment to patient subgroups based on key characteristics is reliant upon accurate diagnostics. Models of stratified care that can potentially improve treatment effects include prognostic risk profiling for persistent LBP, likely response to specific treatment based on clinical prediction models or suspected underlying causal mechanisms. The focus of this editorial is to highlight current research status and future directions for LBP diagnostics and stratified care. PMID:27004162

Developing a new intelligent system for the diagnosis of tuberculous pleural effusion.

PubMed

Li, Chengye; Hou, Lingxian; Sharma, Bishundat Yanesh; Li, Huaizhong; Chen, ChengShui; Li, Yuping; Zhao, Xuehua; Huang, Hui; Cai, Zhennao; Chen, Huiling

2018-01-01

In countries with high prevalence of tuberculosis (TB), clinicians often diagnose tuberculous pleural effusion (TPE) by using diagnostic tests, which have not only poor sensitivity, but poor availability as well. The aim of our study is to develop a new artificial intelligence based diagnostic model that is accurate, fast, non-invasive and cost effective to diagnose TPE. It is expected that a tool derived based on the model be installed on simple computer devices (such as smart phones and tablets) and be used by clinicians widely. For this study, data of 140 patients whose clinical signs, routine blood test results, blood biochemistry markers, pleural fluid cell type and count, and pleural fluid biochemical tests' results were prospectively collected into a database. An Artificial intelligence based diagnostic model, which employs moth flame optimization based support vector machine with feature selection (FS-MFO-SVM), is constructed to predict the diagnosis of TPE. The optimal model results in an average of 95% accuracy (ACC), 0.9564 the area under the receiver operating characteristic curve (AUC), 93.35% sensitivity, and 97.57% specificity for FS-MFO-SVM. The proposed artificial intelligence based diagnostic model is found to be highly reliable for diagnosing TPE based on simple clinical signs, blood samples and pleural effusion samples. Therefore, the proposed model can be widely used in clinical practice and further evaluated for use as a substitute of invasive pleural biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated turbulence forecasts for aviation hazards

NASA Astrophysics Data System (ADS)

Sharman, R.; Frehlich, R.; Vandenberghe, F.

2010-09-01

An operational turbulence forecast system for commercial and aviation use is described that is based on an ensemble of turbulence diagnostics derived from standard NWP model outputs. In the U. S. this forecast product is named GTG (Graphical Turbulence Guidance) and has been described in detail in Sharman et al., WAF 2006. Since turbulence has many sources in the atmosphere, the ensemble approach of combining diagnostics has been shown to provide greater statistical accuracy than the use of a single diagnostic, or of a subgrid tke parameterization. GTG is sponsored by the FAA, and has undergone rigorous accuracy, safety, and usability evaluations. The GTG product is now hosted on NOAA's Aviation Data Service (ADDS), web site (http://aviationweather.gov/), for access by pilots, air traffic controllers, and dispatchers. During this talk the various turbulence diagnostics, their statistical properties, and their relative performance (based on comparisons to observations) will be presented. Importantly, the model output is ɛ1/3 (where ɛ is the eddy dissipation rate), so is aircraft independent. The diagnostics are individually and collectively calibrated so that their PDFs satisfy the expected log normal distribution of ɛ^1/3. Some of the diagnostics try to take into account the role of gravity waves and inertia-gravity waves in the turbulence generation process. Although the current GTG product is based on the RUC forecast model running over the CONUS, it is transitioning to a WRF based product, and in fact WRF-based versions are currently running operationally over Taiwan and has also been implemented for use by the French Navy in climatological studies. Yet another version has been developed which uses GFS model output to provide global turbulence forecasts. Thus the forecast product is available as a postprocessing program for WRF or other model output and provides 3D maps of turbulence likelihood of any region where NWP model data is available. Although the current GTG has been used mainly for large commercial aircraft, since the output is aircraft independent it could readily be scaled to smaller aircraft such as UAVs. Further, the ensemble technique allows the diagnostics to be used to form probabilistic forecasts, in a manner similar to ensemble NWP forecasts.

Mobile Diagnostics Based on Motion? A Close Look at Motility Patterns in the Schistosome Life Cycle

PubMed Central

Linder, Ewert; Varjo, Sami; Thors, Cecilia

2016-01-01

Imaging at high resolution and subsequent image analysis with modified mobile phones have the potential to solve problems related to microscopy-based diagnostics of parasitic infections in many endemic regions. Diagnostics using the computing power of “smartphones” is not restricted by limited expertise or limitations set by visual perception of a microscopist. Thus diagnostics currently almost exclusively dependent on recognition of morphological features of pathogenic organisms could be based on additional properties, such as motility characteristics recognizable by computer vision. Of special interest are infectious larval stages and “micro swimmers” of e.g., the schistosome life cycle, which infect the intermediate and definitive hosts, respectively. The ciliated miracidium, emerges from the excreted egg upon its contact with water. This means that for diagnostics, recognition of a swimming miracidium is equivalent to recognition of an egg. The motility pattern of miracidia could be defined by computer vision and used as a diagnostic criterion. To develop motility pattern-based diagnostics of schistosomiasis using simple imaging devices, we analyzed Paramecium as a model for the schistosome miracidium. As a model for invasive nematodes, such as strongyloids and filaria, we examined a different type of motility in the apathogenic nematode Turbatrix, the “vinegar eel.” The results of motion time and frequency analysis suggest that target motility may be expressed as specific spectrograms serving as “diagnostic fingerprints.” PMID:27322330

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

ERIC Educational Resources Information Center

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

ISHM-oriented adaptive fault diagnostics for avionics based on a distributed intelligent agent system

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Zhong, Zhengqiang; Xu, Lei

2015-10-01

In this paper, an integrated system health management-oriented adaptive fault diagnostics and model for avionics is proposed. With avionics becoming increasingly complicated, precise and comprehensive avionics fault diagnostics has become an extremely complicated task. For the proposed fault diagnostic system, specific approaches, such as the artificial immune system, the intelligent agents system and the Dempster-Shafer evidence theory, are used to conduct deep fault avionics diagnostics. Through this proposed fault diagnostic system, efficient and accurate diagnostics can be achieved. A numerical example is conducted to apply the proposed hybrid diagnostics to a set of radar transmitters on an avionics system and to illustrate that the proposed system and model have the ability to achieve efficient and accurate fault diagnostics. By analyzing the diagnostic system's feasibility and pragmatics, the advantages of this system are demonstrated.

Diagnostic indicators for integrated assessment models of climate policy

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

Kriegler, Elmar; Petermann, Nils; Krey, Volker

2015-01-01

Integrated assessments of how climate policy interacts with energy-economic systems can be performed by a variety of models with different functional structures. This article proposes a diagnostic scheme that can be applied to a wide range of integrated assessment models to classify differences among models based on their carbon price responses. Model diagnostics can uncover patterns and provide insights into why, under a given scenario, certain types of models behave in observed ways. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnosticmore » indicators to characterize model responses to carbon price signals and test these in a diagnostic study with 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to more easily explain variations among policy-relevant model results.« less

Designing an activity-based costing model for a non-admitted prisoner healthcare setting.

PubMed

Cai, Xiao; Moore, Elizabeth; McNamara, Martin

2013-09-01

To design and deliver an activity-based costing model within a non-admitted prisoner healthcare setting. Key phases from the NSW Health clinical redesign methodology were utilised: diagnostic, solution design and implementation. The diagnostic phase utilised a range of strategies to identify issues requiring attention in the development of the costing model. The solution design phase conceptualised distinct 'building blocks' of activity and cost based on the speciality of clinicians providing care. These building blocks enabled the classification of activity and comparisons of costs between similar facilities. The implementation phase validated the model. The project generated an activity-based costing model based on actual activity performed, gained acceptability among clinicians and managers, and provided the basis for ongoing efficiency and benchmarking efforts.

Health-based risk adjustment: improving the pharmacy-based cost group model by adding diagnostic cost groups.

PubMed

Prinsze, Femmeke J; van Vliet, René C J A

Since 1991, risk-adjusted premium subsidies have existed in the Dutch social health insurance sector, which covered about two-thirds of the population until 2006. In 2002, pharmacy-based cost groups (PCGs) were included in the demographic risk adjustment model, which improved the goodness-of-fit, as measured by the R2, to 11.5%. The model's R2 reached 22.8% in 2004, when inpatient diagnostic information was added in the form of diagnostic cost groups (DCGs). PCGs and DCGs appear to be complementary in their ability to predict future costs. PCGs particularly improve the R2 for outpatient expenses, whereas DCGs improve the R2 for inpatient expenses. In 2006, this system of risk-adjusted premium subsidies was extended to cover the entire population.

Livingstone Model-Based Diagnosis of Earth Observing One Infusion Experiment

NASA Technical Reports Server (NTRS)

Hayden, Sandra C.; Sweet, Adam J.; Christa, Scott E.

2004-01-01

The Earth Observing One satellite, launched in November 2000, is an active earth science observation platform. This paper reports on the progress of an infusion experiment in which the Livingstone 2 Model-Based Diagnostic engine is deployed on Earth Observing One, demonstrating the capability to monitor the nominal operation of the spacecraft under command of an on-board planner, and demonstrating on-board diagnosis of spacecraft failures. Design and development of the experiment, specification and validation of diagnostic scenarios, characterization of performance results and benefits of the model- based approach are presented.

Comparison of Prognostic and Diagnostic Approaches to Modeling Evapotranspiration in the Nile River Basin

NASA Astrophysics Data System (ADS)

Yilmaz, M.; Anderson, M. C.; Zaitchik, B. F.; Crow, W. T.; Hain, C.; Ozdogan, M.; Chun, J. A.

2012-12-01

Actual evapotranspiration (ET) can be estimated using both prognostic and diagnostic modeling approaches, providing independent yet complementary information for hydrologic applications. Both approaches have advantages and disadvantages. When provided with temporally continuous atmospheric forcing data, prognostic models offer continuous sub-daily ET information together with the full set of water and energy balance fluxes and states (i.e. soil moisture, runoff, sensible and latent heat). On the other hand, the diagnostic modeling approach provides ET estimates over regions where reliable information about available soil water is not known (e.g., due to irrigation practices or shallow ground water levels not included in the prognostic model structure, unknown soil texture or plant rooting depth, etc). Prognostic model-based ET estimates are of great interest whenever consistent and complete water budget information is required or when there is a need to project ET for climate or land use change scenarios. Diagnostic models establish a stronger link to remote sensing observations, can be applied in regions with limited or questionable atmospheric forcing data, and provide valuable observation-derived information about the current land-surface state. Analysis of independently obtained ET estimates is particularly important in data poor regions. Such comparisons can help to reduce the uncertainty in the modeled ET estimates and to exclude outliers based on physical considerations. The Nile river basin is home to tens of millions of people whose daily life depends on water extracted from the river Nile. Yet the complete basin scale water balance of the Nile has been studied only a few times, and the temporal and the spatial distribution of hydrological fluxes (particularly ET) are still a subject of active research. This is due in part to a scarcity of ground-based station data for validation. In such regions, comparison between prognostic and diagnostic model output may be a valuable model evaluation tool. Motivated by the complementary information that exists in prognostic and diagnostic energy balance modeling, as well as the need for evaluation of water consumption estimates over the Nile basin, the purpose of this study is to 1) better describe the conceptual differences between prognostic and diagnostic modeling, 2) present the potential for diagnostic models to capture important hydrologic features that are not explicitly represented in prognostic model, 3) explore the differences in these two approaches over the Nile Basin, where ground data are sparse and transnational data sharing is unreliable. More specifically, we will compare output from the Noah prognostic model and the Atmosphere-Land Exchange Inverse (ALEXI) diagnostic model generated over ground truth data-poor Nile basin. Preliminary results indicate spatially, temporally, and magnitude wise consistent flux estimates for ALEXI and NOAH over irrigated Delta region, while there are differences over river-fed wetlands.

Propulsion IVHM Technology Experiment

NASA Technical Reports Server (NTRS)

Chicatelli, Amy K.; Maul, William A.; Fulton, Christopher E.

2006-01-01

The Propulsion IVHM Technology Experiment (PITEX) successfully demonstrated real-time fault detection and isolation of a virtual reusable launch vehicle (RLV) main propulsion system (MPS). Specifically, the PITEX research project developed and applied a model-based diagnostic system for the MPS of the X-34 RLV, a space-launch technology demonstrator. The demonstration was simulation-based using detailed models of the propulsion subsystem to generate nominal and failure scenarios during captive carry, which is the most safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion System (MPS) was performed, these simulated data were used to verify and validate the software system. Advanced diagnostic and signal processing algorithms were developed and tested in real time on flight-like hardware. In an attempt to expose potential performance problems, the PITEX diagnostic system was subjected to numerous realistic effects in the simulated data including noise, sensor resolution, command/valve talkback information, and nominal build variations. In all cases, the PITEX system performed as required. The research demonstrated potential benefits of model-based diagnostics, defined performance metrics required to evaluate the diagnostic system, and studied the impact of real-world challenges encountered when monitoring propulsion subsystems.

Developing a semantic web model for medical differential diagnosis recommendation.

PubMed

Mohammed, Osama; Benlamri, Rachid

2014-10-01

In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

Model-Based Diagnosis in a Power Distribution Test-Bed

NASA Technical Reports Server (NTRS)

Scarl, E.; McCall, K.

1998-01-01

The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.

Development of Monitoring and Diagnostic Methods for Robots Used In Remediation of Waste Sites - Final Report

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

Martin, M.

2000-04-01

This project is the first evaluation of model-based diagnostics to hydraulic robot systems. A greater understanding of fault detection for hydraulic robots has been gained, and a new theoretical fault detection model developed and evaluated.

Statistical Analysis of Q-matrix Based Diagnostic Classification Models

PubMed Central

Chen, Yunxiao; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2014-01-01

Diagnostic classification models have recently gained prominence in educational assessment, psychiatric evaluation, and many other disciplines. Central to the model specification is the so-called Q-matrix that provides a qualitative specification of the item-attribute relationship. In this paper, we develop theories on the identifiability for the Q-matrix under the DINA and the DINO models. We further propose an estimation procedure for the Q-matrix through the regularized maximum likelihood. The applicability of this procedure is not limited to the DINA or the DINO model and it can be applied to essentially all Q-matrix based diagnostic classification models. Simulation studies are conducted to illustrate its performance. Furthermore, two case studies are presented. The first case is a data set on fraction subtraction (educational application) and the second case is a subsample of the National Epidemiological Survey on Alcohol and Related Conditions concerning the social anxiety disorder (psychiatric application). PMID:26294801

A fluctuation-induced plasma transport diagnostic based upon fast-Fourier transform spectral analysis

NASA Technical Reports Server (NTRS)

Powers, E. J.; Kim, Y. C.; Hong, J. Y.; Roth, J. R.; Krawczonek, W. M.

1978-01-01

A diagnostic, based on fast Fourier-transform spectral analysis techniques, that provides experimental insight into the relationship between the experimentally observable spectral characteristics of the fluctuations and the fluctuation-induced plasma transport is described. The model upon which the diagnostic technique is based and its experimental implementation is discussed. Some characteristic results obtained during the course of an experimental study of fluctuation-induced transport in the electric field dominated NASA Lewis bumpy torus plasma are presented.

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

PubMed

Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin

2017-01-01

Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

Acquiring, Representing, and Evaluating a Competence Model of Diagnostic Strategy.

ERIC Educational Resources Information Center

Clancey, William J.

This paper describes NEOMYCIN, a computer program that models one physician's diagnostic reasoning within a limited area of medicine. NEOMYCIN's knowledge base and reasoning procedure constitute a model of how human knowledge is organized and how it is used in diagnosis. The hypothesis is tested that such a procedure can be used to simulate both…

Climate Model Diagnostic Analyzer Web Service System

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Li, J.; Zhang, J.; Wang, W.

2015-12-01

Both the National Research Council Decadal Survey and the latest Intergovernmental Panel on Climate Change Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with the synergistic use of global satellite observations in order to improve our weather and climate simulation and prediction capabilities. The abundance of satellite observations for fundamental climate parameters and the availability of coordinated model outputs from CMIP5 for the same parameters offer a great opportunity to understand and diagnose model biases in climate models. In addition, the Obs4MIPs efforts have created several key global observational datasets that are readily usable for model evaluations. However, a model diagnostic evaluation process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. In response, we have developed a novel methodology to diagnose model biases in contemporary climate models and implementing the methodology as a web-service based, cloud-enabled, provenance-supported climate-model evaluation system. The evaluation system is named Climate Model Diagnostic Analyzer (CMDA), which is the product of the research and technology development investments of several current and past NASA ROSES programs. The current technologies and infrastructure of CMDA are designed and selected to address several technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. In particular, we have three key technology components: (1) diagnostic analysis methodology; (2) web-service based, cloud-enabled technology; (3) provenance-supported technology. The diagnostic analysis methodology includes random forest feature importance ranking, conditional probability distribution function, conditional sampling, and time-lagged correlation map. We have implemented the new methodology as web services and incorporated the system into the Cloud. We have also developed a provenance management system for CMDA where CMDA service semantics modeling, service search and recommendation, and service execution history management are designed and implemented.

An Overview of Prognosis Health Management Research at Glenn Research Center for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

NASA Technical Reports Server (NTRS)

Arnold, Steven M.; Goldberg, Robert K.; Lerch, Bradley A.; Saleeb, Atef F.

2009-01-01

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s). Key technologies which comprise the proposed integrated approach include (1) diagnostic/detection methodology, (2) prognosis/lifing methodology, (3) diagnostic/prognosis linkage, (4) experimental validation, and (5) material data information management system. A specific prognosis lifing methodology, experimental characterization and validation and data information management are the focal point of current activities being pursued within this integrated approach. The prognostic lifing methodology is based on an advanced multimechanism viscoelastoplastic model which accounts for both stiffness and/or strength reduction damage variables. Methods to characterize both the reversible and irreversible portions of the model are discussed. Once the multiscale model is validated the intent is to link it to appropriate diagnostic methods to provide a full-featured structural health monitoring system.

An Overview of Prognosis Health Management Research at GRC for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

NASA Technical Reports Server (NTRS)

Arnold, Steven M.; Goldberg, Robert K.; Lerch, Bradley A.; Saleeb, Atef F.

2009-01-01

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s). Key technologies which comprise the proposed integrated approach include 1) diagnostic/detection methodology, 2) prognosis/lifing methodology, 3) diagnostic/prognosis linkage, 4) experimental validation and 5) material data information management system. A specific prognosis lifing methodology, experimental characterization and validation and data information management are the focal point of current activities being pursued within this integrated approach. The prognostic lifing methodology is based on an advanced multi-mechanism viscoelastoplastic model which accounts for both stiffness and/or strength reduction damage variables. Methods to characterize both the reversible and irreversible portions of the model are discussed. Once the multiscale model is validated the intent is to link it to appropriate diagnostic methods to provide a full-featured structural health monitoring system.

Model-Based Diagnostics for Propellant Loading Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Foygel, Michael; Smelyanskiy, Vadim N.

2011-01-01

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

Development of an integrated Sasang constitution diagnosis method using face, body shape, voice, and questionnaire information

PubMed Central

2012-01-01

Background Sasang constitutional medicine (SCM) is a unique form of traditional Korean medicine that divides human beings into four constitutional types (Tae-Yang: TY, Tae-Eum: TE, So-Yang: SY, and So-Eum: SE), which differ in inherited characteristics, such as external appearance, personality traits, susceptibility to particular diseases, drug responses, and equilibrium among internal organ functions. According to SCM, herbs that belong to a certain constitution cannot be used in patients with other constitutions; otherwise, this practice may result in no effect or in an adverse effect. Thus, the diagnosis of SC type is the most crucial step in SCM practice. The diagnosis, however, tends to be subjective due to a lack of quantitative standards for SC diagnosis. Methods We have attempted to make the diagnosis method as objective as possible by basing it on an analysis of quantitative data from various Oriental medical clinics. Four individual diagnostic models were developed with multinomial logistic regression based on face, body shape, voice, and questionnaire responses. Inspired by SCM practitioners’ holistic diagnostic processes, an integrated diagnostic model was then proposed by combining the four individual models. Results The diagnostic accuracies in the test set, after the four individual models had been integrated into a single model, improved to 64.0% and 55.2% in the male and female patient groups, respectively. Using a cut-off value for the integrated SC score, such as 1.6, the accuracies increased by 14.7% in male patients and by 4.6% in female patients, which showed that a higher integrated SC score corresponded to a higher diagnostic accuracy. Conclusions This study represents the first trial of integrating the objectification of SC diagnosis based on quantitative data and SCM practitioners’ holistic diagnostic processes. Although the diagnostic accuracy was not great, it is noted that the proposed diagnostic model represents common rules among practitioners who have various points of view. Our results are expected to contribute as a desirable research guide for objective diagnosis in traditional medicine, as well as to contribute to the precise diagnosis of SC types in an objective manner in clinical practice. PMID:22762505

Development of an integrated Sasang constitution diagnosis method using face, body shape, voice, and questionnaire information.

PubMed

Do, Jun-Hyeong; Jang, Eunsu; Ku, Boncho; Jang, Jun-Su; Kim, Honggie; Kim, Jong Yeol

2012-07-04

Sasang constitutional medicine (SCM) is a unique form of traditional Korean medicine that divides human beings into four constitutional types (Tae-Yang: TY, Tae-Eum: TE, So-Yang: SY, and So-Eum: SE), which differ in inherited characteristics, such as external appearance, personality traits, susceptibility to particular diseases, drug responses, and equilibrium among internal organ functions. According to SCM, herbs that belong to a certain constitution cannot be used in patients with other constitutions; otherwise, this practice may result in no effect or in an adverse effect. Thus, the diagnosis of SC type is the most crucial step in SCM practice. The diagnosis, however, tends to be subjective due to a lack of quantitative standards for SC diagnosis. We have attempted to make the diagnosis method as objective as possible by basing it on an analysis of quantitative data from various Oriental medical clinics. Four individual diagnostic models were developed with multinomial logistic regression based on face, body shape, voice, and questionnaire responses. Inspired by SCM practitioners' holistic diagnostic processes, an integrated diagnostic model was then proposed by combining the four individual models. The diagnostic accuracies in the test set, after the four individual models had been integrated into a single model, improved to 64.0% and 55.2% in the male and female patient groups, respectively. Using a cut-off value for the integrated SC score, such as 1.6, the accuracies increased by 14.7% in male patients and by 4.6% in female patients, which showed that a higher integrated SC score corresponded to a higher diagnostic accuracy. This study represents the first trial of integrating the objectification of SC diagnosis based on quantitative data and SCM practitioners' holistic diagnostic processes. Although the diagnostic accuracy was not great, it is noted that the proposed diagnostic model represents common rules among practitioners who have various points of view. Our results are expected to contribute as a desirable research guide for objective diagnosis in traditional medicine, as well as to contribute to the precise diagnosis of SC types in an objective manner in clinical practice.

Model of critical diagnostic reasoning: achieving expert clinician performance.

PubMed

Harjai, Prashant Kumar; Tiwari, Ruby

2009-01-01

Diagnostic reasoning refers to the analytical processes used to determine patient health problems. While the education curriculum and health care system focus on training nurse clinicians to accurately recognize and rescue clinical situations, assessments of non-expert nurses have yielded less than satisfactory data on diagnostic competency. The contrast between the expert and non-expert nurse clinician raises the important question of how differences in thinking may contribute to a large divergence in accurate diagnostic reasoning. This article recognizes superior organization of one's knowledge base, using prototypes, and quick retrieval of pertinent information, using similarity recognition as two reasons for the expert's superior diagnostic performance. A model of critical diagnostic reasoning, using prototypes and similarity recognition, is proposed and elucidated using case studies. This model serves as a starting point toward bridging the gap between clinical data and accurate problem identification, verification, and management while providing a structure for a knowledge exchange between expert and non-expert clinicians.

A Review of Diagnostic Techniques for ISHM Applications

NASA Technical Reports Server (NTRS)

Patterson-Hine, Ann; Biswas, Gautam; Aaseng, Gordon; Narasimhan, Sriam; Pattipati, Krishna

2005-01-01

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.

Applications of Diagnostic Classification Models: A Literature Review and Critical Commentary

ERIC Educational Resources Information Center

Sessoms, John; Henson, Robert A.

2018-01-01

Diagnostic classification models (DCMs) classify examinees based on the skills they have mastered given their test performance. This classification enables targeted feedback that can inform remedial instruction. Unfortunately, applications of DCMs have been criticized (e.g., no validity support). Generally, these evaluations have been brief and…

Diagnostic Teaching of the Language Arts.

ERIC Educational Resources Information Center

Burns, Paul C.

This book is based on the premise that learning can best be facilitated when the teacher takes a diagnostic view of the instructional process. To further this end, each chapter contains materials, models, and techniques designed to implement diagnostic teaching in the language arts program. The seven chapters are "Foundations for Diagnostic…

A Hybrid Stochastic-Neuro-Fuzzy Model-Based System for In-Flight Gas Turbine Engine Diagnostics

DTIC Science & Technology

2001-04-05

Margin (ADM) and (ii) Fault Detection Margin (FDM). Key Words: ANFIS, Engine Health Monitoring , Gas Path Analysis, and Stochastic Analysis Adaptive Network...The paper illustrates the application of a hybrid Stochastic- Fuzzy -Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both...operational history monitored on-line by the engine health management (EHM) system. To capture the complex functional relationships between different

A Framework to Debug Diagnostic Matrices

NASA Technical Reports Server (NTRS)

Kodal, Anuradha; Robinson, Peter; Patterson-Hine, Ann

2013-01-01

Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix) gleaned from physical models. But, sometimes, this may not be coherent to obtain high diagnostic performance. In such a case, it is important to modify this D-matrix based on knowledge obtained from other sources such as time-series data stream (simulated or maintenance data) within the context of a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper logic considering least expensive solution first. This iterative procedure includes conditions ranging from modifying 0s and 1s in the matrix, or adding/removing the rows (failure sources) columns (tests). We will experiment this framework on datasets from DX challenge 2009.

Simulation of light transport in arthritic- and non-arthritic human fingers

NASA Astrophysics Data System (ADS)

Milanic, Matija; Paluchowski, Lukasz A.; Randeberg, Lise L.

2014-03-01

Rheumatoid arthritis is a disease that frequently leads to joint destruction. It has high incidence rates worldwide, and the disease significantly reduces patient's quality of life due to pain, swelling and stiffness of the affected joints. Early diagnosis is necessary to improve course of the disease, therefore sensitive and accurate diagnostic tools are required. Optical imaging techniques have capability for early diagnosis and monitoring of arthritis. As compared to conventional diagnostic techniques optical technique is a noninvasive, noncontact and fast way of collecting diagnostic information. However, a realistic model of light transport in human joints is needed for understanding and developing of such optical diagnostic tools. The aim of this study is to develop a 3D numerical model of light transport in a human finger. The model will guide development of a hyperspectral imaging (HSI) diagnostic modality for arthritis in human fingers. The implemented human finger geometry is based on anatomical data. Optical data of finger tissues are adjusted to represent either an arthritic or an unaffected finger. The geometry and optical data serve as input into a 3D Monte Carlo method, which calculate diffuse reflectance, transmittance and absorbed energy distributions. The parameters of the model are optimized based on HIS-measurements of human fingers. The presented model serves as an important tool for understanding and development of HSI as an arthritis diagnostic modality. Yet, it can be applied to other optical techniques and finger diseases.

PROcess Based Diagnostics PROBE

NASA Technical Reports Server (NTRS)

Clune, T.; Schmidt, G.; Kuo, K.; Bauer, M.; Oloso, H.

2013-01-01

Many of the aspects of the climate system that are of the greatest interest (e.g., the sensitivity of the system to external forcings) are emergent properties that arise via the complex interplay between disparate processes. This is also true for climate models most diagnostics are not a function of an isolated portion of source code, but rather are affected by multiple components and procedures. Thus any model-observation mismatch is hard to attribute to any specific piece of code or imperfection in a specific model assumption. An alternative approach is to identify diagnostics that are more closely tied to specific processes -- implying that if a mismatch is found, it should be much easier to identify and address specific algorithmic choices that will improve the simulation. However, this approach requires looking at model output and observational data in a more sophisticated way than the more traditional production of monthly or annual mean quantities. The data must instead be filtered in time and space for examples of the specific process being targeted.We are developing a data analysis environment called PROcess-Based Explorer (PROBE) that seeks to enable efficient and systematic computation of process-based diagnostics on very large sets of data. In this environment, investigators can define arbitrarily complex filters and then seamlessly perform computations in parallel on the filtered output from their model. The same analysis can be performed on additional related data sets (e.g., reanalyses) thereby enabling routine comparisons between model and observational data. PROBE also incorporates workflow technology to automatically update computed diagnostics for subsequent executions of a model. In this presentation, we will discuss the design and current status of PROBE as well as share results from some preliminary use cases.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

Cancer.gov

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation Value-Added Product

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

Zhang, Chengzhu; Xie, Shaocheng

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstratedmore » in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.« less

Case-Deletion Diagnostics for Nonlinear Structural Equation Models

ERIC Educational Resources Information Center

Lee, Sik-Yum; Lu, Bin

2003-01-01

In this article, a case-deletion procedure is proposed to detect influential observations in a nonlinear structural equation model. The key idea is to develop the diagnostic measures based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. An one-step pseudo approximation is proposed to reduce the…

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations.

Breast tumor malignancy modelling using evolutionary neural logic networks.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Panagi, Georgia; Panourgias, Evangelia

2006-01-01

The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff. Data were properly encoded in a computer database and accordingly, various alternative modelling techniques were applied on them, in an attempt to form diagnostic models. Previous methods included standard optimisation techniques, as well as artificial intelligence approaches, in a way that a variety of related publications exists in modern literature on the subject. In this report, a hybrid computational intelligence approach is suggested, which effectively combines modern mathematical logic principles, neural computation and genetic programming in an effective manner. The approach proves promising either in terms of diagnostic accuracy and generalization capabilities, or in terms of comprehensibility and practical importance for the related medical staff.

The road map towards providing a robust Raman spectroscopy-based cancer diagnostic platform and integration into clinic

NASA Astrophysics Data System (ADS)

Lau, Katherine; Isabelle, Martin; Lloyd, Gavin R.; Old, Oliver; Shepherd, Neil; Bell, Ian M.; Dorney, Jennifer; Lewis, Aaran; Gaifulina, Riana; Rodriguez-Justo, Manuel; Kendall, Catherine; Stone, Nicolas; Thomas, Geraint; Reece, David

2016-03-01

Despite the demonstrated potential as an accurate cancer diagnostic tool, Raman spectroscopy (RS) is yet to be adopted by the clinic for histopathology reviews. The Stratified Medicine through Advanced Raman Technologies (SMART) consortium has begun to address some of the hurdles in its adoption for cancer diagnosis. These hurdles include awareness and acceptance of the technology, practicality of integration into the histopathology workflow, data reproducibility and availability of transferrable models. We have formed a consortium, in joint efforts, to develop optimised protocols for tissue sample preparation, data collection and analysis. These protocols will be supported by provision of suitable hardware and software tools to allow statistically sound classification models to be built and transferred for use on different systems. In addition, we are building a validated gastrointestinal (GI) cancers model, which can be trialled as part of the histopathology workflow at hospitals, and a classification tool. At the end of the project, we aim to deliver a robust Raman based diagnostic platform to enable clinical researchers to stage cancer, define tumour margin, build cancer diagnostic models and discover novel disease bio markers.

Artificial neural networks in mammography interpretation and diagnostic decision making.

PubMed

Ayer, Turgay; Chen, Qiushi; Burnside, Elizabeth S

2013-01-01

Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.

Method of Testing and Predicting Failures of Electronic Mechanical Systems

NASA Technical Reports Server (NTRS)

Iverson, David L.; Patterson-Hine, Frances A.

1996-01-01

A method employing a knowledge base of human expertise comprising a reliability model analysis implemented for diagnostic routines is disclosed. The reliability analysis comprises digraph models that determine target events created by hardware failures human actions, and other factors affecting the system operation. The reliability analysis contains a wealth of human expertise information that is used to build automatic diagnostic routines and which provides a knowledge base that can be used to solve other artificial intelligence problems.

Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

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

Voisin, Sophie; Pinto, Frank M; Morin-Ducote, Garnetta

2013-01-01

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADsmore » images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.« less

A Diagnostic Model for Impending Death in Cancer Patients: Preliminary Report

PubMed Central

Hui, David; Hess, Kenneth; dos Santos, Renata; Chisholm, Gary; Bruera, Eduardo

2015-01-01

Background We recently identified several highly specific bedside physical signs associated with impending death within 3 days among patients with advanced cancer. In this study, we developed and assessed a diagnostic model for impending death based on these physical signs. Methods We systematically documented 62 physical signs every 12 hours from admission to death or discharge in 357 patients with advanced cancer admitted to acute palliative care units (APCUs) at two tertiary care cancer centers. We used recursive partitioning analysis (RPA) to develop a prediction model for impending death in 3 days using admission data. We validated the model with 5 iterations of 10-fold cross-validation, and also applied the model to APCU days 2/3/4/5/6. Results Among 322/357 (90%) patients with complete data for all signs, the 3-day mortality was 24% on admission. The final model was based on 2 variables (palliative performance scale [PPS] and drooping of nasolabial fold) and had 4 terminal leaves: PPS≤20% and drooping of nasolabial fold present, PPS≤20% and drooping of nasolabial fold absent, PPS 30–60% and PPS ≥ 70%, with 3-day mortality of 94%, 42%, 16% and 3%, respectively. The diagnostic accuracy was 81% for the original tree, 80% for cross-validation, and 79%–84% for subsequent APCU days. Conclusion(s) We developed a diagnostic model for impending death within 3 days based on 2 objective bedside physical signs. This model was applicable to both APCU admission and subsequent days. Upon further external validation, this model may help clinicians to formulate the diagnosis of impending death. PMID:26218612

Specialist integrated haematological malignancy diagnostic services: an Activity Based Cost (ABC) analysis of a networked laboratory service model.

PubMed

Dalley, C; Basarir, H; Wright, J G; Fernando, M; Pearson, D; Ward, S E; Thokula, P; Krishnankutty, A; Wilson, G; Dalton, A; Talley, P; Barnett, D; Hughes, D; Porter, N R; Reilly, J T; Snowden, J A

2015-04-01

Specialist Integrated Haematological Malignancy Diagnostic Services (SIHMDS) were introduced as a standard of care within the UK National Health Service to reduce diagnostic error and improve clinical outcomes. Two broad models of service delivery have become established: 'co-located' services operating from a single-site and 'networked' services, with geographically separated laboratories linked by common management and information systems. Detailed systematic cost analysis has never been published on any established SIHMDS model. We used Activity Based Costing (ABC) to construct a cost model for our regional 'networked' SIHMDS covering a two-million population based on activity in 2011. Overall estimated annual running costs were £1 056 260 per annum (£733 400 excluding consultant costs), with individual running costs for diagnosis, staging, disease monitoring and end of treatment assessment components of £723 138, £55 302, £184 152 and £94 134 per annum, respectively. The cost distribution by department was 28.5% for haematology, 29.5% for histopathology and 42% for genetics laboratories. Costs of the diagnostic pathways varied considerably; pathways for myelodysplastic syndromes and lymphoma were the most expensive and the pathways for essential thrombocythaemia and polycythaemia vera being the least. ABC analysis enables estimation of running costs of a SIHMDS model comprised of 'networked' laboratories. Similar cost analyses for other SIHMDS models covering varying populations are warranted to optimise quality and cost-effectiveness in delivery of modern haemato-oncology diagnostic services in the UK as well as internationally. 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.

Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label.

PubMed

Xu, Jin; Xu, Zhao-Xia; Lu, Ping; Guo, Rui; Yan, Hai-Xia; Xu, Wen-Jie; Wang, Yi-Qin; Xia, Chun-Ming

2016-11-01

To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.

The Impact of Measurement Noise in GPA Diagnostic Analysis of a Gas Turbine Engine

NASA Astrophysics Data System (ADS)

Ntantis, Efstratios L.; Li, Y. G.

2013-12-01

The performance diagnostic analysis of a gas turbine is accomplished by estimating a set of internal engine health parameters from available sensor measurements. No physical measuring instruments however can ever completely eliminate the presence of measurement uncertainties. Sensor measurements are often distorted by noise and bias leading to inaccurate estimation results. This paper explores the impact of measurement noise on Gas Turbine GPA analysis. The analysis is demonstrated with a test case where gas turbine performance simulation and diagnostics code TURBOMATCH is used to build a performance model of a model engine similar to Rolls-Royce Trent 500 turbofan engine, and carry out the diagnostic analysis with the presence of different levels of measurement noise. Conclusively, to improve the reliability of the diagnostic results, a statistical analysis of the data scattering caused by sensor uncertainties is made. The diagnostic tool used to deal with the statistical analysis of measurement noise impact is a model-based method utilizing a non-linear GPA.

Addressing the Real-World Challenges in the Development of Propulsion IVHM Technology Experiment (PITEX)

NASA Technical Reports Server (NTRS)

Maul, William A.; Chicatelli, Amy; Fulton, Christopher E.; Balaban, Edward; Sweet, Adam; Hayden, Sandra Claire; Bajwa, Anupa

2005-01-01

The Propulsion IVHM Technology Experiment (PITEX) has been an on-going research effort conducted over several years. PITEX has developed and applied a model-based diagnostic system for the main propulsion system of the X-34 reusable launch vehicle, a space-launch technology demonstrator. The application was simulation-based using detailed models of the propulsion subsystem to generate nominal and failure scenarios during captive carry, which is the most safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion System (MPS) was performed, these simulated data were used to verify and validate the software system. Advanced diagnostic and signal processing algorithms were developed and tested in real-time on flight-like hardware. In an attempt to expose potential performance problems, these PITEX algorithms were subject to numerous real-world effects in the simulated data including noise, sensor resolution, command/valve talkback information, and nominal build variations. The current research has demonstrated the potential benefits of model-based diagnostics, defined the performance metrics required to evaluate the diagnostic system, and studied the impact of real-world challenges encountered when monitoring propulsion subsystems.

Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis.

PubMed

Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G

2018-03-01

Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.

Structure induction in diagnostic causal reasoning.

PubMed

Meder, Björn; Mayrhofer, Ralf; Waldmann, Michael R

2014-07-01

Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences from an effect to its cause should reflect solely the empirically observed conditional probability of cause given effect. We argue against this assumption, as it neglects alternative causal structures that may have generated the sample data. Our structure induction model of diagnostic reasoning takes into account the uncertainty regarding the underlying causal structure. A key prediction of the model is that diagnostic judgments should not only reflect the empirical probability of cause given effect but should also depend on the reasoner's beliefs about the existence and strength of the link between cause and effect. We confirmed this prediction in 2 studies and showed that our theory better accounts for human judgments than alternative theories of diagnostic reasoning. Overall, our findings support the view that in diagnostic reasoning people go "beyond the information given" and use the available data to make inferences on the (unobserved) causal rather than on the (observed) data level. (c) 2014 APA, all rights reserved.

Validation of a Cognitive Diagnostic Model across Multiple Forms of a Reading Comprehension Assessment

ERIC Educational Resources Information Center

Clark, Amy K.

2013-01-01

The present study sought to fit a cognitive diagnostic model (CDM) across multiple forms of a passage-based reading comprehension assessment using the attribute hierarchy method. Previous research on CDMs for reading comprehension assessments served as a basis for the attributes in the hierarchy. The two attribute hierarchies were fit to data from…

Uncertainty evaluation of dead zone of diagnostic ultrasound equipment

NASA Astrophysics Data System (ADS)

Souza, R. M.; Alvarenga, A. V.; Braz, D. S.; Petrella, L. I.; Costa-Felix, R. P. B.

2016-07-01

This paper presents a model for evaluating measurement uncertainty of a feature used in the assessment of ultrasound images: dead zone. The dead zone was measured by two technicians of the INMETRO's Laboratory of Ultrasound using a phantom and following the standard IEC/TS 61390. The uncertainty model was proposed based on the Guide to the Expression of Uncertainty in Measurement. For the tested equipment, results indicate a dead zone of 1.01 mm, and based on the proposed model, the expanded uncertainty was 0.17 mm. The proposed uncertainty model contributes as a novel way for metrological evaluation of diagnostic imaging by ultrasound.

Development of a Diagnostic and Remedial Learning System Based on an Enhanced Concept--Effect Model

ERIC Educational Resources Information Center

Panjaburees, Patcharin; Triampo, Wannapong; Hwang, Gwo-Jen; Chuedoung, Meechoke; Triampo, Darapond

2013-01-01

With the rapid advances in computer technology during recent years, researchers have demonstrated the pivotal influences of computer-assisted diagnostic systems on student learning performance improvement. This research aims to develop a Diagnostic and Remedial Learning System (DRLS) for an algebra course in a Thai lower secondary school context…

Bayes' theorem application in the measure information diagnostic value assessment

NASA Astrophysics Data System (ADS)

Orzechowski, Piotr D.; Makal, Jaroslaw; Nazarkiewicz, Andrzej

2006-03-01

The paper presents Bayesian method application in the measure information diagnostic value assessment that is used in the computer-aided diagnosis system. The computer system described here has been created basing on the Bayesian Network and is used in Benign Prostatic Hyperplasia (BPH) diagnosis. The graphic diagnostic model enables to juxtapose experts' knowledge with data.

A fast Monte Carlo EM algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with AGC

PubMed Central

Kang, Le; Carter, Randy; Darcy, Kathleen; Kauderer, James; Liao, Shu-Yuan

2013-01-01

In this article we use a latent class model (LCM) with prevalence modeled as a function of covariates to assess diagnostic test accuracy in situations where the true disease status is not observed, but observations on three or more conditionally independent diagnostic tests are available. A fast Monte Carlo EM (MCEM) algorithm with binary (disease) diagnostic data is implemented to estimate parameters of interest; namely, sensitivity, specificity, and prevalence of the disease as a function of covariates. To obtain standard errors for confidence interval construction of estimated parameters, the missing information principle is applied to adjust information matrix estimates. We compare the adjusted information matrix based standard error estimates with the bootstrap standard error estimates both obtained using the fast MCEM algorithm through an extensive Monte Carlo study. Simulation demonstrates that the adjusted information matrix approach estimates the standard error similarly with the bootstrap methods under certain scenarios. The bootstrap percentile intervals have satisfactory coverage probabilities. We then apply the LCM analysis to a real data set of 122 subjects from a Gynecologic Oncology Group (GOG) study of significant cervical lesion (S-CL) diagnosis in women with atypical glandular cells of undetermined significance (AGC) to compare the diagnostic accuracy of a histology-based evaluation, a CA-IX biomarker-based test and a human papillomavirus (HPV) DNA test. PMID:24163493

Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

PubMed

Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

2007-12-15

Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

Climate Model Diagnostic Analyzer

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Pan, Lei; Zhai, Chengxing; Tang, Benyang; Kubar, Terry; Zhang, Zia; Wang, Wei

2015-01-01

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

Silicon drift detector based X-ray spectroscopy diagnostic system for the study of non-thermal electrons at Aditya tokamak.

PubMed

Purohit, S; Joisa, Y S; Raval, J V; Ghosh, J; Tanna, R; Shukla, B K; Bhatt, S B

2014-11-01

Silicon drift detector based X-ray spectrometer diagnostic was developed to study the non-thermal electron for Aditya tokamak plasma. The diagnostic was mounted on a radial mid plane port at the Aditya. The objective of diagnostic includes the estimation of the non-thermal electron temperature for the ohmically heated plasma. Bi-Maxwellian plasma model was adopted for the temperature estimation. Along with that the study of high Z impurity line radiation from the ECR pre-ionization experiments was also aimed. The performance and first experimental results from the new X-ray spectrometer system are presented.

Creating of structure of facts for the knowledge base of an expert system for wind power plant's equipment diagnosis

NASA Astrophysics Data System (ADS)

Duer, Stanisław; Wrzesień, Paweł; Duer, Radosław

2017-10-01

This article describes rules and conditions for making a structure (a set) of facts for an expert knowledge base of the intelligent system to diagnose Wind Power Plants' equipment. Considering particular operational conditions of a technical object, that is a set of Wind Power Plant's equipment, this is a significant issue. A structural model of Wind Power Plant's equipment is developed. Based on that, a functional - diagnostic model of Wind Power Plant's equipment is elaborated. That model is a basis for determining primary elements of the object structure, as well as for interpreting a set of diagnostic signals and their reference signals. The key content of this paper is a description of rules for building of facts on the basis of developed analytical dependence. According to facts, their dependence is described by rules for transferring of a set of pieces of diagnostic information into a specific set of facts. The article consists of four chapters that concern particular issues on the subject.

Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

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

Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank

2013-10-15

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS imagesmore » features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.« less

A diagnostic model to estimate winds and small-scale drag from Mars Observer PMIRR data

NASA Technical Reports Server (NTRS)

Barnes, J. R.

1993-01-01

Theoretical and modeling studies indicate that small-scale drag due to breaking gravity waves is likely to be of considerable importance for the circulation in the middle atmospheric region (approximately 40-100 km altitude) on Mars. Recent earth-based spectroscopic observations have provided evidence for the existence of circulation features, in particular, a warm winter polar region, associated with gravity wave drag. Since the Mars Observer PMIRR experiment will obtain temperature profiles extending from the surface up to about 80 km altitude, it will be extensively sampling middle atmospheric regions in which gravity wave drag may play a dominant role. Estimating the drag then becomes crucial to the estimation of the atmospheric winds from the PMIRR-observed temperatures. An interative diagnostic model based upon one previously developed and tested with earth satellite temperature data will be applied to the PMIRR measurements to produce estimates of the small-scale zonal drag and three-dimensional wind fields in the Mars middle atmosphere. This model is based on the primitive equations, and can allow for time dependence (the time tendencies used may be based upon those computed in a Fast Fourier Mapping procedure). The small-scale zonal drag is estimated as the residual in the zonal momentum equation; the horizontal winds having first been estimated from the meridional momentum equation and the continuity equation. The scheme estimates the vertical motions from the thermodynamic equation, and thus needs estimates of the diabatic heating based upon the observed temperatures. The latter will be generated using a radiative model. It is hoped that the diagnostic scheme will be able to produce good estimates of the zonal gravity wave drag in the Mars middle atmosphere, estimates that can then be used in other diagnostic or assimilation efforts, as well as more theoretical studies.

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological review of health technology assessments.

PubMed

Shinkins, Bethany; Yang, Yaling; Abel, Lucy; Fanshawe, Thomas R

2017-04-14

Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests.

Nonparametric predictive inference for combining diagnostic tests with parametric copula

NASA Astrophysics Data System (ADS)

Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.

2017-09-01

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.

Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

PubMed Central

Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

1996-01-01

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

LASERS IN MEDICINE: Laser diagnostics of biofractals

NASA Astrophysics Data System (ADS)

Ushenko, A. G.

1999-12-01

An optical approach to the problem of modelling and diagnostics of the structures of biofractal formations was considered in relation to human bone tissue. A model was proposed for the optical properties of this tissue, including three levels of fractal organisation: microcrystalline, macrocrystalline, and architectural. The studies were based on laser coherent polarimetry ensuring the retrieval of the fullest information about the optical and polarisation properties of bone tissue. A method was developed for contactless noninvasive diagnostics of the orientational and mineralogical structure of bone tissue considered as a biofractal.

A diagnostic model for impending death in cancer patients: Preliminary report.

PubMed

Hui, David; Hess, Kenneth; dos Santos, Renata; Chisholm, Gary; Bruera, Eduardo

2015-11-01

Several highly specific bedside physical signs associated with impending death within 3 days for patients with advanced cancer were recently identified. A diagnostic model for impending death based on these physical signs was developed and assessed. Sixty-two physical signs were systematically documented every 12 hours from admission to death or discharge for 357 patients with advanced cancer who were admitted to acute palliative care units (APCUs) at 2 tertiary care cancer centers. Recursive partitioning analysis was used to develop a prediction model for impending death within 3 days with admission data. The model was validated with 5 iterations of 10-fold cross-validation, and the model was also applied to APCU days 2 to 6. For the 322 of 357 patients (90%) with complete data for all signs, the 3-day mortality rate was 24% on admission. The final model was based on 2 variables (Palliative Performance Scale [PPS] and drooping of nasolabial folds) and had 4 terminal leaves: PPS score ≤ 20% and drooping of nasolabial folds present, PPS score ≤ 20% and drooping of nasolabial folds absent, PPS score of 30% to 60%, and PPS score ≥ 70%. The 3-day mortality rates were 94%, 42%, 16%, and 3%, respectively. The diagnostic accuracy was 81% for the original tree, 80% for cross-validation, and 79% to 84% for subsequent APCU days. Based on 2 objective bedside physical signs, a diagnostic model was developed for impending death within 3 days. This model was applicable to both APCU admission and subsequent days. Upon further external validation, this model may help clinicians to formulate the diagnosis of impending death. © 2015 American Cancer Society.

Diagnosis of pulmonary hypertension from magnetic resonance imaging-based computational models and decision tree analysis.

PubMed

Lungu, Angela; Swift, Andrew J; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M

2016-06-01

Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH.

2007 Beyond SBIR Phase II: Bringing Technology Edge to the Warfighter

DTIC Science & Technology

2007-08-23

Systems Trade-Off Analysis and Optimization Verification and Validation On-Board Diagnostics and Self - healing Security and Anti-Tampering Rapid...verification; Safety and reliability analysis of flight and mission critical systems On-Board Diagnostics and Self - Healing Model-based monitoring and... self - healing On-board diagnostics and self - healing ; Autonomic computing; Network intrusion detection and prevention Anti-Tampering and Trust

A signal-detection-based diagnostic-feature-detection model of eyewitness identification.

PubMed

Wixted, John T; Mickes, Laura

2014-04-01

The theoretical understanding of eyewitness identifications made from a police lineup has long been guided by the distinction between absolute and relative decision strategies. In addition, the accuracy of identifications associated with different eyewitness memory procedures has long been evaluated using measures like the diagnosticity ratio (the correct identification rate divided by the false identification rate). Framed in terms of signal-detection theory, both the absolute/relative distinction and the diagnosticity ratio are mainly relevant to response bias while remaining silent about the key issue of diagnostic accuracy, or discriminability (i.e., the ability to tell the difference between innocent and guilty suspects in a lineup). Here, we propose a signal-detection-based model of eyewitness identification, one that encourages the use of (and helps to conceptualize) receiver operating characteristic (ROC) analysis to measure discriminability. Recent ROC analyses indicate that the simultaneous presentation of faces in a lineup yields higher discriminability than the presentation of faces in isolation, and we propose a diagnostic feature-detection hypothesis to account for that result. According to this hypothesis, the simultaneous presentation of faces allows the eyewitness to appreciate that certain facial features (viz., those that are shared by everyone in the lineup) are non-diagnostic of guilt. To the extent that those non-diagnostic features are discounted in favor of potentially more diagnostic features, the ability to discriminate innocent from guilty suspects will be enhanced.

The clinical performance evaluation of novel protein chips for eleven biomarkers detection and the diagnostic model study.

PubMed

Luo, Yuan; Zhu, Xu; Zhang, Pengjun; Shen, Qian; Wang, Zi; Wen, Xinyu; Wang, Ling; Gao, Jing; Dong, Jin; Yang, Caie; Wu, Tangming; Zhu, Zheng; Tian, Yaping

2015-01-01

We aimed to develop and validate two novel protein chips, which are based on microarray chemiluminescence immunoassay and can simultaneously detected 11 biomarkers, and then to evaluate their clinical diagnostic value by comparing with the traditional methods. Protein chips were evaluated for limit of detection, specificity, common interferences, linearity, precision and accuracy. 11 biomarkers were simultaneously detected by traditional methods and protein chips in 3683 samples, which included 1723 cancer patients, 1798 benign diseases patients and 162 healthy controls. After assay validation, protein chips demonstrated high sensitivity, high specificity, good linearity, low imprecision and were free of common interferences. Compared with the traditional methods, protein chips have good correlation in the detection of all the 13 kinds of biomarkers (r≥0.935, P<0.001). For specific cancer detection, there were no statistically significant differences between the traditional method and novel protein chips, except that male protein chip showed significantly better diagnostic value on NSE detection (P=0.004) but significantly worse value on pro-GRP detection (P=0.012), female chip showed significantly better diagnostic value on pro-GRP detection (P=0.005). Furthermore, both male and female multivariate diagnostic models had significantly better diagnostic value than single detection of PGI, PG II, pro-GRP, NSE and CA125 (P<0.05). In addition, male models had significantly better diagnostic value than single CA199 and free-PSA (P<0.05), while female models observed significantly better diagnostic value than single CA724 and β-HCG (P<0.05). For total disease or cancer detection, the AUC of multivariate logistic regression for the male and female disease detection was 0.981 (95% CI: 0.975-0.987) and 0.836 (95% CI: 0.798-0.874), respectively. While, that for total cancer detection was 0.691 (95% CI: 0.666-0.717) and 0.753 (95% CI: 0.731-0.775), respectively. The new designed protein chips are simple, multiplex and reliable clinical assays and the multi-parameter diagnostic models based on them could significantly improve their clinical performance.

The clinical performance evaluation of novel protein chips for eleven biomarkers detection and the diagnostic model study

PubMed Central

Luo, Yuan; Zhu, Xu; Zhang, Pengjun; Shen, Qian; Wang, Zi; Wen, Xinyu; Wang, Ling; Gao, Jing; Dong, Jin; Yang, Caie; Wu, Tangming; Zhu, Zheng; Tian, Yaping

2015-01-01

We aimed to develop and validate two novel protein chips, which are based on microarray chemiluminescence immunoassay and can simultaneously detected 11 biomarkers, and then to evaluate their clinical diagnostic value by comparing with the traditional methods. Protein chips were evaluated for limit of detection, specificity, common interferences, linearity, precision and accuracy. 11 biomarkers were simultaneously detected by traditional methods and protein chips in 3683 samples, which included 1723 cancer patients, 1798 benign diseases patients and 162 healthy controls. After assay validation, protein chips demonstrated high sensitivity, high specificity, good linearity, low imprecision and were free of common interferences. Compared with the traditional methods, protein chips have good correlation in the detection of all the 13 kinds of biomarkers (r≥0.935, P<0.001). For specific cancer detection, there were no statistically significant differences between the traditional method and novel protein chips, except that male protein chip showed significantly better diagnostic value on NSE detection (P=0.004) but significantly worse value on pro-GRP detection (P=0.012), female chip showed significantly better diagnostic value on pro-GRP detection (P=0.005). Furthermore, both male and female multivariate diagnostic models had significantly better diagnostic value than single detection of PGI, PG II, pro-GRP, NSE and CA125 (P<0.05). In addition, male models had significantly better diagnostic value than single CA199 and free-PSA (P<0.05), while female models observed significantly better diagnostic value than single CA724 and β-HCG (P<0.05). For total disease or cancer detection, the AUC of multivariate logistic regression for the male and female disease detection was 0.981 (95% CI: 0.975-0.987) and 0.836 (95% CI: 0.798-0.874), respectively. While, that for total cancer detection was 0.691 (95% CI: 0.666-0.717) and 0.753 (95% CI: 0.731-0.775), respectively. The new designed protein chips are simple, multiplex and reliable clinical assays and the multi-parameter diagnostic models based on them could significantly improve their clinical performance. PMID:26884957

A dynamic model of Flo-Tron flowmeters

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

Cichy, M.; Bossio, R.B.

1984-08-01

The optimization of diagnostic equipment for reciprocating both internal and external combustion engines are deeply affected by suitability of simulation models. One of the most attractive and difficult diagnostic aspect deals with the fuel instantaneous mass flow rate measurement. A new model of the dynamic simulation of the Flo-Tron flowmeter, whose working principle is based on the hydraulic Wheatstone's bridge is then presented, dealing with the state space equations and bond-graph method.

Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

PubMed Central

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

PubMed

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

Consequences of Base Time for Redundant Signals Experiments

PubMed Central

Townsend, James T.; Honey, Christopher

2007-01-01

We report analytical and computational investigations into the effects of base time on the diagnosticity of two popular theoretical tools in the redundant signals literature: (1) the race model inequality and (2) the capacity coefficient. We show analytically and without distributional assumptions that the presence of base time decreases the sensitivity of both of these measures to model violations. We further use simulations to investigate the statistical power model selection tools based on the race model inequality, both with and without base time. Base time decreases statistical power, and biases the race model test toward conservatism. The magnitude of this biasing effect increases as we increase the proportion of total reaction time variance contributed by base time. We marshal empirical evidence to suggest that the proportion of reaction time variance contributed by base time is relatively small, and that the effects of base time on the diagnosticity of our model-selection tools are therefore likely to be minor. However, uncertainty remains concerning the magnitude and even the definition of base time. Experimentalists should continue to be alert to situations in which base time may contribute a large proportion of the total reaction time variance. PMID:18670591

Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

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

Tourassi, Georgia; Voisin, Sophie; Paquit, Vincent C

2013-01-01

Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By poolingmore » the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.« less

Target prioritization and strategy selection for active case-finding of pulmonary tuberculosis: a tool to support country-level project planning.

PubMed

Nishikiori, Nobuyuki; Van Weezenbeek, Catharina

2013-02-02

Despite the progress made in the past decade, tuberculosis (TB) control still faces significant challenges. In many countries with declining TB incidence, the disease tends to concentrate in vulnerable populations that often have limited access to health care. In light of the limitations of the current case-finding approach and the global urgency to improve case detection, active case-finding (ACF) has been suggested as an important complementary strategy to accelerate tuberculosis control especially among high-risk populations. The present exercise aims to develop a model that can be used for county-level project planning. A simple deterministic model was developed to calculate the number of estimated TB cases diagnosed and the associated costs of diagnosis. The model was designed to compare cost-effectiveness parameters, such as the cost per case detected, for different diagnostic algorithms when they are applied to different risk populations. The model was transformed into a web-based tool that can support national TB programmes and civil society partners in designing ACF activities. According to the model output, tuberculosis active case-finding can be a costly endeavor, depending on the target population and the diagnostic strategy. The analysis suggests the following: (1) Active case-finding activities are cost-effective only if the tuberculosis prevalence among the target population is high. (2) Extensive diagnostic methods (e.g. X-ray screening for the entire group, use of sputum culture or molecular diagnostics) can be applied only to very high-risk groups such as TB contacts, prisoners or people living with human immunodeficiency virus (HIV) infection. (3) Basic diagnostic approaches such as TB symptom screening are always applicable although the diagnostic yield is very limited. The cost-effectiveness parameter was sensitive to local diagnostic costs and the tuberculosis prevalence of target populations. The prioritization of appropriate target populations and careful selection of cost-effective diagnostic strategies are critical prerequisites for rational active case-finding activities. A decision to conduct such activities should be based on the setting-specific cost-effectiveness analysis and programmatic assessment. A web-based tool was developed and is available to support national tuberculosis programmes and partners in the formulation of cost-effective active case-finding activities at the national and subnational levels.

Analysis of the hydrological response of a distributed physically-based model using post-assimilation (EnKF) diagnostics of streamflow and in situ soil moisture observations

NASA Astrophysics Data System (ADS)

Trudel, Mélanie; Leconte, Robert; Paniconi, Claudio

2014-06-01

Data assimilation techniques not only enhance model simulations and forecast, they also provide the opportunity to obtain a diagnostic of both the model and observations used in the assimilation process. In this research, an ensemble Kalman filter was used to assimilate streamflow observations at a basin outlet and at interior locations, as well as soil moisture at two different depths (15 and 45 cm). The simulation model is the distributed physically-based hydrological model CATHY (CATchment HYdrology) and the study site is the Des Anglais watershed, a 690 km2 river basin located in southern Quebec, Canada. Use of Latin hypercube sampling instead of a conventional Monte Carlo method to generate the ensemble reduced the size of the ensemble, and therefore the calculation time. Different post-assimilation diagnostics, based on innovations (observation minus background), analysis residuals (observation minus analysis), and analysis increments (analysis minus background), were used to evaluate assimilation optimality. An important issue in data assimilation is the estimation of error covariance matrices. These diagnostics were also used in a calibration exercise to determine the standard deviation of model parameters, forcing data, and observations that led to optimal assimilations. The analysis of innovations showed a lag between the model forecast and the observation during rainfall events. Assimilation of streamflow observations corrected this discrepancy. Assimilation of outlet streamflow observations improved the Nash-Sutcliffe efficiencies (NSE) between the model forecast (one day) and the observation at both outlet and interior point locations, owing to the structure of the state vector used. However, assimilation of streamflow observations systematically increased the simulated soil moisture values.

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

PubMed

Ray, L R; Townsend, J R; Ramasubramanian, A

2001-01-01

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

[Theoretical grounds of a structural and functional model for quality assurance of radiation diagnostics under conditions of development of the modern health care system in Ukraine].

PubMed

Korop, Oleg A; Lenskykh, Sergiy V

2018-01-01

Introduction: Modern changes in the health care system of Ukraine are focused on financial support in providing medical and diagnostic care to the population and are based on deep and consistent structural and functional transformations. They are aimed at providing adequate quality care, which is the main target function and a principal criterion for operation of health care system. The urgency of this problem is increasing in the context of reforming the health care system and global changes in the governmental financial guarantees for the provision of medical services to the population. The aim of the work is to provide theoretical grounds for a structural and functional model of quality assurance of radiation diagnostics at all levels of medical care given to the population under the current health care reform in Ukraine. Materials and methods: The object of the study is organizing the operation of the radiation diagnostic service; the information is based on the actual data on the characteristics of radiation diagnosis at different levels of medical care provision. Methods of systematic approach, system analysis and structural and functional analysis of the operating system of radiation diagnostics are used. Review: The basis of the quality assurance model is the cyclical process, which includes the stages of the problem identifition, planning of its solution, organization of the system for implementation of decisions, monitoring the quality management process of the radiation diagnostics, and factors influencing the quality of the radiation diagnostics service. These factors include the quality of the structure, process, results, organization of management and control of current processes and the results of radiation diagnostics management. Conclusions: The advantages of the proposed model for ensuring the quality of the radiation diagnostics service are its systemacy and complexity, elimination of identified defects and deficiencies, and achievement of profitability through modern redistribution and use of existing resources of the health care system. The results of adequate service quality management activities in radiation diagnostics are the improvement of organizational and economic principles along with legislative regulation, the implementation of a modern system of radiation diagnostics in the state health care at the national and regional levels, the increase of the accessibility, quality and efficiency of the radiation diagnostics service.

A simplified dynamic model of the T700 turboshaft engine

NASA Technical Reports Server (NTRS)

Duyar, Ahmet; Gu, Zhen; Litt, Jonathan S.

1992-01-01

A simplified open-loop dynamic model of the T700 turboshaft engine, valid within the normal operating range of the engine, is developed. This model is obtained by linking linear state space models obtained at different engine operating points. Each linear model is developed from a detailed nonlinear engine simulation using a multivariable system identification and realization method. The simplified model may be used with a model-based real time diagnostic scheme for fault detection and diagnostics, as well as for open loop engine dynamics studies and closed loop control analysis utilizing a user generated control law.

A connectionist model for diagnostic problem solving

NASA Technical Reports Server (NTRS)

Peng, Yun; Reggia, James A.

1989-01-01

A competition-based connectionist model for solving diagnostic problems is described. The problems considered are computationally difficult in that (1) multiple disorders may occur simultaneously and (2) a global optimum in the space exponential to the total number of possible disorders is sought as a solution. The diagnostic problem is treated as a nonlinear optimization problem, and global optimization criteria are decomposed into local criteria governing node activation updating in the connectionist model. Nodes representing disorders compete with each other to account for each individual manifestation, yet complement each other to account for all manifestations through parallel node interactions. When equilibrium is reached, the network settles into a locally optimal state. Three randomly generated examples of diagnostic problems, each of which has 1024 cases, were tested, and the decomposition plus competition plus resettling approach yielded very high accuracy.

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers.

PubMed

Rantalainen, Mattias; Klevebring, Daniel; Lindberg, Johan; Ivansson, Emma; Rosin, Gustaf; Kis, Lorand; Celebioglu, Fuat; Fredriksson, Irma; Czene, Kamila; Frisell, Jan; Hartman, Johan; Bergh, Jonas; Grönberg, Henrik

2016-11-30

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Li, Shaoxin; Zhang, Yanjiao; Xu, Junfa; Li, Linfang; Zeng, Qiuyao; Lin, Lin; Guo, Zhouyi; Liu, Zhiming; Xiong, Honglian; Liu, Songhao

2014-09-01

This study aims to present a noninvasive prostate cancer screening methods using serum surface-enhanced Raman scattering (SERS) and support vector machine (SVM) techniques through peripheral blood sample. SERS measurements are performed using serum samples from 93 prostate cancer patients and 68 healthy volunteers by silver nanoparticles. Three types of kernel functions including linear, polynomial, and Gaussian radial basis function (RBF) are employed to build SVM diagnostic models for classifying measured SERS spectra. For comparably evaluating the performance of SVM classification models, the standard multivariate statistic analysis method of principal component analysis (PCA) is also applied to classify the same datasets. The study results show that for the RBF kernel SVM diagnostic model, the diagnostic accuracy of 98.1% is acquired, which is superior to the results of 91.3% obtained from PCA methods. The receiver operating characteristic curve of diagnostic models further confirm above research results. This study demonstrates that label-free serum SERS analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive prostate cancer screening.

Diagnosis before treatment: Identifying dairy farmers' determinants for the adoption of sustainable practices in gastrointestinal nematode control.

PubMed

Vande Velde, F; Claerebout, E; Cauberghe, V; Hudders, L; Van Loo, H; Vercruysse, J; Charlier, J

2015-09-15

Anthelmintic resistance is emerging in dairy cattle and this can result in a lack of effective control and production losses. Therefore, sustainable control strategies, such as targeted treatments (TT) and targeted selected treatments (TST), should be adopted by the industry. TT and TST approaches require the use of diagnostic methods to take informed treatment decisions. To understand the factors affecting the farmers' intention to adopt diagnostic methods before implementing anthelmintic drugs ('adoption intention'), a cross-sectional survey was carried out in dairy farms in Belgium (Flanders). A framework was constructed to predict adoption intentions based on two fundamental theories in the field of behavioural psychology and health psychology: the Theory of Planned Behaviour and the Health Belief Model. In the tested model, adoption intentions were predicted based on attitudes towards anthelminthics, attitudes towards diagnostic methods, subjective norms, behavioural control and perceived risk. Structural equation modelling was used for analyses. The model fitted the data well and explained 46% of the variance in adoption intention of diagnostics. The factors 'attitude towards diagnostic methods' and 'subjective norm'; i.e. the influence of significant others, had the strongest, positive influence on adoption intention of diagnostic methods. 'Perceived behavioural control' had a weak, positive effect on intention. Further, 'attitude towards the use of anthelmintic drugs' had a negative effect on adoption intention of the diagnostic methods. This implicates an effect of current behaviour on future adoption, which should be considered in future research. Factors measuring risk perception of anthelmintic resistance; perceived severity and perceived susceptibility, had no effect on the adoption intention of diagnostic methods. The threat of anthelmintic resistance is perceived to be low for dairy herds. The study further did not find any differences in the effects of the predictors for young stock and adult dairy cows. The results of this study can be used to develop communication strategies to advertise sustainable nematode control on dairy farms. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards intelligent diagnostic system employing integration of mathematical and engineering model

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

Isa, Nor Ashidi Mat

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability ofmore » the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.« less

Towards intelligent diagnostic system employing integration of mathematical and engineering model

NASA Astrophysics Data System (ADS)

Isa, Nor Ashidi Mat

2015-05-01

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

Ultra-low-dose computed tomographic angiography with model-based iterative reconstruction compared with standard-dose imaging after endovascular aneurysm repair: a prospective pilot study.

PubMed

Naidu, Sailen G; Kriegshauser, J Scott; Paden, Robert G; He, Miao; Wu, Qing; Hara, Amy K

2014-12-01

An ultra-low-dose radiation protocol reconstructed with model-based iterative reconstruction was compared with our standard-dose protocol. This prospective study evaluated 20 men undergoing surveillance-enhanced computed tomography after endovascular aneurysm repair. All patients underwent standard-dose and ultra-low-dose venous phase imaging; images were compared after reconstruction with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction. Objective measures of aortic contrast attenuation and image noise were averaged. Images were subjectively assessed (1 = worst, 5 = best) for diagnostic confidence, image noise, and vessel sharpness. Aneurysm sac diameter and endoleak detection were compared. Quantitative image noise was 26% less with ultra-low-dose model-based iterative reconstruction than with standard-dose adaptive statistical iterative reconstruction and 58% less than with ultra-low-dose adaptive statistical iterative reconstruction. Average subjective noise scores were not different between ultra-low-dose model-based iterative reconstruction and standard-dose adaptive statistical iterative reconstruction (3.8 vs. 4.0, P = .25). Subjective scores for diagnostic confidence were better with standard-dose adaptive statistical iterative reconstruction than with ultra-low-dose model-based iterative reconstruction (4.4 vs. 4.0, P = .002). Vessel sharpness was decreased with ultra-low-dose model-based iterative reconstruction compared with standard-dose adaptive statistical iterative reconstruction (3.3 vs. 4.1, P < .0001). Ultra-low-dose model-based iterative reconstruction and standard-dose adaptive statistical iterative reconstruction aneurysm sac diameters were not significantly different (4.9 vs. 4.9 cm); concordance for the presence of endoleak was 100% (P < .001). Compared with a standard-dose technique, an ultra-low-dose model-based iterative reconstruction protocol provides comparable image quality and diagnostic assessment at a 73% lower radiation dose.

Automated knowledge generation

NASA Technical Reports Server (NTRS)

Myler, Harley R.; Gonzalez, Avelino J.

1988-01-01

The general objectives of the NASA/UCF Automated Knowledge Generation Project were the development of an intelligent software system that could access CAD design data bases, interpret them, and generate a diagnostic knowledge base in the form of a system model. The initial area of concentration is in the diagnosis of the process control system using the Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. A secondary objective was the study of general problems of automated knowledge generation. A prototype was developed, based on object-oriented language (Flavors).

A Model-based Health Monitoring and Diagnostic System for the UH-60 Helicopter. Appendix D

NASA Technical Reports Server (NTRS)

Patterson-Hine, Ann; Hindson, William; Sanderfer, Dwight; Deb, Somnath; Domagala, Chuck

2001-01-01

Model-based reasoning techniques hold much promise in providing comprehensive monitoring and diagnostics capabilities for complex systems. We are exploring the use of one of these techniques, which utilizes multi-signal modeling and the TEAMS-RT real-time diagnostic engine, on the UH-60 Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) flight research aircraft. We focus on the engine and transmission systems, and acquire sensor data across the 1553 bus as well as by direct analog-to-digital conversion from sensors to the QHuMS (Qualtech health and usage monitoring system) computer. The QHuMS computer uses commercially available components and is rack-mounted in the RASCAL facility. A multi-signal model of the transmission and engine subsystems enables studies of system testability and analysis of the degree of fault isolation available with various instrumentation suites. The model and examples of these analyses will be described and the data architectures enumerated. Flight tests of this system will validate the data architecture and provide real-time flight profiles to be further analyzed in the laboratory.

A Framework for Model-Based Diagnostics and Prognostics of Switched-Mode Power Supplies

DTIC Science & Technology

2014-10-02

system. Some highlights of the work are included but not only limited to the following aspects: first, the methodology is based on electronic ... electronic health management, with the goal of expanding the realm of electronic diagnostics and prognostics. 1. INTRODUCTION Electronic systems such...as electronic controls, onboard computers, communications, navigation and radar perform many critical functions in onboard military and commercial

Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty: STRUCTURAL UNCERTAINTY DIAGNOSTICS

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

Moges, Edom; Demissie, Yonas; Li, Hong-Yi

2016-04-01

In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integratemore » expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.« less

Diagnostic evaluation of distributed physically based model at the REW scale (THREW) using rainfall-runoff event analysis

NASA Astrophysics Data System (ADS)

Tian, F.; Sivapalan, M.; Li, H.; Hu, H.

2007-12-01

The importance of diagnostic analysis of hydrological models is increasingly recognized by the scientific community (M. Sivapalan, et al., 2003; H. V. Gupta, et al., 2007). Model diagnosis refers to model structures and parameters being identified not only by statistical comparison of system state variables and outputs but also by process understanding in a specific watershed. Process understanding can be gained by the analysis of observational data and model results at the specific watershed as well as through regionalization. Although remote sensing technology can provide valuable data about the inputs, state variables, and outputs of the hydrological system, observational rainfall-runoff data still constitute the most accurate, reliable, direct, and thus a basic component of hydrology related database. One critical question in model diagnostic analysis is, therefore, what signature characteristic can we extract from rainfall and runoff data. To this date only a few studies have focused on this question, such as Merz et al. (2006) and Lana-Renault et al. (2007), still none of these studies related event analysis with model diagnosis in an explicit, rigorous, and systematic manner. Our work focuses on the identification of the dominant runoff generation mechanisms from event analysis of rainfall-runoff data, including correlation analysis and analysis of timing pattern. The correlation analysis involves the identification of the complex relationship among rainfall depth, intensity, runoff coefficient, and antecedent conditions, and the timing pattern analysis aims to identify the clustering pattern of runoff events in relation to the patterns of rainfall events. Our diagnostic analysis illustrates the changing pattern of runoff generation mechanisms in the DMIP2 test watersheds located in Oklahoma region, which is also well recognized by numerical simulations based on TsingHua Representative Elementary Watershed (THREW) model. The result suggests the usefulness of rainfall-runoff event analysis for model development as well as model diagnostics.

New V and V Tools for Diagnostic Modeling Environment (DME)

NASA Technical Reports Server (NTRS)

Pecheur, Charles; Nelson, Stacy; Merriam, Marshall (Technical Monitor)

2002-01-01

The purpose of this report is to provide correctness and reliability criteria for verification and validation (V&V) of Second Generation Reusable Launch Vehicle (RLV) Diagnostic Modeling Environment, describe current NASA Ames Research Center tools for V&V of Model Based Reasoning systems, and discuss the applicability of Advanced V&V to DME. This report is divided into the following three sections: (1) correctness and reliability criteria; (2) tools for V&V of Model Based Reasoning; and (3) advanced V&V applicable to DME. The Executive Summary includes an overview of the main points from each section. Supporting details, diagrams, figures, and other information are included in subsequent sections. A glossary, acronym list, appendices, and references are included at the end of this report.

The Meta-Ontology Model of the Fishdisease Diagnostic Knowledge Based on Owl

NASA Astrophysics Data System (ADS)

Shi, Yongchang; Gao, Wen; Hu, Liang; Fu, Zetian

For improving available and reusable of knowledge in fish disease diagnosis (FDD) domain and facilitating knowledge acquisition, an ontology model of FDD knowledge was developed based on owl according to FDD knowledge model. It includes terminology of terms in FDD knowledge and hierarchies of their class.

A general diagnostic model applied to language testing data.

PubMed

von Davier, Matthias

2008-11-01

Probabilistic models with one or more latent variables are designed to report on a corresponding number of skills or cognitive attributes. Multidimensional skill profiles offer additional information beyond what a single test score can provide, if the reported skills can be identified and distinguished reliably. Many recent approaches to skill profile models are limited to dichotomous data and have made use of computationally intensive estimation methods such as Markov chain Monte Carlo, since standard maximum likelihood (ML) estimation techniques were deemed infeasible. This paper presents a general diagnostic model (GDM) that can be estimated with standard ML techniques and applies to polytomous response variables as well as to skills with two or more proficiency levels. The paper uses one member of a larger class of diagnostic models, a compensatory diagnostic model for dichotomous and partial credit data. Many well-known models, such as univariate and multivariate versions of the Rasch model and the two-parameter logistic item response theory model, the generalized partial credit model, as well as a variety of skill profile models, are special cases of this GDM. In addition to an introduction to this model, the paper presents a parameter recovery study using simulated data and an application to real data from the field test for TOEFL Internet-based testing.

Hybrid Kalman Filter: A New Approach for Aircraft Engine In-Flight Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2006-01-01

In this paper, a uniquely structured Kalman filter is developed for its application to in-flight diagnostics of aircraft gas turbine engines. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the in-flight diagnostic system to be updated to the degraded health condition of the engines through a relatively simple process. Through this health baseline update, the effectiveness of the in-flight diagnostic algorithm can be maintained as the health of the engine degrades over time. Another significant aspect of the hybrid Kalman filter methodology is its capability to take advantage of conventional linear and nonlinear Kalman filter approaches. Based on the hybrid Kalman filter, an in-flight fault detection system is developed, and its diagnostic capability is evaluated in a simulation environment. Through the evaluation, the suitability of the hybrid Kalman filter technique for aircraft engine in-flight diagnostics is demonstrated.

Bootstrap imputation with a disease probability model minimized bias from misclassification due to administrative database codes.

PubMed

van Walraven, Carl

2017-04-01

Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias. Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates. Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized. Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

Costs and clinical outcomes for non-invasive versus invasive diagnostic approaches to patients with suspected in-stent restenosis.

PubMed

Min, James K; Hasegawa, James T; Machacz, Susanne F; O'Day, Ken

2016-02-01

This study compared costs and clinical outcomes of invasive versus non-invasive diagnostic evaluations for patients with suspected in-stent restenosis (ISR) after percutaneous coronary intervention. We developed a decision model to compare 2 year diagnosis-related costs for patients who presented with suspected ISR and were evaluated by: (1) invasive coronary angiography (ICA); (2) non-invasive stress testing strategy of myocardial perfusion imaging (MPI) with referral to ICA based on MPI; (3) coronary CT angiography-based testing strategy with referral to ICA based on CCTA. Costs were modeled from the payer's perspective using 2014 Medicare rates. 56 % of patients underwent follow-up diagnostic testing over 2 years. Compared to ICA, MPI (98.6 %) and CCTA (98.1 %) exhibited lower rates of correct diagnoses. Non-invasive strategies were associated with reduced referrals to ICA and costs compared to an ICA-based strategy, with diagnostic costs lower for CCTA than MPI. Overall 2-year costs were highest for ICA for both metallic as well as BVS stents ($1656 and $1656, respectively) when compared to MPI ($1444 and $1411) and CCTA. CCTA costs differed based upon stent size and type, and were highest for metallic stents >3.0 mm followed by metallic stents <3.0 mm, BVS < 3.0 mm and BVS > 3.0 mm ($1466 vs. $1242 vs. $855 vs. $490, respectively). MPI for suspected ISR results in lower costs and rates of complications than invasive strategies using ICA while maintaining high diagnostic performance. Depending upon stent size and type, CCTA results in lower costs than MPI.

A Predictive Model to Estimate Cost Savings of a Novel Diagnostic Blood Panel for Diagnosis of Diarrhea-predominant Irritable Bowel Syndrome.

PubMed

Pimentel, Mark; Purdy, Chris; Magar, Raf; Rezaie, Ali

2016-07-01

A high incidence of irritable bowel syndrome (IBS) is associated with significant medical costs. Diarrhea-predominant IBS (IBS-D) is diagnosed on the basis of clinical presentation and diagnostic test results and procedures that exclude other conditions. This study was conducted to estimate the potential cost savings of a novel IBS diagnostic blood panel that tests for the presence of antibodies to cytolethal distending toxin B and anti-vinculin associated with IBS-D. A cost-minimization (CM) decision tree model was used to compare the costs of a novel IBS diagnostic blood panel pathway versus an exclusionary diagnostic pathway (ie, standard of care). The probability that patients proceed to treatment was modeled as a function of sensitivity, specificity, and likelihood ratios of the individual biomarker tests. One-way sensitivity analyses were performed for key variables, and a break-even analysis was performed for the pretest probability of IBS-D. Budget impact analysis of the CM model was extrapolated to a health plan with 1 million covered lives. The CM model (base-case) predicted $509 cost savings for the novel IBS diagnostic blood panel versus the exclusionary diagnostic pathway because of the avoidance of downstream testing (eg, colonoscopy, computed tomography scans). Sensitivity analysis indicated that an increase in both positive likelihood ratios modestly increased cost savings. Break-even analysis estimated that the pretest probability of disease would be 0.451 to attain cost neutrality. The budget impact analysis predicted a cost savings of $3,634,006 ($0.30 per member per month). The novel IBS diagnostic blood panel may yield significant cost savings by allowing patients to proceed to treatment earlier, thereby avoiding unnecessary testing. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Bivariate random-effects meta-analysis models for diagnostic test accuracy studies using arcsine-based transformations.

PubMed

Negeri, Zelalem F; Shaikh, Mateen; Beyene, Joseph

2018-05-11

Diagnostic or screening tests are widely used in medical fields to classify patients according to their disease status. Several statistical models for meta-analysis of diagnostic test accuracy studies have been developed to synthesize test sensitivity and specificity of a diagnostic test of interest. Because of the correlation between test sensitivity and specificity, modeling the two measures using a bivariate model is recommended. In this paper, we extend the current standard bivariate linear mixed model (LMM) by proposing two variance-stabilizing transformations: the arcsine square root and the Freeman-Tukey double arcsine transformation. We compared the performance of the proposed methods with the standard method through simulations using several performance measures. The simulation results showed that our proposed methods performed better than the standard LMM in terms of bias, root mean square error, and coverage probability in most of the scenarios, even when data were generated assuming the standard LMM. We also illustrated the methods using two real data sets. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Maximum likelihood estimation of signal detection model parameters for the assessment of two-stage diagnostic strategies.

PubMed

Lirio, R B; Dondériz, I C; Pérez Abalo, M C

1992-08-01

The methodology of Receiver Operating Characteristic curves based on the signal detection model is extended to evaluate the accuracy of two-stage diagnostic strategies. A computer program is developed for the maximum likelihood estimation of parameters that characterize the sensitivity and specificity of two-stage classifiers according to this extended methodology. Its use is briefly illustrated with data collected in a two-stage screening for auditory defects.

An ARM data-oriented diagnostics package to evaluate the climate model simulation

NASA Astrophysics Data System (ADS)

Zhang, C.; Xie, S.

2016-12-01

A set of diagnostics that utilize long-term high frequency measurements from the DOE Atmospheric Radiation Measurement (ARM) program is developed for evaluating the regional simulation of clouds, radiation and precipitation in climate models. The diagnostics results are computed and visualized automatically in a python-based package that aims to serve as an easy entry point for evaluating climate simulations using the ARM data, as well as the CMIP5 multi-model simulations. Basic performance metrics are computed to measure the accuracy of mean state and variability of simulated regional climate. The evaluated physical quantities include vertical profiles of clouds, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, radiative fluxes, aerosol and cloud microphysical properties. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena are developed for the evaluation and development of specific model physical parameterizations. Application of the ARM diagnostics package will be presented in the AGU session. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, IM release number is: LLNL-ABS-698645.

Modeling and simulation of a beam emission spectroscopy diagnostic for the ITER prototype neutral beam injector.

PubMed

Barbisan, M; Zaniol, B; Pasqualotto, R

2014-11-01

A test facility for the development of the neutral beam injection system for ITER is under construction at Consorzio RFX. It will host two experiments: SPIDER, a 100 keV H(-)/D(-) ion RF source, and MITICA, a prototype of the full performance ITER injector (1 MV, 17 MW beam). A set of diagnostics will monitor the operation and allow to optimize the performance of the two prototypes. In particular, beam emission spectroscopy will measure the uniformity and the divergence of the fast particles beam exiting the ion source and travelling through the beam line components. This type of measurement is based on the collection of the Hα/Dα emission resulting from the interaction of the energetic particles with the background gas. A numerical model has been developed to simulate the spectrum of the collected emissions in order to design this diagnostic and to study its performance. The paper describes the model at the base of the simulations and presents the modeled Hα spectra in the case of MITICA experiment.

Cost-effectiveness analysis of acute kidney injury biomarkers in pediatric cardiac surgery.

PubMed

Petrovic, Stanislava; Bogavac-Stanojevic, Natasa; Lakic, Dragana; Peco-Antic, Amira; Vulicevic, Irena; Ivanisevic, Ivana; Kotur-Stevuljevic, Jelena; Jelic-Ivanovic, Zorana

2015-01-01

Acute kidney injury (AKI) is significant problem in children with congenital heart disease (CHD) who undergo cardiac surgery. The economic impact of a biomarker-based diagnostic strategy for AKI in pediatric populations undergoing CHD surgery is unknown. The aim of this study was to perform the cost effectiveness analysis of using serum cystatin C (sCysC), urine neutrophil gelatinase-associated lipocalin (uNGAL) and urine liver fatty acid-binding protein (uL-FABP) for the diagnosis of AKI in children after cardiac surgery compared with current diagnostic method (monitoring of serum creatinine (sCr) level). We developed a decision analytical model to estimate incremental cost-effectiveness of different biomarker-based diagnostic strategies compared to current diagnostic strategy. The Markov model was created to compare the lifetime cost associated with using of sCysC, uNGAL, uL-FABP with monitoring of sCr level for the diagnosis of AKI. The utility measurement included in the analysis was quality-adjusted life years (QALY). The results of the analysis are presented as the incremental cost-effectiveness ratio (ICER). Analysed biomarker-based diagnostic strategies for AKI were cost-effective compared to current diagnostic method. However, uNGAL and sCys C strategies yielded higher costs and lower effectiveness compared to uL-FABP strategy. uL-FABP added 1.43 QALY compared to current diagnostic method at an additional cost of $8521.87 per patient. Therefore, ICER for uL-FABP compared to sCr was $5959.35/QALY. Our results suggest that the use of uL-FABP would represent cost effective strategy for early diagnosis of AKI in children after cardiac surgery.

An evidential reasoning extension to quantitative model-based failure diagnosis

NASA Technical Reports Server (NTRS)

Gertler, Janos J.; Anderson, Kenneth C.

1992-01-01

The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

Hybrid diagnostic system: beacon-based exception analysis for multimissions - Livingstone integration

NASA Technical Reports Server (NTRS)

Park, Han G.; Cannon, Howard; Bajwa, Anupa; Mackey, Ryan; James, Mark; Maul, William

2004-01-01

This paper describes the initial integration of a hybrid reasoning system utilizing a continuous domain feature-based detector, Beacon-based Exceptions Analysis for Multimissions (BEAM), and a discrete domain model-based reasoner, Livingstone.

Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction.

PubMed

Warner, Grace; Hoenig, Helen; Montez, Maria; Wang, Fei; Rosen, Amy

2004-02-01

To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction. Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2. Models were replicated in 3 populations. Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888). Not applicable. Inpatient, outpatient, and total days of care in FY97. The DCG models (R(2) range,.22-.38) performed better than ACG models (R(2) range,.04-.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model (R(2) range for ACG,.14-.34; R(2) range for DCG,.24-.38). Information on self-care function slightly improved performance (R(2) range increased from 0 to.04). The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.

From accuracy to patient outcome and cost-effectiveness evaluations of diagnostic tests and biomarkers: an exemplary modelling study

PubMed Central

2013-01-01

Background Proper evaluation of new diagnostic tests is required to reduce overutilization and to limit potential negative health effects and costs related to testing. A decision analytic modelling approach may be worthwhile when a diagnostic randomized controlled trial is not feasible. We demonstrate this by assessing the cost-effectiveness of modified transesophageal echocardiography (TEE) compared with manual palpation for the detection of atherosclerosis in the ascending aorta. Methods Based on a previous diagnostic accuracy study, actual Dutch reimbursement data, and evidence from literature we developed a Markov decision analytic model. Cost-effectiveness of modified TEE was assessed for a life time horizon and a health care perspective. Prevalence rates of atherosclerosis were age-dependent and low as well as high rates were applied. Probabilistic sensitivity analysis was applied. Results The model synthesized all available evidence on the risk of stroke in cardiac surgery patients. The modified TEE strategy consistently resulted in more adapted surgical procedures and, hence, a lower risk of stroke and a slightly higher number of life-years. With 10% prevalence of atherosclerosis the incremental cost-effectiveness ratio was €4,651 and €481 per quality-adjusted life year in 55-year-old men and women, respectively. In all patients aged 65 years or older the modified TEE strategy was cost saving and resulted in additional health benefits. Conclusions Decision analytic modelling to assess the cost-effectiveness of a new diagnostic test based on characteristics, costs and effects of the test itself and of the subsequent treatment options is both feasible and valuable. Our case study on modified TEE suggests that it may reduce the risk of stroke in cardiac surgery patients older than 55 years at acceptable cost-effectiveness levels. PMID:23368927

A systematic review of model-based economic evaluations of diagnostic and therapeutic strategies for lower extremity artery disease.

PubMed

Vaidya, Anil; Joore, Manuela A; ten Cate-Hoek, Arina J; Kleinegris, Marie-Claire; ten Cate, Hugo; Severens, Johan L

2014-01-01

Lower extremity artery disease (LEAD) is a sign of wide spread atherosclerosis also affecting coronary, cerebral and renal arteries and is associated with increased risk of cardiovascular events. Many economic evaluations have been published for LEAD due to its clinical, social and economic importance. The aim of this systematic review was to assess modelling methods used in published economic evaluations in the field of LEAD. Our review appraised and compared the general characteristics, model structure and methodological quality of published models. Electronic databases MEDLINE and EMBASE were searched until February 2013 via OVID interface. Cochrane database of systematic reviews, Health Technology Assessment database hosted by National Institute for Health research and National Health Services Economic Evaluation Database (NHSEED) were also searched. The methodological quality of the included studies was assessed by using the Philips' checklist. Sixteen model-based economic evaluations were identified and included. Eleven models compared therapeutic health technologies; three models compared diagnostic tests and two models compared a combination of diagnostic and therapeutic options for LEAD. Results of this systematic review revealed an acceptable to low methodological quality of the included studies. Methodological diversity and insufficient information posed a challenge for valid comparison of the included studies. In conclusion, there is a need for transparent, methodologically comparable and scientifically credible model-based economic evaluations in the field of LEAD. Future modelling studies should include clinically and economically important cardiovascular outcomes to reflect the wider impact of LEAD on individual patients and on the society.

Chronobiology of epilepsy: diagnostic and therapeutic implications of chrono-epileptology.

PubMed

Loddenkemper, Tobias; Lockley, Steven W; Kaleyias, Joseph; Kothare, Sanjeev V

2011-04-01

The combination of chronobiology and epilepsy offers novel diagnostic and therapeutic management options. Knowledge of the interactions between circadian periodicity, entrainment, sleep patterns, and epilepsy may provide additional diagnostic options beyond sleep deprivation and extended release medication formulations. It may also provide novel insights into the physiologic, biochemical, and genetic regulation processes of epilepsy and the circadian clock, rendering new treatment options. Temporal fluctuations of seizure susceptibility based on sleep homeostasis and circadian phase in selected epilepsies may provide predictability based on mathematical models. Chrono-epileptology offers opportunities for individualized patient-oriented treatment paradigms based on chrono-pharmacology, differential medication dosing, chrono-drug delivery systems, and utilization of "zeitgebers" such as chronobiotics or light-therapy and desynchronization strategies among others.

Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

NASA Astrophysics Data System (ADS)

Michalak, Anna M.; Randazzo, Nina A.; Chevallier, Frédéric

2017-06-01

The ability to predict the trajectory of climate change requires a clear understanding of the emissions and uptake (i.e., surface fluxes) of long-lived greenhouse gases (GHGs). Furthermore, the development of climate policies is driving a need to constrain the budgets of anthropogenic GHG emissions. Inverse problems that couple atmospheric observations of GHG concentrations with an atmospheric chemistry and transport model have increasingly been used to gain insights into surface fluxes. Given the inherent technical challenges associated with their solution, it is imperative that objective approaches exist for the evaluation of such inverse problems. Because direct observation of fluxes at compatible spatiotemporal scales is rarely possible, diagnostics tools must rely on indirect measures. Here we review diagnostics that have been implemented in recent studies and discuss their use in informing adjustments to model setup. We group the diagnostics along a continuum starting with those that are most closely related to the scientific question being targeted, and ending with those most closely tied to the statistical and computational setup of the inversion. We thus begin with diagnostics based on assessments against independent information (e.g., unused atmospheric observations, large-scale scientific constraints), followed by statistical diagnostics of inversion results, diagnostics based on sensitivity tests, and analyses of robustness (e.g., tests focusing on the chemistry and transport model, the atmospheric observations, or the statistical and computational framework), and close with the use of synthetic data experiments (i.e., observing system simulation experiments, OSSEs). We find that existing diagnostics provide a crucial toolbox for evaluating and improving flux estimates but, not surprisingly, cannot overcome the fundamental challenges associated with limited atmospheric observations or the lack of direct flux measurements at compatible scales. As atmospheric inversions are increasingly expected to contribute to national reporting of GHG emissions, the need for developing and implementing robust and transparent evaluation approaches will only grow.

Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas.

PubMed

Hoyer, A; Kuss, O

2015-05-20

In real life and somewhat contrary to biostatistical textbook knowledge, sensitivity and specificity (and not only predictive values) of diagnostic tests can vary with the underlying prevalence of disease. In meta-analysis of diagnostic studies, accounting for this fact naturally leads to a trivariate expansion of the traditional bivariate logistic regression model with random study effects. In this paper, a new model is proposed using trivariate copulas and beta-binomial marginal distributions for sensitivity, specificity, and prevalence as an expansion of the bivariate model. Two different copulas are used, the trivariate Gaussian copula and a trivariate vine copula based on the bivariate Plackett copula. This model has a closed-form likelihood, so standard software (e.g., SAS PROC NLMIXED) can be used. The results of a simulation study have shown that the copula models perform at least as good but frequently better than the standard model. The methods are illustrated by two examples. Copyright © 2015 John Wiley & Sons, Ltd.

1998 Technology Showcase. JOAP International Condition Monitoring Conference.

DTIC Science & Technology

1998-04-01

Systems using Automated SEM/ EDX and New Diagnostic Routines 276 N. W Farrant & T. Luckhurst ADVANCED DIAGNOSTIC SYSTEMS Model-Based Diagnostics of Gas...Microscopy with Energy Dispersive X-Ray (SEM/ EDX ) micro analysis packages and Energy Dispersive X-Ray Fluorescence (EDXRF) analytical equipment. Therqfore...wear particles separated by ferrogram method. a- I WEAR PARTICLE A SLAS 97 (HOME PAGE) Fig I Home Page NONFE;RROUS MATERIAL A wW~ a48 -1, rV fr , ý b

Stability of Initial Autism Spectrum Disorder Diagnoses in Community Settings

ERIC Educational Resources Information Center

Daniels, Amy M.; Rosenberg, Rebecca E.; Law, J. Kiely; Lord, Catherine; Kaufmann, Walter E.; Law, Paul A.

2011-01-01

The study's objectives were to assess diagnostic stability of initial autism spectrum disorder (ASD) diagnoses in community settings and identify factors associated with diagnostic instability using data from a national Web-based autism registry. A Cox proportional hazards model was used to assess the relative risk of change in initial ASD…

Diagnostic Opportunities Using Rasch Measurement in the Context of a Misconceptions-Based Physical Science Assessment

ERIC Educational Resources Information Center

Wind, Stefanie A.; Gale, Jessica D.

2015-01-01

Multiple-choice (MC) items that are constructed such that distractors target known misconceptions for a particular domain provide useful diagnostic information about student misconceptions (Herrmann-Abell & DeBoer, 2011, 2014; Sadler, 1998). Item response theory models can be used to examine misconceptions distractor-driven multiple-choice…

Energetic particle instabilities in fusion plasmas

NASA Astrophysics Data System (ADS)

Sharapov, S. E.; Alper, B.; Berk, H. L.; Borba, D. N.; Breizman, B. N.; Challis, C. D.; Classen, I. G. J.; Edlund, E. M.; Eriksson, J.; Fasoli, A.; Fredrickson, E. D.; Fu, G. Y.; Garcia-Munoz, M.; Gassner, T.; Ghantous, K.; Goloborodko, V.; Gorelenkov, N. N.; Gryaznevich, M. P.; Hacquin, S.; Heidbrink, W. W.; Hellesen, C.; Kiptily, V. G.; Kramer, G. J.; Lauber, P.; Lilley, M. K.; Lisak, M.; Nabais, F.; Nazikian, R.; Nyqvist, R.; Osakabe, M.; Perez von Thun, C.; Pinches, S. D.; Podesta, M.; Porkolab, M.; Shinohara, K.; Schoepf, K.; Todo, Y.; Toi, K.; Van Zeeland, M. A.; Voitsekhovich, I.; White, R. B.; Yavorskij, V.; TG, ITPA EP; Contributors, JET-EFDA

2013-10-01

Remarkable progress has been made in diagnosing energetic particle instabilities on present-day machines and in establishing a theoretical framework for describing them. This overview describes the much improved diagnostics of Alfvén instabilities and modelling tools developed world-wide, and discusses progress in interpreting the observed phenomena. A multi-machine comparison is presented giving information on the performance of both diagnostics and modelling tools for different plasma conditions outlining expectations for ITER based on our present knowledge.

A temporally and spatially resolved electron density diagnostic method for the edge plasma based on Stark broadening

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

Zafar, A., E-mail: zafara@ornl.gov; Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830; Martin, E. H.

2016-11-15

An electron density diagnostic (≥10{sup 10} cm{sup −3}) capable of high temporal (ms) and spatial (mm) resolution is currently under development at Oak Ridge National Laboratory. The diagnostic is based on measuring the Stark broadened, Doppler-free spectral line profile of the n = 6–2 hydrogen Balmer series transition. The profile is then fit to a fully quantum mechanical model including the appropriate electric and magnetic field operators. The quasi-static approach used to calculate the Doppler-free spectral line profile is outlined here and the results from the model are presented for H-δ spectra for electron densities of 10{sup 10}–10{sup 13} cm{supmore » −3}. The profile shows complex behavior due to the interaction between the magnetic substates of the atom.« less

Untangling nociceptive, neuropathic and neuroplastic mechanisms underlying the biological domain of back pain.

PubMed

Hush, Julia M; Stanton, Tasha R; Siddall, Philip; Marcuzzi, Anna; Attal, Nadine

2013-05-01

SUMMARY Current clinical practice guidelines advocate a model of diagnostic triage for back pain, underpinned by the biopsychosocial paradigm. However, limitations of this clinical model have become apparent: it can be difficult to classify patients into the diagnostic triage categories; patients with 'nonspecific back pain' are clearly not a homogenous group; and mean effects of treatments based on this approach are small. In this article, it is proposed that the biological domain of the biopsychosocial model needs to be reconceptualized using a neurobiological mechanism-based approach. Recent evidence about nociceptive and neuropathic contributors to back pain is outlined in the context of maladaptive neuroplastic changes of the somatosensory system. Implications for clinical practice and research are discussed.

Qualitative model-based diagnosis using possibility theory

NASA Technical Reports Server (NTRS)

Joslyn, Cliff

1994-01-01

The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.

Assessing clinical reasoning (ASCLIRE): Instrument development and validation.

PubMed

Kunina-Habenicht, Olga; Hautz, Wolf E; Knigge, Michel; Spies, Claudia; Ahlers, Olaf

2015-12-01

Clinical reasoning is an essential competency in medical education. This study aimed at developing and validating a test to assess diagnostic accuracy, collected information, and diagnostic decision time in clinical reasoning. A norm-referenced computer-based test for the assessment of clinical reasoning (ASCLIRE) was developed, integrating the entire clinical decision process. In a cross-sectional study participants were asked to choose as many diagnostic measures as they deemed necessary to diagnose the underlying disease of six different cases with acute or sub-acute dyspnea and provide a diagnosis. 283 students and 20 content experts participated. In addition to diagnostic accuracy, respective decision time and number of used relevant diagnostic measures were documented as distinct performance indicators. The empirical structure of the test was investigated using a structural equation modeling approach. Experts showed higher accuracy rates and lower decision times than students. In a cross-sectional comparison, the diagnostic accuracy of students improved with the year of study. Wrong diagnoses provided by our sample were comparable to wrong diagnoses in practice. We found an excellent fit for a model with three latent factors-diagnostic accuracy, decision time, and choice of relevant diagnostic information-with diagnostic accuracy showing no significant correlation with decision time. ASCLIRE considers decision time as an important performance indicator beneath diagnostic accuracy and provides evidence that clinical reasoning is a complex ability comprising diagnostic accuracy, decision time, and choice of relevant diagnostic information as three partly correlated but still distinct aspects.

Diagnostic tolerance for missing sensor data

NASA Technical Reports Server (NTRS)

Scarl, Ethan A.

1989-01-01

For practical automated diagnostic systems to continue functioning after failure, they must not only be able to diagnose sensor failures but also be able to tolerate the absence of data from the faulty sensors. It is shown that conventional (associational) diagnostic methods will have combinatoric problems when trying to isolate faulty sensors, even if they adequately diagnose other components. Moreover, attempts to extend the operation of diagnostic capability past sensor failure will necessarily compound those difficulties. Model-based reasoning offers a structured alternative that has no special problems diagnosing faulty sensors and can operate gracefully when sensor data is missing.

Does probability guided hysteroscopy reduce costs in women investigated for postmenopausal bleeding?

PubMed

Breijer, M C; van Hanegem, N; Visser, N C M; Verheijen, R H M; Mol, B W J; Pijnenborg, J M A; Opmeer, B C; Timmermans, A

2015-01-01

To evaluate whether a model to predict a failed endometrial biopsy in women with postmenopausal bleeding (PMB) and a thickened endometrium can reduce costs without compromising diagnostic accuracy. Model based cost-minimization analysis. A decision analytic model was designed to compare two diagnostic strategies for women with PMB: (I) attempting office endometrial biopsy and performing outpatient hysteroscopy after failed biopsy and (II) predicted probability of a failed endometrial biopsy based on patient characteristics to guide the decision for endometrial biopsy or immediate hysteroscopy. Robustness of assumptions regarding costs was evaluated in sensitivity analyses. Costs for the different strategies. At different cut-offs for the predicted probability of failure of an endometrial biopsy, strategy I was generally less expensive than strategy II. The costs for strategy I were always € 460; the costs for strategy II varied between € 457 and € 475. At a 65% cut-off, a possible saving of € 3 per woman could be achieved. Individualizing the decision to perform an endometrial biopsy or immediate hysteroscopy in women presenting with postmenopausal bleeding based on patient characteristics does not increase the efficiency of the diagnostic work-up.

Recognition ROCS Are Curvilinear--Or Are They? On Premature Arguments against the Two-High-Threshold Model of Recognition

ERIC Educational Resources Information Center

Broder, Arndt; Schutz, Julia

2009-01-01

Recent reviews of recognition receiver operating characteristics (ROCs) claim that their curvilinear shape rules out threshold models of recognition. However, the shape of ROCs based on confidence ratings is not diagnostic to refute threshold models, whereas ROCs based on experimental bias manipulations are. Also, fitting predicted frequencies to…

Methods Developed by the Tools for Engine Diagnostics Task to Monitor and Predict Rotor Damage in Real Time

NASA Technical Reports Server (NTRS)

Baaklini, George Y.; Smith, Kevin; Raulerson, David; Gyekenyesi, Andrew L.; Sawicki, Jerzy T.; Brasche, Lisa

2003-01-01

Tools for Engine Diagnostics is a major task in the Propulsion System Health Management area of the Single Aircraft Accident Prevention project under NASA s Aviation Safety Program. The major goal of the Aviation Safety Program is to reduce fatal aircraft accidents by 80 percent within 10 years and by 90 percent within 25 years. The goal of the Propulsion System Health Management area is to eliminate propulsion system malfunctions as a primary or contributing factor to the cause of aircraft accidents. The purpose of Tools for Engine Diagnostics, a 2-yr-old task, is to establish and improve tools for engine diagnostics and prognostics that measure the deformation and damage of rotating engine components at the ground level and that perform intermittent or continuous monitoring on the engine wing. In this work, nondestructive-evaluation- (NDE-) based technology is combined with model-dependent disk spin experimental simulation systems, like finite element modeling (FEM) and modal norms, to monitor and predict rotor damage in real time. Fracture mechanics time-dependent fatigue crack growth and damage-mechanics-based life estimation are being developed, and their potential use investigated. In addition, wireless eddy current and advanced acoustics are being developed for on-wing and just-in-time NDE engine inspection to provide deeper access and higher sensitivity to extend on-wing capabilities and improve inspection readiness. In the long run, these methods could establish a base for prognostic sensing while an engine is running, without any overt actions, like inspections. This damage-detection strategy includes experimentally acquired vibration-, eddy-current- and capacitance-based displacement measurements and analytically computed FEM-, modal norms-, and conventional rotordynamics-based models of well-defined damages and critical mass imbalances in rotating disks and rotors.

Etiological analysis and predictive diagnostic model building of community-acquired pneumonia in adult outpatients in Beijing, China.

PubMed

Liu, Ya-Fen; Gao, Yan; Chen, Mei-Fang; Cao, Bin; Yang, Xiao-Hua; Wei, Lai

2013-07-09

Etiological epidemiology and diagnosis are important issues in adult community-acquired pneumonia (CAP), and identifying pathogens based on patient clinical features is especially a challenge. CAP-associated main pathogens in adults include viruses as well as bacteria. However, large-scale epidemiological investigations of adult viral CAP in China are still lacking. In this study, we analyzed the etiology of adult CAP in Beijing, China and constructed diagnostic models based on combinations of patient clinical factors. A multicenter cohort was established with 500 adult CAP outpatients enrolled in Beijing between November 2010 to October 2011. Multiplex and quantitative real-time fluorescence PCR were used to detect 15 respiratory viruses and mycoplasma pneumoniae, respectively. Bacteria were detected with culture and enzyme immunoassay of the Streptococcus pneumoniae urinary antigen. Univariate analysis, multivariate analysis, discriminatory analysis and Receiver Operating Characteristic (ROC) curves were used to build predictive models for etiological diagnosis of adult CAP. Pathogens were detected in 54.2% (271/500) of study patients. Viruses accounted for 36.4% (182/500), mycoplasma pneumoniae for 18.0% (90/500) and bacteria for 14.4% (72/500) of the cases. In 182 of the patients with viruses, 219 virus strains were detected, including 166 single and 53 mixed viral infections. Influenza A virus represented the greatest proportion with 42.0% (92/219) and 9.1% (20/219) in single and mixed viral infections, respectively. Factors selected for the predictive etiological diagnostic model of viral CAP included cough, dyspnea, absence of chest pain and white blood cell count (4.0-10.0) × 10(9)/L, and those of mycoplasma pneumoniae CAP were being younger than 45 years old and the absence of a coexisting disease. However, these models showed low accuracy levels for etiological diagnosis (areas under ROC curve for virus and mycoplasma pneumoniae were both 0.61, P < 0.05). Greater consideration should be given to viral and mycoplasma pneumoniae infections in adult CAP outpatients. While predictive etiological diagnostic models of viral and mycoplasma pneumoniae based on combinations of demographic and clinical factors may provide indications of etiology, diagnostic confirmation of CAP remains dependent on laboratory pathogen test results.

A diagnostic model for chronic hypersensitivity pneumonitis

PubMed Central

Johannson, Kerri A; Elicker, Brett M; Vittinghoff, Eric; Assayag, Deborah; de Boer, Kaïssa; Golden, Jeffrey A; Jones, Kirk D; King, Talmadge E; Koth, Laura L; Lee, Joyce S; Ley, Brett; Wolters, Paul J; Collard, Harold R

2017-01-01

The objective of this study was to develop a diagnostic model that allows for a highly specific diagnosis of chronic hypersensitivity pneumonitis using clinical and radiological variables alone. Chronic hypersensitivity pneumonitis and other interstitial lung disease cases were retrospectively identified from a longitudinal database. High-resolution CT scans were blindly scored for radiographic features (eg, ground-glass opacity, mosaic perfusion) as well as the radiologist’s diagnostic impression. Candidate models were developed then evaluated using clinical and radiographic variables and assessed by the cross-validated C-statistic. Forty-four chronic hypersensitivity pneumonitis and eighty other interstitial lung disease cases were identified. Two models were selected based on their statistical performance, clinical applicability and face validity. Key model variables included age, down feather and/or bird exposure, radiographic presence of ground-glass opacity and mosaic perfusion and moderate or high confidence in the radiographic impression of chronic hypersensitivity pneumonitis. Models were internally validated with good performance, and cut-off values were established that resulted in high specificity for a diagnosis of chronic hypersensitivity pneumonitis. PMID:27245779

Statistical physics of medical diagnostics: Study of a probabilistic model.

PubMed

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Statistical physics of medical diagnostics: Study of a probabilistic model

NASA Astrophysics Data System (ADS)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

A systematic review of diagnostic criteria for psoriasis in adults and children: evidence from studies with a primary aim to develop or validate diagnostic criteria.

PubMed

Burden-Teh, E; Phillips, R C; Thomas, K S; Ratib, S; Grindlay, D; Murphy, R

2017-11-06

The diagnosis of psoriasis in adults and children is made clinically, for both patient management and the selection of participants in research. Diagnostic criteria provide a structure for clinical assessment, which in turn helps standardize patient recruitment into clinical trials and case definitions in observational studies. The aim of this systematic review was to identify and critically appraise the published studies to date that had a primary research aim to develop or validate diagnostic criteria for psoriasis. A search of Ovid MEDLINE and Ovid Embase was conducted in October 2016. The primary objective was to record the sensitivity and specificity of diagnostic criteria for psoriasis. Secondary objectives included diagnostic recommendations, applicability to children and study characteristics. Diagnostic accuracy studies were critically appraised for risk of bias using the QUADAS-2 tool. Twenty-three studies met the inclusion criteria. None detailed clinical examination-based diagnostic criteria. The included criteria varied from genetic and molecular diagnostic models to skin imaging, histopathology, and questionnaire-based, computer-aided and traditional Chinese medicine criteria. High sensitivity and specificity (> 90%) were reported in many studies. However, the study authors often did not specify how the criteria would be used clinically or in research. This review identified studies with varying risk of bias, and due to each study developing separate criteria meta-analysis was not possible. Clinical examination-based diagnostic criteria are currently lacking for psoriasis. Future research could follow an international collaborative approach and employ study designs allowing high-quality diagnostic accuracy testing. Existing and newly developed criteria require validation. © 2017 British Association of Dermatologists.

Proposed Diagnostic Criteria for Smartphone Addiction

PubMed Central

Lin, Yu-Hsuan; Chiang, Chih-Lin; Lin, Po-Hsien; Chang, Li-Ren; Ko, Chih-Hung; Lee, Yang-Han

2016-01-01

Background Global smartphone penetration has led to unprecedented addictive behaviors. The aims of this study are to develop diagnostic criteria of smartphone addiction and to examine the discriminative ability and the validity of the diagnostic criteria. Methods We developed twelve candidate criteria for characteristic symptoms of smartphone addiction and four criteria for functional impairment caused by excessive smartphone use. The participants consisted of 281 college students. Each participant was systematically assessed for smartphone-using behaviors by psychiatrist’s structured diagnostic interview. The sensitivity, specificity, and diagnostic accuracy of the candidate symptom criteria were analyzed with reference to the psychiatrists’ clinical global impression. The optimal model selection with its cutoff point of the diagnostic criteria differentiating the smartphone addicted subjects from non-addicted subjects was then determined by the best diagnostic accuracy. Results Six symptom criteria model with optimal cutoff point were determined based on the maximal diagnostic accuracy. The proposed smartphone addiction diagnostic criteria consisted of (1) six symptom criteria, (2) four functional impairment criteria and (3) exclusion criteria. Setting three symptom criteria as the cutoff point resulted in the highest diagnostic accuracy (84.3%), while the sensitivity and specificity were 79.4% and 87.5%, respectively. We suggested determining the functional impairment by two or more of the four domains considering the high accessibility and penetration of smartphone use. Conclusion The diagnostic criteria of smartphone addiction demonstrated the core symptoms “impaired control” paralleled with substance related and addictive disorders. The functional impairment involved multiple domains provide a strict standard for clinical assessment. PMID:27846211

Proposed Diagnostic Criteria for Smartphone Addiction.

PubMed

Lin, Yu-Hsuan; Chiang, Chih-Lin; Lin, Po-Hsien; Chang, Li-Ren; Ko, Chih-Hung; Lee, Yang-Han; Lin, Sheng-Hsuan

2016-01-01

Global smartphone penetration has led to unprecedented addictive behaviors. The aims of this study are to develop diagnostic criteria of smartphone addiction and to examine the discriminative ability and the validity of the diagnostic criteria. We developed twelve candidate criteria for characteristic symptoms of smartphone addiction and four criteria for functional impairment caused by excessive smartphone use. The participants consisted of 281 college students. Each participant was systematically assessed for smartphone-using behaviors by psychiatrist's structured diagnostic interview. The sensitivity, specificity, and diagnostic accuracy of the candidate symptom criteria were analyzed with reference to the psychiatrists' clinical global impression. The optimal model selection with its cutoff point of the diagnostic criteria differentiating the smartphone addicted subjects from non-addicted subjects was then determined by the best diagnostic accuracy. Six symptom criteria model with optimal cutoff point were determined based on the maximal diagnostic accuracy. The proposed smartphone addiction diagnostic criteria consisted of (1) six symptom criteria, (2) four functional impairment criteria and (3) exclusion criteria. Setting three symptom criteria as the cutoff point resulted in the highest diagnostic accuracy (84.3%), while the sensitivity and specificity were 79.4% and 87.5%, respectively. We suggested determining the functional impairment by two or more of the four domains considering the high accessibility and penetration of smartphone use. The diagnostic criteria of smartphone addiction demonstrated the core symptoms "impaired control" paralleled with substance related and addictive disorders. The functional impairment involved multiple domains provide a strict standard for clinical assessment.

The clinical inadequacy of the DSM-5 classification of somatic symptom and related disorders: an alternative trans-diagnostic model.

PubMed

Cosci, Fiammetta; Fava, Giovanni A

2016-08-01

The Diagnostic and Statistical of Mental Disorders, Fifth Edition (DSM-5) somatic symptom and related disorders chapter has a limited clinical utility. In addition to the problems that the single diagnostic rubrics and the deletion of the diagnosis of hypochondriasis entail, there are 2 major ambiguities: (1) the use of the term "somatic symptoms" reflects an ill-defined concept of somatization and (2) abnormal illness behavior is included in all diagnostic rubrics, but it is never conceptually defined. In the present review of the literature, we will attempt to approach the clinical issue from a different angle, by introducing the trans-diagnostic viewpoint of illness behavior and propose an alternative clinimetric classification system, based on the Diagnostic Criteria for Psychosomatic Research.

A new dump system design for stray light reduction of Thomson scattering diagnostic system on EAST.

PubMed

Xiao, Shumei; Zang, Qing; Han, Xiaofeng; Wang, Tengfei; Yu, Jin; Zhao, Junyu

2016-07-01

Thomson scattering (TS) diagnostic is an important diagnostic for measuring electron temperature and density during plasma discharge. However, the measurement of Thomson scattering signal is disturbed by the stray light easily. The stray light sources in the Experimental Advanced Superconducting Tokamak (EAST) TS diagnostic system were analyzed by a simulation model of the diagnostic system, and simulation results show that the dump system is the primary stray light source. Based on the optics theory and the simulation analysis, a novel dump system including an improved beam trap was proposed and installed. The measurement results indicate that the new dump system can reduce more than 60% of the stray light for the diagnostic system, and the influence of stray light on the error of measured density decreases.

Computational Nosology and Precision Psychiatry

PubMed Central

Redish, A. David; Gordon, Joshua A.

2017-01-01

This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present—and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations. PMID:29400354

A proposal for a new multiaxial model of psychiatric diagnosis. A continuum-based patient model derived from evolutionary developmental gene-environment interaction.

PubMed

Leigh, Hoyle

2009-01-01

To review recent genetic and neuroscientific research on psychiatric syndromes based on the current diagnostic scheme, and develop a better-fitting multiaxial patient-oriented diagnostic model. DSM I, published in 1952, considered psychiatric illnesses as reactions or extremes of adaptations of the patient's personality to stressful environmental demands. Personality itself was determined by constitution and psychodynamic development. In 1980, this continuum model gave way to an atheoretical categorical diagnostic scheme (DSM III), based on research diagnostic criteria for obtaining 'pure cultures' of patients for biological research. Subsequent research using the 'pure cultures' suggests that psychiatric syndromes represent a phenotypic continuum determined by genes, childhood traumas, and recent stress, mitigated by childhood nurturance, education, and current social support. Specific gene x childhood abuse x recent stress interactions have been discovered, which may serve as a model of how interacting vulnerability genes may or may not result in a psychiatric syndrome, depending on the individual's developmental history and current stress. A continuum model is proposed, with genes interacting with early experiences of stress or nurturance resulting in brain states that may evince minor but persistent symptoms (neurosis) or maladaptive patterns of behavior (personality disorder). The addition of recent or current stress may precipitate a major psychiatric syndrome. While a severe genetic predisposition, such as a mutation, may be sufficient to cause a major syndrome, major psychiatric syndromes are best conceptualized as dysregulation of evolutionarily adaptive brain functions, such as anxiety and vigilance. A new multiaxial model of psychiatric diagnosis is proposed based on this model: axis I for phenomenological diagnoses that include major psychiatric syndromes (e.g. depressive syndrome, psychosis), neuroses, personality disorders, and isolated symptoms; axis II for geno-neuroscience diagnoses, some of which may represent biological conditions associated with axis I, i.e. genes, specific brain morphology, and the functional state of specific brain areas based on laboratory and imaging studies; axis III for medical diseases and conditions; axis IV for stress (childhood, recent, and current); axis V for psychosocial assets (intelligence, education, school/work, social support, and global assessment of functioning) over past 5 years and current. (c) 2008 S. Karger AG, Basel.

Interrelation of Evaluation and Self-Evaluation in the Diagnostic Procedures to Assess Teachers' Readiness for Innovation

ERIC Educational Resources Information Center

Tyunnikov, Yurii S.

2016-01-01

The paper solves the problem of the relationship of external diagnosis and self-diagnosis of readiness of teachers to innovative activity. It highlights major disadvantages of measurement tools that are used to this process. The author demonstrates an alternative approach to harmonizing the diagnosis, based on a modular diagnostic model, general…

Graph-based real-time fault diagnostics

NASA Technical Reports Server (NTRS)

Padalkar, S.; Karsai, G.; Sztipanovits, J.

1988-01-01

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

A dynamic integrated fault diagnosis method for power transformers.

PubMed

Gao, Wensheng; Bai, Cuifen; Liu, Tong

2015-01-01

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

A Dynamic Integrated Fault Diagnosis Method for Power Transformers

PubMed Central

Gao, Wensheng; Liu, Tong

2015-01-01

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

Real-time diagnostics for a reusable rocket engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Merrill, W.; Duyar, A.

1992-01-01

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

NASA Technical Reports Server (NTRS)

Kurtoglu, Tolga; Narasimhan, Sriram; Poll, Scott; Garcia, David; Wright, Stephanie

2009-01-01

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.

Modeling and simulation of a beam emission spectroscopy diagnostic for the ITER prototype neutral beam injector

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

Barbisan, M., E-mail: marco.barbisan@igi.cnr.it; Zaniol, B.; Pasqualotto, R.

2014-11-15

A test facility for the development of the neutral beam injection system for ITER is under construction at Consorzio RFX. It will host two experiments: SPIDER, a 100 keV H{sup −}/D{sup −} ion RF source, and MITICA, a prototype of the full performance ITER injector (1 MV, 17 MW beam). A set of diagnostics will monitor the operation and allow to optimize the performance of the two prototypes. In particular, beam emission spectroscopy will measure the uniformity and the divergence of the fast particles beam exiting the ion source and travelling through the beam line components. This type of measurementmore » is based on the collection of the H{sub α}/D{sub α} emission resulting from the interaction of the energetic particles with the background gas. A numerical model has been developed to simulate the spectrum of the collected emissions in order to design this diagnostic and to study its performance. The paper describes the model at the base of the simulations and presents the modeled H{sub α} spectra in the case of MITICA experiment.« less

Advances in Optical Fiber-Based Faraday Rotation Diagnostics

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

White, A D; McHale, G B; Goerz, D A

2009-07-27

In the past two years, we have used optical fiber-based Faraday Rotation Diagnostics (FRDs) to measure pulsed currents on several dozen capacitively driven and explosively driven pulsed power experiments. We have made simplifications to the necessary hardware for quadrature-encoded polarization analysis, including development of an all-fiber analysis scheme. We have developed a numerical model that is useful for predicting and quantifying deviations from the ideal diagnostic response. We have developed a method of analyzing quadrature-encoded FRD data that is simple to perform and offers numerous advantages over several existing methods. When comparison has been possible, we have seen good agreementmore » with our FRDs and other current sensors.« less

Autonomous power expert fault diagnostic system for Space Station Freedom electrical power system testbed

NASA Technical Reports Server (NTRS)

Truong, Long V.; Walters, Jerry L.; Roth, Mary Ellen; Quinn, Todd M.; Krawczonek, Walter M.

1990-01-01

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control to the Space Station Freedom Electrical Power System (SSF/EPS) testbed being developed and demonstrated at NASA Lewis Research Center. The objectives of the program are to establish artificial intelligence technology paths, to craft knowledge-based tools with advanced human-operator interfaces for power systems, and to interface and integrate knowledge-based systems with conventional controllers. The Autonomous Power EXpert (APEX) portion of the APS program will integrate a knowledge-based fault diagnostic system and a power resource planner-scheduler. Then APEX will interface on-line with the SSF/EPS testbed and its Power Management Controller (PMC). The key tasks include establishing knowledge bases for system diagnostics, fault detection and isolation analysis, on-line information accessing through PMC, enhanced data management, and multiple-level, object-oriented operator displays. The first prototype of the diagnostic expert system for fault detection and isolation has been developed. The knowledge bases and the rule-based model that were developed for the Power Distribution Control Unit subsystem of the SSF/EPS testbed are described. A corresponding troubleshooting technique is also described.

A user-friendly, open-source tool to project impact and cost of diagnostic tests for tuberculosis

PubMed Central

Dowdy, David W; Andrews, Jason R; Dodd, Peter J; Gilman, Robert H

2014-01-01

Most models of infectious diseases, including tuberculosis (TB), do not provide results customized to local conditions. We created a dynamic transmission model to project TB incidence, TB mortality, multidrug-resistant (MDR) TB prevalence, and incremental costs over 5 years after scale-up of nine alternative diagnostic strategies. A corresponding web-based interface allows users to specify local costs and epidemiology. In settings with little capacity for up-front investment, same-day microscopy had the greatest impact on TB incidence and became cost-saving within 5 years if delivered at $10/test. With greater initial investment, population-level scale-up of Xpert MTB/RIF or microcolony-based culture often averted 10 times more TB cases than narrowly-targeted strategies, at minimal incremental long-term cost. Xpert for smear-positive TB had reasonable impact on MDR-TB incidence, but at substantial price and little impact on overall TB incidence and mortality. This user-friendly modeling framework improves decision-makers' ability to evaluate the local impact of TB diagnostic strategies. DOI: http://dx.doi.org/10.7554/eLife.02565.001 PMID:24898755

Dual Processing Model for Medical Decision-Making: An Extension to Diagnostic Testing

PubMed Central

Tsalatsanis, Athanasios; Hozo, Iztok; Kumar, Ambuj; Djulbegovic, Benjamin

2015-01-01

Dual Processing Theories (DPT) assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called “threshold probability” at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT) and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today’s clinical practice. PMID:26244571

Dual Processing Model for Medical Decision-Making: An Extension to Diagnostic Testing.

PubMed

Tsalatsanis, Athanasios; Hozo, Iztok; Kumar, Ambuj; Djulbegovic, Benjamin

2015-01-01

Dual Processing Theories (DPT) assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called "threshold probability" at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT) and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today's clinical practice.

Rocket Engine Oscillation Diagnostics

NASA Technical Reports Server (NTRS)

Nesman, Tom; Turner, James E. (Technical Monitor)

2002-01-01

Rocket engine oscillating data can reveal many physical phenomena ranging from unsteady flow and acoustics to rotordynamics and structural dynamics. Because of this, engine diagnostics based on oscillation data should employ both signal analysis and physical modeling. This paper describes an approach to rocket engine oscillation diagnostics, types of problems encountered, and example problems solved. Determination of design guidelines and environments (or loads) from oscillating phenomena is required during initial stages of rocket engine design, while the additional tasks of health monitoring, incipient failure detection, and anomaly diagnostics occur during engine development and operation. Oscillations in rocket engines are typically related to flow driven acoustics, flow excited structures, or rotational forces. Additional sources of oscillatory energy are combustion and cavitation. Included in the example problems is a sampling of signal analysis tools employed in diagnostics. The rocket engine hardware includes combustion devices, valves, turbopumps, and ducts. Simple models of an oscillating fluid system or structure can be constructed to estimate pertinent dynamic parameters governing the unsteady behavior of engine systems or components. In the example problems it is shown that simple physical modeling when combined with signal analysis can be successfully employed to diagnose complex rocket engine oscillatory phenomena.

A fault injection experiment using the AIRLAB Diagnostic Emulation Facility

NASA Technical Reports Server (NTRS)

Baker, Robert; Mangum, Scott; Scheper, Charlotte

1988-01-01

The preparation for, conduct of, and results of a simulation based fault injection experiment conducted using the AIRLAB Diagnostic Emulation facilities is described. An objective of this experiment was to determine the effectiveness of the diagnostic self-test sequences used to uncover latent faults in a logic network providing the key fault tolerance features for a flight control computer. Another objective was to develop methods, tools, and techniques for conducting the experiment. More than 1600 faults were injected into a logic gate level model of the Data Communicator/Interstage (C/I). For each fault injected, diagnostic self-test sequences consisting of over 300 test vectors were supplied to the C/I model as inputs. For each test vector within a test sequence, the outputs from the C/I model were compared to the outputs of a fault free C/I. If the outputs differed, the fault was considered detectable for the given test vector. These results were then analyzed to determine the effectiveness of some test sequences. The results established coverage of selt-test diagnostics, identified areas in the C/I logic where the tests did not locate faults, and suggest fault latency reduction opportunities.

Immune-Response Patterns and Next Generation Sequencing Diagnostics for the Detection of Mycoses in Patients with Septic Shock-Results of a Combined Clinical and Experimental Investigation.

PubMed

Decker, Sebastian O; Sigl, Annette; Grumaz, Christian; Stevens, Philip; Vainshtein, Yevhen; Zimmermann, Stefan; Weigand, Markus A; Hofer, Stefan; Sohn, Kai; Brenner, Thorsten

2017-08-18

Fungi are of increasing importance in sepsis. However, culture-based diagnostic procedures are associated with relevant weaknesses. Therefore, culture- and next-generation sequencing (NGS)-based fungal findings as well as corresponding plasma levels of β-d-glucan, interferon gamma (INF-γ), tumor necrosis factor alpha (TNF-α), interleukin (IL)-2, -4, -6, -10, -17A, and mid-regional proadrenomedullin (MR-proADM) were evaluated in 50 septic patients at six consecutive time points within 28 days after sepsis onset. Furthermore, immune-response patterns during infections with Candida spp. were studied in a reconstituted human epithelium model. In total, 22% ( n = 11) of patients suffered from a fungal infection. An NGS-based diagnostic approach appeared to be suitable for the identification of fungal pathogens in patients suffering from fungemia as well as in patients with negative blood cultures. Moreover, MR-proADM and IL-17A in plasma proved suitable for the identification of patients with a fungal infection. Using RNA-seq., adrenomedullin (ADM) was shown to be a target gene which is upregulated early after an epithelial infection with Candida spp. In summary, an NGS-based diagnostic approach was able to close the diagnostic gap of routinely used culture-based diagnostic procedures, which can be further facilitated by plasmatic measurements of MR-proADM and IL-17A. In addition, ADM was identified as an early target gene in response to epithelial infections with Candida spp.

Models based on value and probability in health improve shared decision making.

PubMed

Ortendahl, Monica

2008-10-01

Diagnostic reasoning and treatment decisions are a key competence of doctors. A model based on values and probability provides a conceptual framework for clinical judgments and decisions, and also facilitates the integration of clinical and biomedical knowledge into a diagnostic decision. Both value and probability are usually estimated values in clinical decision making. Therefore, model assumptions and parameter estimates should be continually assessed against data, and models should be revised accordingly. Introducing parameter estimates for both value and probability, which usually pertain in clinical work, gives the model labelled subjective expected utility. Estimated values and probabilities are involved sequentially for every step in the decision-making process. Introducing decision-analytic modelling gives a more complete picture of variables that influence the decisions carried out by the doctor and the patient. A model revised for perceived values and probabilities by both the doctor and the patient could be used as a tool for engaging in a mutual and shared decision-making process in clinical work.

A photosynthesis-based two-leaf canopy stomatal conductance model for meteorology and air quality modeling with WRF/CMAQ PX LSM

EPA Science Inventory

A coupled photosynthesis-stomatal conductance model with single-layer sunlit and shaded leaf canopy scaling is implemented and evaluated in a diagnostic box model with the Pleim-Xiu land surface model (PX LSM) and ozone deposition model components taken directly from the meteorol...

Assessing Change in Latent Skills across Time with Longitudinal Cognitive Diagnosis Modeling: An Evaluation of Model Performance

ERIC Educational Resources Information Center

Kaya, Yasemin; Leite, Walter L.

2017-01-01

Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor…

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Synthetic neutron camera and spectrometer in JET based on AFSI-ASCOT simulations

NASA Astrophysics Data System (ADS)

Sirén, P.; Varje, J.; Weisen, H.; Koskela, T.; contributors, JET

2017-09-01

The ASCOT Fusion Source Integrator (AFSI) has been used to calculate neutron production rates and spectra corresponding to the JET 19-channel neutron camera (KN3) and the time-of-flight spectrometer (TOFOR) as ideal diagnostics, without detector-related effects. AFSI calculates fusion product distributions in 4D, based on Monte Carlo integration from arbitrary reactant distribution functions. The distribution functions were calculated by the ASCOT Monte Carlo particle orbit following code for thermal, NBI and ICRH particle reactions. Fusion cross-sections were defined based on the Bosch-Hale model and both DD and DT reactions have been included. Neutrons generated by AFSI-ASCOT simulations have already been applied as a neutron source of the Serpent neutron transport code in ITER studies. Additionally, AFSI has been selected to be a main tool as the fusion product generator in the complete analysis calculation chain: ASCOT - AFSI - SERPENT (neutron and gamma transport Monte Carlo code) - APROS (system and power plant modelling code), which encompasses the plasma as an energy source, heat deposition in plant structures as well as cooling and balance-of-plant in DEMO applications and other reactor relevant analyses. This conference paper presents the first results and validation of the AFSI DD fusion model for different auxiliary heating scenarios (NBI, ICRH) with very different fast particle distribution functions. Both calculated quantities (production rates and spectra) have been compared with experimental data from KN3 and synthetic spectrometer data from ControlRoom code. No unexplained differences have been observed. In future work, AFSI will be extended for synthetic gamma diagnostics and additionally, AFSI will be used as part of the neutron transport calculation chain to model real diagnostics instead of ideal synthetic diagnostics for quantitative benchmarking.

Optimization of a middle atmosphere diagnostic scheme

NASA Astrophysics Data System (ADS)

Akmaev, Rashid A.

1997-06-01

A new assimilative diagnostic scheme based on the use of a spectral model was recently tested on the CIRA-86 empirical model. It reproduced the observed climatology with an annual global rms temperature deviation of 3.2 K in the 15-110 km layer. The most important new component of the scheme is that the zonal forcing necessary to maintain the observed climatology is diagnosed from empirical data and subsequently substituted into the simulation model at the prognostic stage of the calculation in an annual cycle mode. The simulation results are then quantitatively compared with the empirical model, and the above mentioned rms temperature deviation provides an objective measure of the `distance' between the two climatologies. This quantitative criterion makes it possible to apply standard optimization procedures to the whole diagnostic scheme and/or the model itself. The estimates of the zonal drag have been improved in this study by introducing a nudging (Newtonian-cooling) term into the thermodynamic equation at the diagnostic stage. A proper optimal adjustment of the strength of this term makes it possible to further reduce the rms temperature deviation of simulations down to approximately 2.7 K. These results suggest that direct optimization can successfully be applied to atmospheric model parameter identification problems of moderate dimensionality.

Process-Oriented Diagnostics of Tropical Cyclones in Global Climate Models

NASA Astrophysics Data System (ADS)

Moon, Y.; Kim, D.; Camargo, S. J.; Wing, A. A.; Sobel, A. H.; Bosilovich, M. G.; Murakami, H.; Reed, K. A.; Vecchi, G. A.; Wehner, M. F.; Zarzycki, C. M.; Zhao, M.

2017-12-01

Simulating tropical cyclone (TC) activity with global climate models (GCMs) remains a challenging problem. While some GCMs are able to simulate TC activity that is in good agreement with the observations, many other models exhibit strong biases. Decreasing horizontal grid spacing of the GCM simulations tends to improve the characteristics of simulated TCs, but this enhancement alone does not necessarily lead to greater skill in simulating TC activity. This study uses process-based diagnostics to identify model characteristics that could explain why some GCM simulations are able to produce more realistic TC activity than others. The diagnostics examine how convection, moisture, clouds and related processes are coupled at individual grid points, which yields useful information into how convective parameterizations interact with resolved model dynamics. These diagnostics share similarities with those originally developed to examine the Madden-Julian Oscillations in climate models. This study will examine TCs in eight different GCM simulations performed at NOAA/GFDL, NCAR and NASA that have different horizontal resolutions and ocean coupling. Preliminary results suggest that stronger TCs are closely associated with greater rainfall - thus greater diabatic heating - in the inner-core regions of the storms, which is consistent with previous theoretical studies. Other storm characteristics that can be used to infer why GCM simulations with comparable horizontal grid spacings produce different TC activity will be examined.

In vivo study of the effectiveness of quantitative percussion diagnostics as an indicator of the level of structural pathology of teeth after restoration.

PubMed

Sheets, Cherilyn G; Wu, Jean C; Rashad, Samer; Phelan, Michael; Earthman, James C

2017-02-01

Conventional diagnostic aids based upon imagery and patient symptoms do not indicate whether restorative treatments have eliminated structural pathology. The purpose of this clinical study was to evaluate quantitative percussion diagnostics (QPD), a mechanics-based methodology that tests the structural integrity of teeth noninvasively. The study hypothesis was that QPD would provide knowledge of the structural instability of teeth after restorative work. Eight participants with 60 sites needing restoration were enrolled in an IRB-approved clinical study. Each participant was examined comprehensively, including QPD testing. Each site was disassembled and microscopically video documented, and the results were recorded on a defect assessment sheet. A predictive model was developed for the pathology rating based on normalized fit error (NFE) values using data from the before treatment phase of the study published previously. Each restored site was then tested using QPD. The mean change in NFE values after restoration was evaluated by the pathology rating before treatment. The model was then used to predictively classify the rating after restoration based on the NFE values after treatment. The diagnostic potential of the rating was explored as a marker for risk of pathology after restoration. After restoration, 51 of the 60 sites fell below an NFE of 0.04, representing a greatly stabilized tooth site sample group. Several sites remained in the high-risk category and some increased in pathologic micromovement. Two models were used to determine severity with indicative cutoff points to group sites with similar values. The data support the hypothesis that QPD can indicate a revised level of structural instability of teeth after restoration. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Cognitive balanced model: a conceptual scheme of diagnostic decision making.

PubMed

Lucchiari, Claudio; Pravettoni, Gabriella

2012-02-01

Diagnostic reasoning is a critical aspect of clinical performance, having a high impact on quality and safety of care. Although diagnosis is fundamental in medicine, we still have a poor understanding of the factors that determine its course. According to traditional understanding, all information used in diagnostic reasoning is objective and logically driven. However, these conditions are not always met. Although we would be less likely to make an inaccurate diagnosis when following rational decision making, as described by normative models, the real diagnostic process works in a different way. Recent work has described the major cognitive biases in medicine as well as a number of strategies for reducing them, collectively called debiasing techniques. However, advances have encountered obstacles in achieving implementation into clinical practice. While traditional understanding of clinical reasoning has failed to consider contextual factors, most debiasing techniques seem to fail in raising sound and safer medical praxis. Technological solutions, being data driven, are fundamental in increasing care safety, but they need to consider human factors. Thus, balanced models, cognitive driven and technology based, are needed in day-to-day applications to actually improve the diagnostic process. The purpose of this article, then, is to provide insight into cognitive influences that have resulted in wrong, delayed or missed diagnosis. Using a cognitive approach, we describe the basis of medical error, with particular emphasis on diagnostic error. We then propose a conceptual scheme of the diagnostic process by the use of fuzzy cognitive maps. © 2011 Blackwell Publishing Ltd.

Diagnostics of the power oil-filled transformer equipment of thermal power plants

NASA Astrophysics Data System (ADS)

Eltyshev, D. K.; Khoroshev, N. I.

2016-08-01

Problems concerning improvement of the diagnostics efficiency of the electrical facilities and functioning of the generation and distribution systems through the examples of the power oil-filled transformers, as the responsible elements referring to the electrical part of thermal power plants (TPP), were considered. Research activity is based on the fuzzy logic system allowing working both with statistical and expert information presented in the form of knowledge accumulated during operation of the power oil-filled transformer facilities. The diagnostic algorithm for various types of transformers, with the use of the intellectual estimation model of its thermal state on the basis of the key diagnostic parameters and fuzzy inference hierarchy, was developed. Criteria for taking measures allowing preventing emergencies in the electric power systems were developed. The fuzzy hierarchical model for the state assessment of the power oil-filled transformers of 110 kV, possessing high degree of credibility and setting quite strict requirements to the limits of variables of the equipment diagnostic parameters, was developed. The most frequent defects of the transformer standard elements, related with the disturbance of the isolation properties and instrumentation operation, were revealed after model testing on the real object. Presented results may be used both for the express diagnostics of the transformers state without disconnection from the power line and for more detailed analysis of the defects causes on the basis of the advanced list of the diagnostic parameters; information on those parameters may be received only after complete or partial disconnection.

Evidence-based development of a diagnosis-dependent therapy planning system and its implementation in modern diagnostic software.

PubMed

Ahlers, M O; Jakstat, H A

2005-07-01

The prerequisite for structured individual therapy of craniomandibular dysfunctions is differential diagnostics. Suggestions for the structured recording of findings and their structured evaluation beyond the global diagnosis of "craniomandibular disorders" have been published. Only this structured approach enables computerization of the diagnostic process. The respective software is available for use in practice (CMDcheck for CMD screening, CMDfact for the differential diagnostics). Based on this structured diagnostics, knowledge-based therapy planning is also conceivable. The prerequisite for this would be a model of achieving consensus on the indicated forms of therapy related to the diagnosis. Therefore, a procedure for evidence-based achievement of consensus on suitable forms of therapy in CMD was developed first in multicentric cooperation, and then implemented in corresponding software. The clinical knowledge of experienced specialists was included consciously for the consensus achievement process. At the same time, anonymized mathematical statistical evaluations were used for control and objectification. Different examiners form different departments of several universities working independently of one another assigned the theoretically conceiveable therapeutic alternatives to the already published diagnostic scheme. After anonymization, the correlation of these assignments was then calculated mathematically. For achieving consensus in those cases for which no agreement initally existed, agreement was subsequently arrived at in the course of a consensus conference on the basis of literature evaluations and the discussion of clinical case examples. This consensus in turn finally served as the basis of a therapy planner implemented in the above-mentioned diagnostic software CMDfact. Contributing to quality assurance, the principles of programming this assistant as well as the interface for linking into the diagnostic software are documented and also published here.

Control and Diagnostic Model of Brushless Dc Motor

NASA Astrophysics Data System (ADS)

Abramov, Ivan V.; Nikitin, Yury R.; Abramov, Andrei I.; Sosnovich, Ella V.; Božek, Pavol

2014-09-01

A simulation model of brushless DC motor (BLDC) control and diagnostics is considered. The model has been developed using a freeware complex "Modeling in technical devices". Faults and diagnostic parameters of BLDC are analyzed. A logicallinguistic diagnostic model of BLDC has been developed on basis of fuzzy logic. The calculated rules determine dependence of technical condition on diagnostic parameters, their trends and utilized lifetime of BLDC. Experimental results of BLDC technical condition diagnostics are discussed. It is shown that in the course of BLDC degradation the motor condition change depends on diagnostic parameter values

A new dump system design for stray light reduction of Thomson scattering diagnostic system on EAST

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

Xiao, Shumei; Zang, Qing, E-mail: zangq@ipp.ac.cn; Han, Xiaofeng

Thomson scattering (TS) diagnostic is an important diagnostic for measuring electron temperature and density during plasma discharge. However, the measurement of Thomson scattering signal is disturbed by the stray light easily. The stray light sources in the Experimental Advanced Superconducting Tokamak (EAST) TS diagnostic system were analyzed by a simulation model of the diagnostic system, and simulation results show that the dump system is the primary stray light source. Based on the optics theory and the simulation analysis, a novel dump system including an improved beam trap was proposed and installed. The measurement results indicate that the new dump systemmore » can reduce more than 60% of the stray light for the diagnostic system, and the influence of stray light on the error of measured density decreases.« less

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2015-01-01

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2014-01-01

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

Development of quantitative radioactive methodologies on paper to determine important lateral-flow immunoassay parameters.

PubMed

Mosley, Garrett L; Nguyen, Phuong; Wu, Benjamin M; Kamei, Daniel T

2016-08-07

The lateral-flow immunoassay (LFA) is a well-established diagnostic technology that has recently seen significant advancements due in part to the rapidly expanding fields of paper diagnostics and paper-fluidics. As LFA-based diagnostics become more complex, it becomes increasingly important to quantitatively determine important parameters during the design and evaluation process. However, current experimental methods for determining these parameters have certain limitations when applied to LFA systems. In this work, we describe our novel methods of combining paper and radioactive measurements to determine nanoprobe molarity, the number of antibodies per nanoprobe, and the forward and reverse rate constants for nanoprobe binding to immobilized target on the LFA test line. Using a model LFA system that detects for the presence of the protein transferrin (Tf), we demonstrate the application of our methods, which involve quantitative experimentation and mathematical modeling. We also compare the results of our rate constant experiments with traditional experiments to demonstrate how our methods more appropriately capture the influence of the LFA environment on the binding interaction. Our novel experimental approaches can therefore more efficiently guide the research process for LFA design, leading to more rapid advancement of the field of paper-based diagnostics.

CAD-Based Shielding Analysis for ITER Port Diagnostics

NASA Astrophysics Data System (ADS)

Serikov, Arkady; Fischer, Ulrich; Anthoine, David; Bertalot, Luciano; De Bock, Maartin; O'Connor, Richard; Juarez, Rafael; Krasilnikov, Vitaly

2017-09-01

Radiation shielding analysis conducted in support of design development of the contemporary diagnostic systems integrated inside the ITER ports is relied on the use of CAD models. This paper presents the CAD-based MCNP Monte Carlo radiation transport and activation analyses for the Diagnostic Upper and Equatorial Port Plugs (UPP #3 and EPP #8, #17). The creation process of the complicated 3D MCNP models of the diagnostics systems was substantially accelerated by application of the CAD-to-MCNP converter programs MCAM and McCad. High performance computing resources of the Helios supercomputer allowed to speed-up the MCNP parallel transport calculations with the MPI/OpenMP interface. The found shielding solutions could be universal, reducing ports R&D costs. The shield block behind the Tritium and Deposit Monitor (TDM) optical box was added to study its influence on Shut-Down Dose Rate (SDDR) in Port Interspace (PI) of EPP#17. Influence of neutron streaming along the Lost Alpha Monitor (LAM) on the neutron energy spectra calculated in the Tangential Neutron Spectrometer (TNS) of EPP#8. For the UPP#3 with Charge eXchange Recombination Spectroscopy (CXRS-core), an excessive neutron streaming along the CXRS shutter, which should be prevented in further design iteration.

COMPARISON OF ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTION (ASIR™) AND MODEL-BASED ITERATIVE RECONSTRUCTION (VEO™) FOR PAEDIATRIC ABDOMINAL CT EXAMINATIONS: AN OBSERVER PERFORMANCE STUDY OF DIAGNOSTIC IMAGE QUALITY.

PubMed

Hultenmo, Maria; Caisander, Håkan; Mack, Karsten; Thilander-Klang, Anne

2016-06-01

The diagnostic image quality of 75 paediatric abdominal computed tomography (CT) examinations reconstructed with two different iterative reconstruction (IR) algorithms-adaptive statistical IR (ASiR™) and model-based IR (Veo™)-was compared. Axial and coronal images were reconstructed with 70 % ASiR with the Soft™ convolution kernel and with the Veo algorithm. The thickness of the reconstructed images was 2.5 or 5 mm depending on the scanning protocol used. Four radiologists graded the delineation of six abdominal structures and the diagnostic usefulness of the image quality. The Veo reconstruction significantly improved the visibility of most of the structures compared with ASiR in all subgroups of images. For coronal images, the Veo reconstruction resulted in significantly improved ratings of the diagnostic use of the image quality compared with the ASiR reconstruction. This was not seen for the axial images. The greatest improvement using Veo reconstruction was observed for the 2.5 mm coronal slices. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Enormous knowledge base of disease diagnosis criteria.

PubMed

Xiao, Z H; Xiao, Y H; Pei, J H

1995-01-01

One of the problems in the development of the medical knowledge systems is the limitations of the system's knowledge. It is a common expectation to increase the number of diseases contained in a system. Using a high density knowledge representation method designed by us, we have developed the Enormous Knowledge Base of Disease Diagnosis Criteria (EKBDDC). It contains diagnostic criteria of 1,001 diagnostic entities and describes nearly 4,000 items of diagnostic indicators. It is the core of a huge medical project--the Electronic-Brain Medical Erudite (EBME). This enormous knowledge base was implemented initially on a low-cost popular microcomputer, which can aid in the prompting of typical disease and in teaching of diagnosis. The knowledge base is easy to expand. One of the main goals of EKBDDC is to increase the number of diseases included in it as far as possible using a low-cost computer with a comparatively small storage capacity. For this, we have designed a high density knowledge representation method. Criteria of various diagnostic entities are respectively stored in different records of the knowledge base. Each diagnostic entity corresponds to a diagnostic criterion data set; each data set consists of some diagnostic criterion data values (Table 1); each data is composed of two parts: integer and decimal; the integral part is the coding number of the given diagnostic information, and the decimal part is the diagnostic value of this information to the disease indicated by corresponding record number. For example, 75.02: the integer 75 is the coding number of "hemorrhagic skin rash"; the decimal 0.02 is the diagnostic value of this manifestation for diagnosing allergic purpura. TABULAR DATA, SEE PUBLISHED ABSTRACT. The algebraic sum method, a special form of the weighted summation, is adopted as mathematical model. In EKBDDC, the diagnostic values, which represent the significance of the disease manifestations for diagnosing corresponding diseases, were determined empirically. It is of a great economical, practical, and technical significance to realize enormous knowledge bases of disease diagnosis criteria on a low-cost popular microcomputer. This is beneficial for the developing countries to popularize medical informatics. To create the enormous international computer-aided diagnosis system, one may jointly develop the unified modules of disease diagnosis criteria used to "inlay" relevant computer-aided diagnosis systems. It is just like assembling a house using prefabricated panels.

Study design requirements for RNA sequencing-based breast cancer diagnostics.

PubMed

Mer, Arvind Singh; Klevebring, Daniel; Grönberg, Henrik; Rantalainen, Mattias

2016-02-01

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.

Model-based diagnostics of gas turbine engine lubrication systems

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

Byington, C.S.

1998-09-01

The objective of the current research was to develop improved methodology for diagnosing anomalies and maintaining oil lubrication systems for gas turbine engines. The effort focused on the development of reasoning modules that utilize the existing, inexpensive sensors and are applicable to on-line monitoring within the full-authority digital engine controller (FADEC) of the engine. The target application is the Enhanced TF-40B gas turbine engine that powers the Landing Craft Air Cushion (LCAC) platform. To accomplish the development of the requisite data fusion algorithms and automated reasoning for the diagnostic modules, Penn State ARL produced a generic Turbine Engine Lubrication Systemmore » Simulator (TELSS) and Data Fusion Workbench (DFW). TELSS is a portable simulator code that calculates lubrication system parameters based upon one-dimensional fluid flow resistance network equations. Validation of the TF- 40B modules was performed using engineering and limited test data. The simulation model was used to analyze operational data from the LCAC fleet. The TELSS, as an integral portion of the DFW, provides the capability to experiment with combinations of variables and feature vectors that characterize normal and abnormal operation of the engine lubrication system. The model-based diagnostics approach is applicable to all gas turbine engines and mechanical transmissions with similar pressure-fed lubrication systems.« less

Cost-effectiveness of a novel blood-pool contrast agent in the setting of chest pain evaluation in an emergency department.

PubMed

Espinosa, Gabriela; Annapragada, Ananth

2013-10-01

We evaluated three diagnostic strategies with the objective of comparing the current standard of care for individuals presenting acute chest pain and no history of coronary artery disease (CAD) with a novel diagnostic strategy using an emerging technology (blood-pool contrast agent [BPCA]) to identify the potential benefits and cost reductions. A decision analytic model of diagnostic strategies and outcomes using a BPCA and a conventional agent for CT angiography (CTA) in patients with acute chest pain was built. The model was used to evaluate three diagnostic strategies: CTA using a BPCA followed by invasive coronary angiography (ICA), CTA using a conventional agent followed by ICA, and ICA alone. The use of the two CTA-based triage tests before ICA in a population with a CAD prevalence of less than 47% was predicted to be more cost-effective than ICA alone. Using the base-case values and a cost premium for BPCA over the conventional CT agent (cost of BPCA ≈ 5× that of a conventional agent) showed that CTA with a BPCA before ICA resulted in the most cost-effective strategy; the other strategies were ruled out by simple dominance. The model strongly depends on the rates of complications from the diagnostic tests included in the model. In a population with an elevated risk of contrast-induced nephropathy (CIN), a significant premium cost per BPCA dose still resulted in the alternative whereby CTA using BPCA was more cost-effective than CTA using a conventional agent. A similar effect was observed for potential complications resulting from the BPCA injection. Conversely, in the presence of a similar complication rate from BPCA, the diagnostic strategy of CTA using a conventional agent would be the optimal alternative. BPCAs could have a significant impact in the diagnosis of acute chest pain, in particular for populations with high incidences of CIN. In addition, a BPCA strategy could garner further savings if currently excluded phenomena including renal disease and incidental findings were included in the decision model.

Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia

PubMed Central

Yang, Quan‐Jun; Zhao, Jiang‐Rong; Hao, Juan; Li, Bin; Huo, Yan; Han, Yong‐Long; Wan, Li‐Li; Li, Jie; Huang, Jinlu; Lu, Jin

2017-01-01

Abstract Background Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model. Methods Eighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model. Results Forty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88 × log(Carnosine) −239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set. Conclusions This metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at‐risk populations through the detection of serum metabolites. PMID:29152916

Qualitative and quantitative detection of T7 bacteriophages using paper based sandwich ELISA.

PubMed

Khan, Mohidus Samad; Pande, Tripti; van de Ven, Theo G M

2015-08-01

Viruses cause many infectious diseases and consequently epidemic health threats. Paper based diagnostics and filters can offer attractive options for detecting and deactivating pathogens. However, due to their infectious characteristics, virus detection using paper diagnostics is more challenging compared to the detection of bacteria, enzymes, DNA or antigens. The major objective of this study was to prepare reliable, degradable and low cost paper diagnostics to detect viruses, without using sophisticated optical or microfluidic analytical instruments. T7 bacteriophage was used as a model virus. A paper based sandwich ELISA technique was developed to detect and quantify the T7 phages in solution. The paper based sandwich ELISA detected T7 phage concentrations as low as 100 pfu/mL to as high as 10(9) pfu/mL. The compatibility of paper based sandwich ELISA with the conventional titre count was tested using T7 phage solutions of unknown concentrations. The paper based sandwich ELISA technique is faster and economical compared to the traditional detection techniques. Therefore, with proper calibration and right reagents, and by following the biosafety regulations, the paper based technique can be said to be compatible and economical to the sophisticated laboratory diagnostic techniques applied to detect pathogenic viruses and other microorganisms. Copyright © 2015 Elsevier B.V. All rights reserved.

OpenID connect as a security service in Cloud-based diagnostic imaging systems

NASA Astrophysics Data System (ADS)

Ma, Weina; Sartipi, Kamran; Sharghi, Hassan; Koff, David; Bak, Peter

2015-03-01

The evolution of cloud computing is driving the next generation of diagnostic imaging (DI) systems. Cloud-based DI systems are able to deliver better services to patients without constraining to their own physical facilities. However, privacy and security concerns have been consistently regarded as the major obstacle for adoption of cloud computing by healthcare domains. Furthermore, traditional computing models and interfaces employed by DI systems are not ready for accessing diagnostic images through mobile devices. RESTful is an ideal technology for provisioning both mobile services and cloud computing. OpenID Connect, combining OpenID and OAuth together, is an emerging REST-based federated identity solution. It is one of the most perspective open standards to potentially become the de-facto standard for securing cloud computing and mobile applications, which has ever been regarded as "Kerberos of Cloud". We introduce OpenID Connect as an identity and authentication service in cloud-based DI systems and propose enhancements that allow for incorporating this technology within distributed enterprise environment. The objective of this study is to offer solutions for secure radiology image sharing among DI-r (Diagnostic Imaging Repository) and heterogeneous PACS (Picture Archiving and Communication Systems) as well as mobile clients in the cloud ecosystem. Through using OpenID Connect as an open-source identity and authentication service, deploying DI-r and PACS to private or community clouds should obtain equivalent security level to traditional computing model.

Modeling and Diagnostic Software for Liquefying-Fuel Rockets

NASA Technical Reports Server (NTRS)

Poll, Scott; Iverson, David; Ou, Jeremy; Sanderfer, Dwight; Patterson-Hine, Ann

2005-01-01

A report presents a study of five modeling and diagnostic computer programs considered for use in an integrated vehicle health management (IVHM) system during testing of liquefying-fuel hybrid rocket engines in the Hybrid Combustion Facility (HCF) at NASA Ames Research Center. Three of the programs -- TEAMS, L2, and RODON -- are model-based reasoning (or diagnostic) programs. The other two programs -- ICS and IMS -- do not attempt to isolate the causes of failures but can be used for detecting faults. In the study, qualitative models (in TEAMS and L2) and quantitative models (in RODON) having varying scope and completeness were created. Each of the models captured the structure and behavior of the HCF as a physical system. It was noted that in the cases of the qualitative models, the temporal aspects of the behavior of the HCF and the abstraction of sensor data are handled outside of the models, and it is necessary to develop additional code for this purpose. A need for additional code was also noted in the case of the quantitative model, though the amount of development effort needed was found to be less than that for the qualitative models.

Telangiectatic osteosarcoma: Outcome analyses and a diagnostic model for differentiation from aneurysmal bone cyst.

PubMed

Yin, Jun-Qiang; Fu, Yi-Wei; Xie, Xian-Biao; Cheng, Xiao-Yu; Yang, Xiao-Yu; Liu, Wei-Hai; Tu, Jian; Gao, Zhen-Hua; Shen, Jing-Nan

2018-06-01

Telangiectatic osteosarcoma (TOS), a rare variant of osteosarcoma, may be easily misdiagnosed as aneurysmal bone cyst (ABC). The aims of this study were to investigate the diagnostic and prognostic factors of TOS by reviewing our experience with TOS and to develop a diagnostic model that may distinguish TOS from ABC. We identified 51 cases of TOS treated at the First Affiliated Hospital of Sun Yat-Sen University from March 2001 to January 2016 and reviewed their records, imaging information and pathological studies. A diagnostic model was developed to differentiate TOS and ABC by Bayes discriminant analysis and was evaluated. The log-rank test was used to analyze the prognostic factors of TOS and to compare the outcome differences between TOS and other high-grade osteosarcoma subtypes. The multi-disciplinary diagnostic method employed that combined clinical, imaging, and pathological studies enhanced the diagnostic accuracy. Age 18 years or younger and pathologic fracture were more common among the TOS patients than among the ABC patients (P = .004 and .005, respectively). The average white blood cell (WBC), platelet, lactate dehydrogenase (LDH), and alkaline phosphatase (ALP) values of the TOS patients were higher than those of the ABC patients ( P = .002, .003, .007, and .007, respectively). Our diagnostic model, including the aforementioned factors, accurately predicted 62% and 78% of the TOS patients in the training and validation sets, respectively. The 5-year estimates of event-free survival and overall survival of the TOS patients were 52.5 ± 9.4% and 54.9 ± 8.8%, respectively, which were similar to those of patients with other osteosarcoma subtypes ( P = .950 and .615, respectively). Tumor volume and the LDH level were predictive prognostic factors ( P = .040 and .044) but not the presence of pathologic fracture or misdiagnosis ( P = .424 and .632, all respectively). The multi-disciplinary diagnostic method and diagnostic model based on predictive factors, i.e. , age, the presence of pathologic fracture, and platelet, LDH, ALP and WBC levels, aided the differentiation of TOS and ABC. Smaller tumors and normal LDH levels were associated with better outcomes.

A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects

PubMed Central

Ng, Selina S. Y.; Tse, Peter W.; Tsui, Kwok L.

2014-01-01

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets. PMID:24419162

A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects.

PubMed

Ng, Selina S Y; Tse, Peter W; Tsui, Kwok L

2014-01-13

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

Quad-phased data mining modeling for dementia diagnosis.

PubMed

Bang, Sunjoo; Son, Sangjoon; Roh, Hyunwoong; Lee, Jihye; Bae, Sungyun; Lee, Kyungwon; Hong, Changhyung; Shin, Hyunjung

2017-05-18

The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians. Therefore, in this paper we propose a quad-phased data mining modeling consisting of 4 modules. In Proposer Module, significant diagnostic criteria are selected that are effective for diagnostics. Then in Predictor Module, a model is constructed to predict and diagnose dementia based on a machine learning algorism. To help clinical physicians understand results of the predictive model better, in Descriptor Module, we interpret causes of diagnostics by profiling patient groups. Lastly, in Visualization Module, we provide visualization to effectively explore characteristics of patient groups. The proposed model is applied for CREDOS study which contains clinical data collected from 37 university-affiliated hospitals in republic of Korea from year 2005 to 2013. This research is an intelligent system enabling intuitive collaboration between CAD system and physicians. And also, improved evaluation process is able to effectively reduce time and cost consuming for clinicians and patients.

Study of a high power hydrogen beam diagnostic based on secondary electron emission.

PubMed

Sartori, E; Panasenkov, A; Veltri, P; Serianni, G; Pasqualotto, R

2016-11-01

In high power neutral beams for fusion, beam uniformity is an important figure of merit. Knowing the transverse power profile is essential during the initial phases of beam source operation, such as those expected for the ITER heating neutral beam (HNB) test facility. To measure it a diagnostic technique is proposed, based on the collection of secondary electrons generated by beam-surface and beam-gas interactions, by an array of positively biased collectors placed behind the calorimeter tubes. This measurement showed in the IREK test stand good proportionality to the primary beam current. To investigate the diagnostic performances in different conditions, we developed a numerical model of secondary electron emission, induced by beam particle impact on the copper tubes, and reproducing the cascade of secondary emission caused by successive electron impacts. The model is first validated against IREK measurements. It is then applied to the HNB case, to assess the locality of the measurement, the proportionality to the beam current density, and the influence of beam plasma.

Generalizing on Multiple Grounds: Performance Learning in Model-Based Troubleshooting

DTIC Science & Technology

1989-02-01

Aritificial Intelligence , 24, 1984. [Ble88] Guy E. Blelloch. Scan Primitives and Parallel Vector Models. PhD thesis, Artificial Intelligence Laboratory...Diagnostic reasoning based on strcture and behavior. Aritificial Intelligence , 24, 1984. [dK86] J. de Kleer. An assumption-based truth maintenance system...diagnosis. Aritificial Intelligence , 24. �. )3 94 BIBLIOGRAPHY [Ham87] Kristian J. Hammond. Learning to anticipate and avoid planning prob- lems

An informatics model for guiding assembly of telemicrobiology workstations for malaria collaborative diagnostics using commodity products and open-source software.

PubMed

Suhanic, West; Crandall, Ian; Pennefather, Peter

2009-07-17

Deficits in clinical microbiology infrastructure exacerbate global infectious disease burdens. This paper examines how commodity computation, communication, and measurement products combined with open-source analysis and communication applications can be incorporated into laboratory medicine microbiology protocols. Those commodity components are all now sourceable globally. An informatics model is presented for guiding the use of low-cost commodity components and free software in the assembly of clinically useful and usable telemicrobiology workstations. The model incorporates two general principles: 1) collaborative diagnostics, where free and open communication and networking applications are used to link distributed collaborators for reciprocal assistance in organizing and interpreting digital diagnostic data; and 2) commodity engineering, which leverages globally available consumer electronics and open-source informatics applications, to build generic open systems that measure needed information in ways substantially equivalent to more complex proprietary systems. Routine microscopic examination of Giemsa and fluorescently stained blood smears for diagnosing malaria is used as an example to validate the model. The model is used as a constraint-based guide for the design, assembly, and testing of a functioning, open, and commoditized telemicroscopy system that supports distributed acquisition, exploration, analysis, interpretation, and reporting of digital microscopy images of stained malarial blood smears while also supporting remote diagnostic tracking, quality assessment and diagnostic process development. The open telemicroscopy workstation design and use-process described here can address clinical microbiology infrastructure deficits in an economically sound and sustainable manner. It can boost capacity to deal with comprehensive measurement of disease and care outcomes in individuals and groups in a distributed and collaborative fashion. The workstation enables local control over the creation and use of diagnostic data, while allowing for remote collaborative support of diagnostic data interpretation and tracking. It can enable global pooling of malaria disease information and the development of open, participatory, and adaptable laboratory medicine practices. The informatic model highlights how the larger issue of access to generic commoditized measurement, information processing, and communication technology in both high- and low-income countries can enable diagnostic services that are much less expensive, but substantially equivalent to those currently in use in high-income countries.

Reconstruction method for data protection in telemedicine systems

NASA Astrophysics Data System (ADS)

Buldakova, T. I.; Suyatinov, S. I.

2015-03-01

In the report the approach to protection of transmitted data by creation of pair symmetric keys for the sensor and the receiver is offered. Since biosignals are unique for each person, their corresponding processing allows to receive necessary information for creation of cryptographic keys. Processing is based on reconstruction of the mathematical model generating time series that are diagnostically equivalent to initial biosignals. Information about the model is transmitted to the receiver, where the restoration of physiological time series is performed using the reconstructed model. Thus, information about structure and parameters of biosystem model received in the reconstruction process can be used not only for its diagnostics, but also for protection of transmitted data in telemedicine complexes.

Internet-based system for simulation-based medical planning for cardiovascular disease.

PubMed

Steele, Brooke N; Draney, Mary T; Ku, Joy P; Taylor, Charles A

2003-06-01

Current practice in vascular surgery utilizes only diagnostic and empirical data to plan treatments, which does not enable quantitative a priori prediction of the outcomes of interventions. We have previously described simulation-based medical planning methods to model blood flow in arteries and plan medical treatments based on physiologic models. An important consideration for the design of these patient-specific modeling systems is the accessibility to physicians with modest computational resources. We describe a simulation-based medical planning environment developed for the World Wide Web (WWW) using the Virtual Reality Modeling Language (VRML) and the Java programming language.

Deep learning based syndrome diagnosis of chronic gastritis.

PubMed

Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

PubMed Central

Liu, Guo-Ping; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:24734118

Gut feelings as a third track in general practitioners' diagnostic reasoning.

PubMed

Stolper, Erik; Van de Wiel, Margje; Van Royen, Paul; Van Bokhoven, Marloes; Van der Weijden, Trudy; Dinant, Geert Jan

2011-02-01

General practitioners (GPs) are often faced with complicated, vague problems in situations of uncertainty that they have to solve at short notice. In such situations, gut feelings seem to play a substantial role in their diagnostic process. Qualitative research distinguished a sense of alarm and a sense of reassurance. However, not every GP trusted their gut feelings, since a scientific explanation is lacking. This paper explains how gut feelings arise and function in GPs' diagnostic reasoning. The paper reviews literature from medical, psychological and neuroscientific perspectives. Gut feelings in general practice are based on the interaction between patient information and a GP's knowledge and experience. This is visualized in a knowledge-based model of GPs' diagnostic reasoning emphasizing that this complex task combines analytical and non-analytical cognitive processes. The model integrates the two well-known diagnostic reasoning tracks of medical decision-making and medical problem-solving, and adds gut feelings as a third track. Analytical and non-analytical diagnostic reasoning interacts continuously, and GPs use elements of all three tracks, depending on the task and the situation. In this dual process theory, gut feelings emerge as a consequence of non-analytical processing of the available information and knowledge, either reassuring GPs or alerting them that something is wrong and action is required. The role of affect as a heuristic within the physician's knowledge network explains how gut feelings may help GPs to navigate in a mostly efficient way in the often complex and uncertain diagnostic situations of general practice. Emotion research and neuroscientific data support the unmistakable role of affect in the process of making decisions and explain the bodily sensation of gut feelings.The implications for health care practice and medical education are discussed.

Gut Feelings as a Third Track in General Practitioners’ Diagnostic Reasoning

PubMed Central

Van de Wiel, Margje; Van Royen, Paul; Van Bokhoven, Marloes; Van der Weijden, Trudy; Dinant, Geert Jan

2010-01-01

Background General practitioners (GPs) are often faced with complicated, vague problems in situations of uncertainty that they have to solve at short notice. In such situations, gut feelings seem to play a substantial role in their diagnostic process. Qualitative research distinguished a sense of alarm and a sense of reassurance. However, not every GP trusted their gut feelings, since a scientific explanation is lacking. Objective This paper explains how gut feelings arise and function in GPs’ diagnostic reasoning. Approach The paper reviews literature from medical, psychological and neuroscientific perspectives. Conclusions Gut feelings in general practice are based on the interaction between patient information and a GP’s knowledge and experience. This is visualized in a knowledge-based model of GPs’ diagnostic reasoning emphasizing that this complex task combines analytical and non-analytical cognitive processes. The model integrates the two well-known diagnostic reasoning tracks of medical decision-making and medical problem-solving, and adds gut feelings as a third track. Analytical and non-analytical diagnostic reasoning interacts continuously, and GPs use elements of all three tracks, depending on the task and the situation. In this dual process theory, gut feelings emerge as a consequence of non-analytical processing of the available information and knowledge, either reassuring GPs or alerting them that something is wrong and action is required. The role of affect as a heuristic within the physician’s knowledge network explains how gut feelings may help GPs to navigate in a mostly efficient way in the often complex and uncertain diagnostic situations of general practice. Emotion research and neuroscientific data support the unmistakable role of affect in the process of making decisions and explain the bodily sensation of gut feelings.The implications for health care practice and medical education are discussed. PMID:20967509

Development and validation of risk models and molecular diagnostics to permit personalized management of cancer.

PubMed

Pu, Xia; Ye, Yuanqing; Wu, Xifeng

2014-01-01

Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation. © 2013 American Cancer Society.

Diagnostics for generalized linear hierarchical models in network meta-analysis.

PubMed

Zhao, Hong; Hodges, James S; Carlin, Bradley P

2017-09-01

Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's substantive conclusions. In this paper, we examine such discrepancies from a diagnostic point of view. Our methods seek to detect influential and outlying observations in NMA at a trial-by-arm level. These observations may have a large effect on the parameter estimates in NMA, or they may deviate markedly from other observations. We develop formal diagnostics for a Bayesian hierarchical model to check the effect of deleting any observation. Diagnostics are specified for generalized linear hierarchical NMA models and investigated for both published and simulated datasets. Results from our example dataset using either contrast- or arm-based models and from the simulated datasets indicate that the sources of inconsistency in NMA tend not to be influential, though results from the example dataset suggest that they are likely to be outliers. This mimics a familiar result from linear model theory, in which outliers with low leverage are not influential. Future extensions include incorporating baseline covariates and individual-level patient data. Copyright © 2017 John Wiley & Sons, Ltd.

Immune-Response Patterns and Next Generation Sequencing Diagnostics for the Detection of Mycoses in Patients with Septic Shock—Results of a Combined Clinical and Experimental Investigation

PubMed Central

Decker, Sebastian O.; Sigl, Annette; Grumaz, Christian; Stevens, Philip; Vainshtein, Yevhen; Zimmermann, Stefan; Weigand, Markus A.; Hofer, Stefan; Sohn, Kai; Brenner, Thorsten

2017-01-01

Fungi are of increasing importance in sepsis. However, culture-based diagnostic procedures are associated with relevant weaknesses. Therefore, culture- and next-generation sequencing (NGS)-based fungal findings as well as corresponding plasma levels of β-d-glucan, interferon gamma (INF-γ), tumor necrosis factor alpha (TNF-α), interleukin (IL)-2, -4, -6, -10, -17A, and mid-regional proadrenomedullin (MR-proADM) were evaluated in 50 septic patients at six consecutive time points within 28 days after sepsis onset. Furthermore, immune-response patterns during infections with Candida spp. were studied in a reconstituted human epithelium model. In total, 22% (n = 11) of patients suffered from a fungal infection. An NGS-based diagnostic approach appeared to be suitable for the identification of fungal pathogens in patients suffering from fungemia as well as in patients with negative blood cultures. Moreover, MR-proADM and IL-17A in plasma proved suitable for the identification of patients with a fungal infection. Using RNA-seq., adrenomedullin (ADM) was shown to be a target gene which is upregulated early after an epithelial infection with Candida spp. In summary, an NGS-based diagnostic approach was able to close the diagnostic gap of routinely used culture-based diagnostic procedures, which can be further facilitated by plasmatic measurements of MR-proADM and IL-17A. In addition, ADM was identified as an early target gene in response to epithelial infections with Candida spp. PMID:28820494

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Reducing cognitive skill decay and diagnostic error: theory-based practices for continuing education in health care.

PubMed

Weaver, Sallie J; Newman-Toker, David E; Rosen, Michael A

2012-01-01

Missed, delayed, or wrong diagnoses can have a severe impact on patients, providers, and the entire health care system. One mechanism implicated in such diagnostic errors is the deterioration of cognitive diagnostic skills that are used rarely or not at all over a prolonged period of time. Existing evidence regarding maintenance of effective cognitive reasoning skills in the clinical education, organizational training, and human factors literatures suggest that continuing education plays a critical role in mitigating and managing diagnostic skill decay. Recent models also underscore the role of system level factors (eg, cognitive decision support tools, just-in-time training opportunities) in supporting clinical reasoning process. The purpose of this manuscript is to offer a multidisciplinary review of cognitive models of clinical decision making skills in order to provide a list of best practices for supporting continuous improvement and maintenance of cognitive diagnostic processes through continuing education. Copyright © 2012 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical Education.

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.

PubMed

Mercan, Ezgi; Aksoy, Selim; Shapiro, Linda G; Weaver, Donald L; Brunyé, Tad T; Elmore, Joann G

2016-08-01

Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.

[Mathematical model of technical equipment of a clinical-diagnostic laboratory].

PubMed

Bukin, S I; Busygin, D V; Tilevich, M E

1990-01-01

The paper is concerned with the problems of technical equipment of standard clinico-diagnostic laboratories (CDL) in this country. The authors suggest a mathematic model that may minimize expenditures for laboratory studies. The model enables the following problems to be solved: to issue scientifically-based recommendations for technical equipment of CDL; to validate the medico-technical requirements for newly devised items; to select the optimum types of uniform items; to define optimal technical decisions at the stage of the design; to determine the lab assistant's labour productivity and the cost of some investigations; to compute the medical laboratory engineering requirement for treatment and prophylactic institutions of this country.

[Conceptual foundation of the operational diagnostic approach in psychiatry].

PubMed

Jäger, M; Strauss, A; Frasch, K; Becker, T

2007-08-01

Based on the pioneering work of Emil Kraepelin, Karl Jaspers, Kurt Schneider and representatives of logical empiricism, the basic principles of the operational diagnostic approach in psychiatry are described. Operational diagnostic systems like ICD-10 and DSM-IV aimed at a standardisation of psychiatric language which can be accepted by different schools in psychiatry. However, ICD-10 and DSM-IV should not be misinterpreted as "nosology" because they do not reflect a specific model of psychiatric diseases. The advantages of operational diagnostic systems as instruments for communication in a clinical and scientific context are limited by the fact that they disregard the subjective psychopathology. This dimension, however, deserves attention in clinic and research.

Assessing the value-adding impact of diagnostic-type tests on drug development and marketing.

PubMed

Blair, Edward D

2008-01-01

We explore the cash value of the companion diagnostics opportunity from the perspective of the pharmaceutical partner. Cashflow-based modeling is used to demonstrate the potential financial benefits of key relationships between the pharmaceutical and diagnostics industries. In four scenarios, the uplift in the net present value (NPV) of a proprietary medicine can exceed $US1.8 billion. By simple extrapolation, the uplifted NPV calculations allow realistic and plausible estimates of the companion diagnostic opportunity to be in the region of $US40 billion to $US90 billion. It is expected that such market valuation could drive a macroeconomic change that shifts healthcare practice from reactionary disease-treatment to proactive health maintenance.

Development of the Internet addiction scale based on the Internet Gaming Disorder criteria suggested in DSM-5.

PubMed

Cho, Hyun; Kwon, Min; Choi, Ji-Hye; Lee, Sang-Kyu; Choi, Jung Seok; Choi, Sam-Wook; Kim, Dai-Jin

2014-09-01

This study was conducted to develop and validate a standardized self-diagnostic Internet addiction (IA) scale based on the diagnosis criteria for Internet Gaming Disorder (IGD) in the Diagnostic and Statistical Manual of Mental Disorder, 5th edition (DSM-5). Items based on the IGD diagnosis criteria were developed using items of the previous Internet addiction scales. Data were collected from a community sample. The data were divided into two sets, and confirmatory factor analysis (CFA) was performed repeatedly. The model was modified after discussion with professionals based on the first CFA results, after which the second CFA was performed. The internal consistency reliability was generally good. The items that showed significantly low correlation values based on the item-total correlation of each factor were excluded. After the first CFA was performed, some factors and items were excluded. Seven factors and 26 items were prepared for the final model. The second CFA results showed good general factor loading, Squared Multiple Correlation (SMC) and model fit. The model fit of the final model was good, but some factors were very highly correlated. It is recommended that some of the factors be refined through further studies. Copyright © 2014. Published by Elsevier Ltd.

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

Examinations of electron temperature calculation methods in Thomson scattering diagnostics.

PubMed

Oh, Seungtae; Lee, Jong Ha; Wi, Hanmin

2012-10-01

Electron temperature from Thomson scattering diagnostic is derived through indirect calculation based on theoretical model. χ-square test is commonly used in the calculation, and the reliability of the calculation method highly depends on the noise level of input signals. In the simulations, noise effects of the χ-square test are examined and scale factor test is proposed as an alternative method.

Developing a Learning Progression for Number Sense Based on the Rule Space Model in China

ERIC Educational Resources Information Center

Chen, Fu; Yan, Yue; Xin, Tao

2017-01-01

The current study focuses on developing the learning progression of number sense for primary school students, and it applies a cognitive diagnostic model, the rule space model, to data analysis. The rule space model analysis firstly extracted nine cognitive attributes and their hierarchy model from the analysis of previous research and the…

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Modeling and design of a beam emission spectroscopy diagnostic for the negative ion source NIO1

NASA Astrophysics Data System (ADS)

Barbisan, M.; Zaniol, B.; Cavenago, M.; Pasqualotto, R.

2014-02-01

Consorzio RFX and INFN-LNL are building a flexible small ion source (Negative Ion Optimization 1, NIO1) capable of producing about 130 mA of H- ions accelerated at 60 KeV. Aim of the experiment is to test and develop the instrumentation for SPIDER and MITICA, the prototypes, respectively, of the negative ion sources and of the whole neutral beam injectors which will operate in the ITER experiment. As SPIDER and MITICA, NIO1 will be monitored with beam emission spectroscopy (BES), a non-invasive diagnostic based on the analysis of the spectrum of the Hα emission produced by the interaction of the energetic ions with the background gas. Aim of BES is to monitor direction, divergence, and uniformity of the ion beam. The precision of these measurements depends on a number of factors related to the physics of production and acceleration of the negative ions, to the geometry of the beam, and to the collection optics. These elements were considered in a set of codes developed to identify the configuration of the diagnostic which minimizes the measurement errors. The model was already used to design the BES diagnostic for SPIDER and MITICA. The paper presents the model and describes its application to design the BES diagnostic in NIO1.

Integration of Diagnostics into Ground Equipment Study. Volume 1

DTIC Science & Technology

2004-07-30

Marine Corps V-22, CH-53E, MH-53E, SH- 60B, MH- 60S /R, AH-1Z and UH -1Y aircraft. In addition, 30 systems are in delivery to the US Army Aviation Applied...simultaneous) can be connected to the VMEP system, which is based on a PC-104 platform and a 233MHz processor. The AH-64 Apache and UH - 60 Blackhawk are outfitted...34A Model-Based Health and Usage Monitoring and Diagnostic System for the UH - 60 Helicopter," Proceedings of the American Helicopter Society 57th

Student Modeling and Ab Initio Language Learning.

ERIC Educational Resources Information Center

Heift, Trude; Schulze, Mathias

2003-01-01

Provides examples of student modeling techniques that have been employed in computer-assisted language learning over the past decade. Describes two systems for learning German: "German Tutor" and "Geroline." Shows how a student model can support computerized adaptive language testing for diagnostic purposes in a Web-based language learning…

On the climate policy implications of substitutability and flexibility in the economy: An in-depth integrated assessment model diagnostic

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

Craxton, Melanie; Merrick, James; Makridis, Christos

This paper conducts an in-depth model diagnostic exercise for two parameters, 1) the elasticity of substitution between the capital/labour aggregate and the energy aggregate in the Integrated Assessment Model (IAM) MERGE's production function and 2) the rate at which new technologies can be deployed within the energy system. We show that in a more complementary world the model's ability to adjust the carbon intensity of its energy sector is more important whereas in a more substitutable world the ability to expand carbon free technologies is of lesser relative importance. The uncertainty in the literature surrounding the elasticity of substitution parameter,more » its interaction with the mechanisms of technical change, and the associated danger of grounding forward-looking analyses in historically based parameters lend support to the importance of such a diagnostic exercise. Building on work from model intercomparison studies, we investigate whether a given model's choice of strategy is primarily a function of the choice of its parameter values or its structure. As a result, a deeper understanding of what drives model behaviour is beneficial to both modellers and the policymakers who utilise their insights and output.« less

On the climate policy implications of substitutability and flexibility in the economy: An in-depth integrated assessment model diagnostic

DOE PAGES

Craxton, Melanie; Merrick, James; Makridis, Christos; ...

2017-07-12

This paper conducts an in-depth model diagnostic exercise for two parameters, 1) the elasticity of substitution between the capital/labour aggregate and the energy aggregate in the Integrated Assessment Model (IAM) MERGE's production function and 2) the rate at which new technologies can be deployed within the energy system. We show that in a more complementary world the model's ability to adjust the carbon intensity of its energy sector is more important whereas in a more substitutable world the ability to expand carbon free technologies is of lesser relative importance. The uncertainty in the literature surrounding the elasticity of substitution parameter,more » its interaction with the mechanisms of technical change, and the associated danger of grounding forward-looking analyses in historically based parameters lend support to the importance of such a diagnostic exercise. Building on work from model intercomparison studies, we investigate whether a given model's choice of strategy is primarily a function of the choice of its parameter values or its structure. As a result, a deeper understanding of what drives model behaviour is beneficial to both modellers and the policymakers who utilise their insights and output.« less

Kinetic Description of the Impedance Probe

NASA Astrophysics Data System (ADS)

Oberrath, Jens; Lapke, Martin; Mussenbrock, Thomas; Brinkmann, Ralf

2011-10-01

Active plasma resonance spectroscopy is a well known diagnostic method. Many concepts of this method are theoretically investigated and realized as a diagnostic tool, one of which is the impedance probe (IP). The application of such a probe in plasmas with pressures of a few Pa raises the question whether kinetic effects have to be taken into account or not. To address this question a kinetic model is necessary. A general kinetic model for an electrostatic concept of active plasma spectroscopy was presented by R.P. Brinkmann and can be used to describe the multipole resonance probe (MRP). In principle the IP is interpretable as a special case of the MRP in lower order. Thus, we are able to describe the IP by the kinetic model of the MRP. Based on this model we derive a solution to investigate the influence of kinetic effects to the resonance behavior of the IP. Active plasma resonance spectroscopy is a well known diagnostic method. Many concepts of this method are theoretically investigated and realized as a diagnostic tool, one of which is the impedance probe (IP). The application of such a probe in plasmas with pressures of a few Pa raises the question whether kinetic effects have to be taken into account or not. To address this question a kinetic model is necessary. A general kinetic model for an electrostatic concept of active plasma spectroscopy was presented by R.P. Brinkmann and can be used to describe the multipole resonance probe (MRP). In principle the IP is interpretable as a special case of the MRP in lower order. Thus, we are able to describe the IP by the kinetic model of the MRP. Based on this model we derive a solution to investigate the influence of kinetic effects to the resonance behavior of the IP. The authors acknowledge the support by the Deutsche Forschungsgemeinschaft (DFG) via the Ruhr University Research School and the Federal Ministry of Education and Research in frame of the PluTO project.

Development of an On-board Failure Diagnostics and Prognostics System for Solid Rocket Booster

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry G.; Osipov, Vyatcheslav V.; Timucin, Dogan A.; Uckun, Serdar

2009-01-01

We develop a case breach model for the on-board fault diagnostics and prognostics system for subscale solid-rocket boosters (SRBs). The model development was motivated by recent ground firing tests, in which a deviation of measured time-traces from the predicted time-series was observed. A modified model takes into account the nozzle ablation, including the effect of roughness of the nozzle surface, the geometry of the fault, and erosion and burning of the walls of the hole in the metal case. The derived low-dimensional performance model (LDPM) of the fault can reproduce the observed time-series data very well. To verify the performance of the LDPM we build a FLUENT model of the case breach fault and demonstrate a good agreement between theoretical predictions based on the analytical solution of the model equations and the results of the FLUENT simulations. We then incorporate the derived LDPM into an inferential Bayesian framework and verify performance of the Bayesian algorithm for the diagnostics and prognostics of the case breach fault. It is shown that the obtained LDPM allows one to track parameters of the SRB during the flight in real time, to diagnose case breach fault, and to predict its values in the future. The application of the method to fault diagnostics and prognostics (FD&P) of other SRB faults modes is discussed.

The Buffer Diagnostic Prototype: A fault isolation application using CLIPS

NASA Technical Reports Server (NTRS)

Porter, Ken

1994-01-01

This paper describes problem domain characteristics and development experiences from using CLIPS 6.0 in a proof-of-concept troubleshooting application called the Buffer Diagnostic Prototype. The problem domain is a large digital communications subsystems called the real-time network (RTN), which was designed to upgrade the launch processing system used for shuttle support at KSC. The RTN enables up to 255 computers to share 50,000 data points with millisecond response times. The RTN's extensive built-in test capability but lack of any automatic fault isolation capability presents a unique opportunity for a diagnostic expert system application. The Buffer Diagnostic Prototype addresses RTN diagnosis with a multiple strategy approach. A novel technique called 'faulty causality' employs inexact qualitative models to process test results. Experimental knowledge provides a capability to recognize symptom-fault associations. The implementation utilizes rule-based and procedural programming techniques, including a goal-directed control structure and simple text-based generic user interface that may be reusable for other rapid prototyping applications. Although limited in scope, this project demonstrates a diagnostic approach that may be adapted to troubleshoot a broad range of equipment.

Automated extraction of knowledge for model-based diagnostics

NASA Technical Reports Server (NTRS)

Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.

1990-01-01

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

Diagnostic layer integration in FPGA-based pipeline measurement systems for HEP experiments

NASA Astrophysics Data System (ADS)

Pozniak, Krzysztof T.

2007-08-01

Integrated triggering and data acquisition systems for high energy physics experiments may be considered as fast, multichannel, synchronous, distributed, pipeline measurement systems. A considerable extension of functional, technological and monitoring demands, which has recently been imposed on them, forced a common usage of large field-programmable gate array (FPGA), digital signal processing-enhanced matrices and fast optical transmission for their realization. This paper discusses modelling, design, realization and testing of pipeline measurement systems. A distribution of synchronous data stream flows is considered in the network. A general functional structure of a single network node is presented. A suggested, novel block structure of the node model facilitates full implementation in the FPGA chip, circuit standardization and parametrization, as well as integration of functional and diagnostic layers. A general method for pipeline system design was derived. This method is based on a unified model of the synchronous data network node. A few examples of practically realized, FPGA-based, pipeline measurement systems were presented. The described systems were applied in ZEUS and CMS.

Verifying Diagnostic Software

NASA Technical Reports Server (NTRS)

Lindsey, Tony; Pecheur, Charles

2004-01-01

Livingstone PathFinder (LPF) is a simulation-based computer program for verifying autonomous diagnostic software. LPF is designed especially to be applied to NASA s Livingstone computer program, which implements a qualitative-model-based algorithm that diagnoses faults in a complex automated system (e.g., an exploratory robot, spacecraft, or aircraft). LPF forms a software test bed containing a Livingstone diagnosis engine, embedded in a simulated operating environment consisting of a simulator of the system to be diagnosed by Livingstone and a driver program that issues commands and faults according to a nondeterministic scenario provided by the user. LPF runs the test bed through all executions allowed by the scenario, checking for various selectable error conditions after each step. All components of the test bed are instrumented, so that execution can be single-stepped both backward and forward. The architecture of LPF is modular and includes generic interfaces to facilitate substitution of alternative versions of its different parts. Altogether, LPF provides a flexible, extensible framework for simulation-based analysis of diagnostic software; these characteristics also render it amenable to application to diagnostic programs other than Livingstone.

Added value of cost-utility analysis in simple diagnostic studies of accuracy: (18)F-fluoromethylcholine PET/CT in prostate cancer staging.

PubMed

Gerke, Oke; Poulsen, Mads H; Høilund-Carlsen, Poul Flemming

2015-01-01

Diagnostic studies of accuracy targeting sensitivity and specificity are commonly done in a paired design in which all modalities are applied in each patient, whereas cost-effectiveness and cost-utility analyses are usually assessed either directly alongside to or indirectly by means of stochastic modeling based on larger randomized controlled trials (RCTs). However the conduct of RCTs is hampered in an environment such as ours, in which technology is rapidly evolving. As such, there is a relatively limited number of RCTs. Therefore, we investigated as to which extent paired diagnostic studies of accuracy can be also used to shed light on economic implications when considering a new diagnostic test. We propose a simple decision tree model-based cost-utility analysis of a diagnostic test when compared to the current standard procedure and exemplify this approach with published data from lymph node staging of prostate cancer. Average procedure costs were taken from the Danish Diagnosis Related Groups Tariff in 2013 and life expectancy was estimated for an ideal 60 year old patient based on prostate cancer stage and prostatectomy or radiation and chemotherapy. Quality-adjusted life-years (QALYs) were deduced from the literature, and an incremental cost-effectiveness ratio (ICER) was used to compare lymph node dissection with respective histopathological examination (reference standard) and (18)F-fluoromethylcholine positron emission tomography/computed tomography (FCH-PET/CT). Lower bounds of sensitivity and specificity of FCH-PET/CT were established at which the replacement of the reference standard by FCH-PET/CT comes with a trade-off between worse effectiveness and lower costs. Compared to the reference standard in a diagnostic accuracy study, any imperfections in accuracy of a diagnostic test imply that replacing the reference standard generates a loss in effectiveness and utility. We conclude that diagnostic studies of accuracy can be put to a more extensive use, over and above a mere indication of sensitivity and specificity of an imaging test, and that health economic considerations should be undertaken when planning a prospective diagnostic accuracy study. These endeavors will prove especially fruitful when comparing several imaging techniques with one another, or the same imaging technique using different tracers, with an independent reference standard for the evaluation of results.

TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

NASA Technical Reports Server (NTRS)

Manner, David B.

1990-01-01

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system.

Modification of the Integrated Sasang Constitutional Diagnostic Model

PubMed Central

Nam, Jiho

2017-01-01

In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to address quantification problems in the existing Sasang constitutional diagnostic method. However, certain issues have been raised regarding a revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problems of the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absolute shrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly, proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the test set for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables of individual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the male SY type (36.4% → 62.0%) and the female SE type (43.7% → 64.5%), which were areas of limitation of the previous integrated diagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasang constitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine. PMID:29317897

On-line experimental validation of a model-based diagnostic algorithm dedicated to a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Polverino, Pierpaolo; Esposito, Angelo; Pianese, Cesare; Ludwig, Bastian; Iwanschitz, Boris; Mai, Andreas

2016-02-01

In the current energetic scenario, Solid Oxide Fuel Cells (SOFCs) exhibit appealing features which make them suitable for environmental-friendly power production, especially for stationary applications. An example is represented by micro-combined heat and power (μ-CHP) generation units based on SOFC stacks, which are able to produce electric and thermal power with high efficiency and low pollutant and greenhouse gases emissions. However, the main limitations to their diffusion into the mass market consist in high maintenance and production costs and short lifetime. To improve these aspects, the current research activity focuses on the development of robust and generalizable diagnostic techniques, aimed at detecting and isolating faults within the entire system (i.e. SOFC stack and balance of plant). Coupled with appropriate recovery strategies, diagnosis can prevent undesired system shutdowns during faulty conditions, with consequent lifetime increase and maintenance costs reduction. This paper deals with the on-line experimental validation of a model-based diagnostic algorithm applied to a pre-commercial SOFC system. The proposed algorithm exploits a Fault Signature Matrix based on a Fault Tree Analysis and improved through fault simulations. The algorithm is characterized on the considered system and it is validated by means of experimental induction of faulty states in controlled conditions.

Statistical Methods for Assessments in Simulations and Serious Games. Research Report. ETS RR-14-12

ERIC Educational Resources Information Center

Fu, Jianbin; Zapata, Diego; Mavronikolas, Elia

2014-01-01

Simulation or game-based assessments produce outcome data and process data. In this article, some statistical models that can potentially be used to analyze data from simulation or game-based assessments are introduced. Specifically, cognitive diagnostic models that can be used to estimate latent skills from outcome data so as to scale these…

Visualization techniques for tongue analysis in traditional Chinese medicine

NASA Astrophysics Data System (ADS)

Pham, Binh L.; Cai, Yang

2004-05-01

Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C).

Behavioural phenotyping assays for mouse models of autism

PubMed Central

Silverman, Jill L.; Yang, Mu; Lord, Catherine; Crawley, Jacqueline N.

2011-01-01

Autism is a heterogeneous neurodevelopmental disorder of unknown aetiology that affects 1 in 100–150 individuals. Diagnosis is based on three categories of behavioural criteria: abnormal social interactions, communication deficits and repetitive behaviours. Strong evidence for a genetic basis has prompted the development of mouse models with targeted mutations in candidate genes for autism. As the diagnostic criteria for autism are behavioural, phenotyping these mouse models requires behavioural assays with high relevance to each category of the diagnostic symptoms. Behavioural neuroscientists are generating a comprehensive set of assays for social interaction, communication and repetitive behaviours to test hypotheses about the causes of austism. Robust phenotypes in mouse models hold great promise as translational tools for discovering effective treatments for components of autism spectrum disorders. PMID:20559336

Designing a training tool for imaging mental models

NASA Technical Reports Server (NTRS)

Dede, Christopher J.; Jayaram, Geetha

1990-01-01

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network.

Colonoscopy video quality assessment using hidden Markov random fields

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dusty; Spofford, Inbar; Vosburgh, Kirby

2011-03-01

With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.

Urinary Proteomic Biomarkers for Diagnosis and Risk Stratification of Autosomal Dominant Polycystic Kidney Disease: A Multicentric Study

PubMed Central

Kistler, Andreas D.; Serra, Andreas L.; Siwy, Justyna; Poster, Diane; Krauer, Fabienne; Torres, Vicente E.; Mrug, Michal; Grantham, Jared J.; Bae, Kyongtae T.; Bost, James E.; Mullen, William; Wüthrich, Rudolf P.; Mischak, Harald; Chapman, Arlene B.

2013-01-01

Treatment options for autosomal dominant polycystic kidney disease (ADPKD) will likely become available in the near future, hence reliable diagnostic and prognostic biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS), we compared the urinary peptidome of 41 ADPKD patients to 189 healthy controls and identified 657 peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic biomarker model based on the 142 most consistent peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251 ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in ADPKD included, but were not limited to markers previously associated with acute kidney injury (AKI). The diagnostic biomarker model was highly specific for ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV) based on proteomic analysis of 134 ADPKD patients and showed a correlation of r = 0.415 (p<0.0001) with htTKV in an independent validation cohort consisting of 158 ADPKD patients. In conclusion, the performance of peptidomic biomarker scores is superior to any other biochemical markers of ADPKD and the proteomic biomarker patterns are a promising tool for prognostic evaluation of ADPKD. PMID:23326375

Model Performance Evaluation and Scenario Analysis ...

EPA Pesticide Factsheets

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors. The performance measures include error analysis, coefficient of determination, Nash-Sutcliffe efficiency, and a new weighted rank method. These performance metrics only provide useful information about the overall model performance. Note that MPESA is based on the separation of observed and simulated time series into magnitude and sequence components. The separation of time series into magnitude and sequence components and the reconstruction back to time series provides diagnostic insights to modelers. For example, traditional approaches lack the capability to identify if the source of uncertainty in the simulated data is due to the quality of the input data or the way the analyst adjusted the model parameters. This report presents a suite of model diagnostics that identify if mismatches between observed and simulated data result from magnitude or sequence related errors. MPESA offers graphical and statistical options that allow HSPF users to compare observed and simulated time series and identify the parameter values to adjust or the input data to modify. The scenario analysis part of the too

The Diagnostic Efficacy of Cone-beam Computed Tomography in Endodontics: A Systematic Review and Analysis by a Hierarchical Model of Efficacy.

PubMed

Rosen, Eyal; Taschieri, Silvio; Del Fabbro, Massimo; Beitlitum, Ilan; Tsesis, Igor

2015-07-01

The aim of this study was to evaluate the diagnostic efficacy of cone-beam computed tomographic (CBCT) imaging in endodontics based on a systematic search and analysis of the literature using an efficacy model. A systematic search of the literature was performed to identify studies evaluating the use of CBCT imaging in endodontics. The identified studies were subjected to strict inclusion criteria followed by an analysis using a hierarchical model of efficacy (model) designed for appraisal of the literature on the levels of efficacy of a diagnostic imaging modality. Initially, 485 possible relevant articles were identified. After title and abstract screening and a full-text evaluation, 58 articles (12%) that met the inclusion criteria were analyzed and allocated to levels of efficacy. Most eligible articles (n = 52, 90%) evaluated technical characteristics or the accuracy of CBCT imaging, which was defined in this model as low levels of efficacy. Only 6 articles (10%) proclaimed to evaluate the efficacy of CBCT imaging to support the practitioner's decision making; treatment planning; and, ultimately, the treatment outcome, which was defined as higher levels of efficacy. The expected ultimate benefit of CBCT imaging to the endodontic patient as evaluated by its level of diagnostic efficacy is unclear and is mainly limited to its technical and diagnostic accuracy efficacies. Even for these low levels of efficacy, current knowledge is limited. Therefore, a cautious and rational approach is advised when considering CBCT imaging for endodontic purposes. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Diagnosis of Parkinsonian disorders using a channelized Hotelling observer model: Proof of principle

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

Bal, H.; Bal, G.; Acton, P. D.

2007-10-15

Imaging dopamine transporters using PET and SPECT probes is a powerful technique for the early diagnosis of Parkinsonian disorders. In order to perform automated accurate diagnosis of these diseases, a channelized Hotelling observer (CHO) based model was developed and evaluated using the SPECT tracer [Tc-99m]TRODAT-1. Computer simulations were performed using a digitized striatal phantom to characterize early stages of the disease (20 lesion-present cases with varying lesion size and contrast). Projection data, modeling the effects of attenuation and geometric response function, were obtained for each case. Statistical noise levels corresponding to those observed clinically were added to the projection datamore » to obtain 100 noise realizations for each case. All the projection data were reconstructed, and a subset of the transaxial slices containing the striatum was summed and used for further analysis. CHO models, using the Laguerre-Gaussian functions as channels, were designed for two cases: (1) By training the model using individual lesion-present samples and (2) by training the model using pooled lesion-present samples. A decision threshold obtained for each CHO model was used to classify the study population (n=40). It was observed that individual lesion trained CHO models gave high diagnostic accuracy for lesions that were larger than those used to train the model and vice-versa. On the other hand, the pooled CHO model was found to give a high diagnostic accuracy for all the lesion cases (average diagnostic accuracy=0.95{+-}0.07; p<0.0001 Fisher's exact test). Based on our results, we conclude that a CHO model has the potential to provide early and accurate diagnosis of Parkinsonian disorders, thereby improving patient management.« less

A business model for diagnostic startups-a business model for a new generation of diagnostics companies.

PubMed

Kurtzman, Gary

2005-10-01

Venture capital has tended to shy away from diagnostics companies, whose products are not predicated on the blockbuster model of pharmaceuticals. But several new diagnostics companies are developing products that hold immense potential to improve healthcare delivery. Here's why venture investors should take another look at the diagnostics area.

Polar bear encephalitis: establishment of a comprehensive next-generation pathogen analysis pipeline for captive and free-living wildlife.

PubMed

Szentiks, C A; Tsangaras, K; Abendroth, B; Scheuch, M; Stenglein, M D; Wohlsein, P; Heeger, F; Höveler, R; Chen, W; Sun, W; Damiani, A; Nikolin, V; Gruber, A D; Grobbel, M; Kalthoff, D; Höper, D; Czirják, G Á; Derisi, J; Mazzoni, C J; Schüle, A; Aue, A; East, M L; Hofer, H; Beer, M; Osterrieder, N; Greenwood, A D

2014-05-01

This report describes three possibly related incidences of encephalitis, two of them lethal, in captive polar bears (Ursus maritimus). Standard diagnostic methods failed to identify pathogens in any of these cases. A comprehensive, three-stage diagnostic 'pipeline' employing both standard serological methods and new DNA microarray and next generation sequencing-based diagnostics was developed, in part as a consequence of this initial failure. This pipeline approach illustrates the strengths, weaknesses and limitations of these tools in determining pathogen caused deaths in non-model organisms such as wildlife species and why the use of a limited number of diagnostic tools may fail to uncover important wildlife pathogens. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Sanctuary Model of Trauma-Informed Organizational Change

ERIC Educational Resources Information Center

Bloom, Sandra L.; Sreedhar, Sarah Yanosy

2008-01-01

This article features the Sanctuary Model[R], a trauma-informed method for creating or changing an organizational culture. Although the model is based on trauma theory, its tenets have application in working with children and adults across a wide diagnostic spectrum. Originally developed in a short-term, acute inpatient psychiatric setting for…

A Generalized Approach to Defining Item Discrimination for DCMs

ERIC Educational Resources Information Center

Henson, Robert; DiBello, Lou; Stout, Bill

2018-01-01

Diagnostic classification models (DCMs, also known as cognitive diagnosis models) hold the promise of providing detailed classroom information about the skills a student has or has not mastered. Specifically, DCMs are special cases of constrained latent class models where classes are defined based on mastery/nonmastery of a set of attributes (or…

An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Narasimhan, Sriram; Roychoudhury, Indranil; Daigle, Matthew; Pulido, Belarmino

2013-01-01

Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

Diagnostic budgets of analyzed and modelled tropical plumes

NASA Technical Reports Server (NTRS)

Mcguirk, James P.; Vest, Gerry W.

1993-01-01

Blackwell et al. successfully simulated tropical plumes in a global barotropic model valid at 200 mb. The plume evolved in response to strong equatorial convergence which simulated a surge in the Walker Circulation. The defining characteristics of simulated plumes are: a subtropical jet with southerlies emanating from the deep tropics; a tropical/mid-latitude trough to the west; a convergence/divergence dipole straddling the trough; and strong cross contour flow at the tropical base of the jet. Diagnostic budgets of vorticity, divergence, and kinetic energy are calculated to explain the evolution of the modelled plumes. Budgets describe the unforced (basic) state, forced plumes, forced cases with no plumes, and ECMWF analyzed plumes.

The Log-Linear Cognitive Diagnostic Model (LCDM) as a Special Case of The General Diagnostic Model (GDM). Research Report. ETS RR-14-40

ERIC Educational Resources Information Center

von Davier, Matthias

2014-01-01

Diagnostic models combine multiple binary latent variables in an attempt to produce a latent structure that provides more information about test takers' performance than do unidimensional latent variable models. Recent developments in diagnostic modeling emphasize the possibility that multiple skills may interact in a conjunctive way within the…

Expert systems and simulation models; Proceedings of the Seminar, Tucson, AZ, November 18, 19, 1985

NASA Technical Reports Server (NTRS)

1986-01-01

The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2015-01-01

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2016-01-01

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Influence diagnostics in meta-regression model.

PubMed

Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

2017-09-01

This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

Practical Application of Model-based Programming and State-based Architecture to Space Missions

NASA Technical Reports Server (NTRS)

Horvath, Gregory; Ingham, Michel; Chung, Seung; Martin, Oliver; Williams, Brian

2006-01-01

A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps

A diagnostic model for chronic hypersensitivity pneumonitis.

PubMed

Johannson, Kerri A; Elicker, Brett M; Vittinghoff, Eric; Assayag, Deborah; de Boer, Kaïssa; Golden, Jeffrey A; Jones, Kirk D; King, Talmadge E; Koth, Laura L; Lee, Joyce S; Ley, Brett; Wolters, Paul J; Collard, Harold R

2016-10-01

The objective of this study was to develop a diagnostic model that allows for a highly specific diagnosis of chronic hypersensitivity pneumonitis using clinical and radiological variables alone. Chronic hypersensitivity pneumonitis and other interstitial lung disease cases were retrospectively identified from a longitudinal database. High-resolution CT scans were blindly scored for radiographic features (eg, ground-glass opacity, mosaic perfusion) as well as the radiologist's diagnostic impression. Candidate models were developed then evaluated using clinical and radiographic variables and assessed by the cross-validated C-statistic. Forty-four chronic hypersensitivity pneumonitis and eighty other interstitial lung disease cases were identified. Two models were selected based on their statistical performance, clinical applicability and face validity. Key model variables included age, down feather and/or bird exposure, radiographic presence of ground-glass opacity and mosaic perfusion and moderate or high confidence in the radiographic impression of chronic hypersensitivity pneumonitis. Models were internally validated with good performance, and cut-off values were established that resulted in high specificity for a diagnosis of chronic hypersensitivity pneumonitis. 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/

A Business Model for Diagnostic Startups-A Business Model for a New Generation Of Diagnostics Companies

PubMed Central

Kurtzman, Gary

2005-01-01

Venture capital has tended to shy away from diagnostics companies, whose products are not predicated on the blockbuster model of pharmaceuticals. But several new diagnostics companies are developing products that hold immense potential to improve healthcare delivery. Here’s why venture investors should take another look at the diagnostics area. PMID:23424311

Study of a high power hydrogen beam diagnostic based on secondary electron emission

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

Sartori, E., E-mail: emanuele.sartori@igi.cnr.it; Department of Management and Engineering, University di Padova strad. S. Nicola 3, 36100 Vicenza; Panasenkov, A.

2016-11-15

In high power neutral beams for fusion, beam uniformity is an important figure of merit. Knowing the transverse power profile is essential during the initial phases of beam source operation, such as those expected for the ITER heating neutral beam (HNB) test facility. To measure it a diagnostic technique is proposed, based on the collection of secondary electrons generated by beam-surface and beam-gas interactions, by an array of positively biased collectors placed behind the calorimeter tubes. This measurement showed in the IREK test stand good proportionality to the primary beam current. To investigate the diagnostic performances in different conditions, wemore » developed a numerical model of secondary electron emission, induced by beam particle impact on the copper tubes, and reproducing the cascade of secondary emission caused by successive electron impacts. The model is first validated against IREK measurements. It is then applied to the HNB case, to assess the locality of the measurement, the proportionality to the beam current density, and the influence of beam plasma.« less

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient?

PubMed

Lamers, L M

Adequate risk adjustment is critical to the success of market-oriented health care reforms in many countries. Currently used risk adjusters based on demographic and diagnostic cost groups (DCGs) do not reflect expected costs accurately. This study examines the simultaneous predictive accuracy of inpatient and outpatient morbidity measures and prior costs. DCGs, pharmacy cost groups (PCGs), and prior year's costs improve the predictive accuracy of the demographic model substantially. DCGs and PCGs seem complementary in their ability to predict future costs. However, this study shows that the combination of DCGs and PCGs still leaves room for cream skimming.

Model-Based Reasoning in the Detection of Satellite Anomalies

DTIC Science & Technology

1990-12-01

Conference on Artificial Intellegence . 1363-1368. Detroit, Michigan, August 89. Chu, Wei-Hai. "Generic Expert System Shell for Diagnostic Reasoning... Intellegence . 1324-1330. Detroit, Michigan, August 89. de Kleer, Johan and Brian C. Williams. "Diagnosing Multiple Faults," Artificial Intellegence , 32(1): 97...Benjamin Kuipers. "Model-Based Monitoring of Dynamic Systems," Proceedings of the Eleventh Intematianal Joint Conference on Artificial Intellegence . 1238

Dutch Research on Knowledge-Based Instructional Systems: Introduction to the Special Issue.

ERIC Educational Resources Information Center

van Merrienboer, Jeroen J. G.

1994-01-01

Provides an overview of this issue that reviews Dutch research concerning knowledge-based instructional systems. Topics discussed include experimental research, conceptual models, design considerations, and guidelines; the design of student diagnostic modules, instructional modules, and interface modules; second-language teaching; intelligent…

Applications of digital processing for noise removal from plasma diagnostics

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

Kane, R.J.; Candy, J.V.; Casper, T.A.

1985-11-11

The use of digital signal techniques for removal of noise components present in plasma diagnostic signals is discussed, particularly with reference to diamagnetic loop signals. These signals contain noise due to power supply ripple in addition to plasma characteristics. The application of noise canceling techniques, such as adaptive noise canceling and model-based estimation, will be discussed. The use of computer codes such as SIG is described. 19 refs., 5 figs.

Development and validation of a highly sensitive urine-based test to identify patients with colonic adenomatous polyps.

PubMed

Wang, Haili; Tso, Victor; Wong, Clarence; Sadowski, Dan; Fedorak, Richard N

2014-03-20

Adenomatous polyps are precursors of colorectal cancer; their detection and removal is the goal of colon cancer screening programs. However, fecal-based methods identify patients with adenomatous polyps with low levels of sensitivity. The aim or this study was to develop a highly accurate, prototypic, proof-of-concept, spot urine-based diagnostic test using metabolomic technology to distinguish persons with adenomatous polyps from those without polyps. Prospective urine and stool samples were collected from 876 participants undergoing colonoscopy examination in a colon cancer screening program, from April 2008 to October 2009 at the University of Alberta. Colonoscopy reference standard identified 633 participants with no colonic polyps and 243 with colonic adenomatous polyps. One-dimensional nuclear magnetic resonance spectra of urine metabolites were analyzed to define a diagnostic metabolomic profile for colonic adenomas. A urine metabolomic diagnostic test for colonic adenomatous polyps was established using 67% of the samples (un-blinded training set) and validated using the other 33% of the samples (blinded testing set). The urine metabolomic diagnostic test's specificity and sensitivity were compared with those of fecal-based tests. Using a two-component, orthogonal, partial least-squares model of the metabolomic profile, the un-blinded training set identified patients with colonic adenomatous polyps with 88.9% sensitivity and 50.2% specificity. Validation using the blinded testing set confirmed sensitivity and specificity values of 82.7% and 51.2%, respectively. Sensitivities of fecal-based tests to identify colonic adenomas ranged from 2.5 to 11.9%. We describe a proof-of-concept spot urine-based metabolomic diagnostic test that identifies patients with colonic adenomatous polyps with a greater level of sensitivity (83%) than fecal-based tests.

Postscript: Making Important Distinctions--Diagnostic Models, Theoretical Models, and the Mnemonic Model of PTSD

ERIC Educational Resources Information Center

Monroe, Scott M.; Mineka, Susan

2008-01-01

Our commentary was intended to stimulate discussion about what we perceive to be shortcomings of the mnemonic model and its research base, in the hope of shedding some light on key questions for understanding posttraumatic stress disorder (PTSD). In our view, Berntsen, Rubin, and Bohni have responded only to what they perceive to be shortcomings…

Knowledge-based diagnosis for aerospace systems

NASA Technical Reports Server (NTRS)

Atkinson, David J.

1988-01-01

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

ADIPOSITY-BASED CHRONIC DISEASE AS A NEW DIAGNOSTIC TERM: THE AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY POSITION STATEMENT.

PubMed

Mechanick, Jeffrey I; Hurley, Daniel L; Garvey, W Timothy

2017-03-01

The American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) have created a chronic care model, advanced diagnostic framework, clinical practice guidelines, and clinical practice algorithm for the comprehensive management of obesity. This coordinated effort is not solely based on body mass index as in previous models, but emphasizes a complications-centric approach that primarily determines therapeutic decisions and desired outcomes. Adiposity-Based Chronic Disease (ABCD) is a new diagnostic term for obesity that explicitly identifies a chronic disease, alludes to a precise pathophysiologic basis, and avoids the stigmata and confusion related to the differential use and multiple meanings of the term "obesity." Key elements to further the care of patients using this new ABCD term are: (1) positioning lifestyle medicine in the promotion of overall health, not only as the first algorithmic step, but as the central, pervasive action; (2) standardizing protocols that comprehensively and durably address weight loss and management of adiposity-based complications; (3) approaching patient care through contextualization (e.g., primordial prevention to decrease obesogenic environmental risk factors and transculturalization to adapt evidence-based recommendations for different ethnicities, cultures, and socio-economics); and lastly, (4) developing evidence-based strategies for successful implementation, monitoring, and optimization of patient care over time. This AACE/ACE blueprint extends current work and aspires to meaningfully improve both individual and population health by presenting a new ABCD term for medical diagnostic purposes, use in a complications-centric management and staging strategy, and precise reference to the obesity chronic disease state, divested from counterproductive stigmata and ambiguities found in the general public sphere. AACE = American Association of Clinical Endocrinologists ABCD = Adiposity-Based Chronic Disease ACE = American College of Endocrinology BMI = body mass index CPG = clinical practice guidelines HCP = health care professionals.

Semi-Cooperative Learning in Smart Grid Agents

DTIC Science & Technology

2013-12-01

2009 HOEP and its ACF/PACF diagnostic functions. . . 37 2.21 Residuals from multiplicative seasonal ARIMA model fit on 2009 HOEP. . . . . . . . . . 38...2.22 Typical forecast for the next 72 hours based on the ARIMA model of Eq. 2.14. . . . . . . 40 3.1 Consumption capacity of two small villages over...1 (the training series). . 49 3.4 ARIMA model (Eq. 3.1) forecasts based on the full village 1 time series (top subfigure) and the first 24 hours of

Deletion Diagnostics for the Generalised Linear Mixed Model with independent random effects

PubMed Central

Ganguli, B.; Roy, S. Sen; Naskar, M.; Malloy, E. J.; Eisen, E. A.

2015-01-01

The Generalised Linear Mixed Model (GLMM) is widely used for modelling environmental data. However, such data are prone to influential observations which can distort the estimated exposure-response curve particularly in regions of high exposure. Deletion diagnostics for iterative estimation schemes commonly derive the deleted estimates based on a single iteration of the full system holding certain pivotal quantities such as the information matrix to be constant. In this paper, we present an approximate formula for the deleted estimates and Cook’s distance for the GLMM which does not assume that the estimates of variance parameters are unaffected by deletion. The procedure allows the user to calculate standardised DFBETAs for mean as well as variance parameters. In certain cases, such as when using the GLMM as a device for smoothing, such residuals for the variance parameters are interesting in their own right. In general, the procedure leads to deleted estimates of mean parameters which are corrected for the effect of deletion on variance components as estimation of the two sets of parameters is interdependent. The probabilistic behaviour of these residuals is investigated and a simulation based procedure suggested for their standardisation. The method is used to identify influential individuals in an occupational cohort exposed to silica. The results show that failure to conduct post model fitting diagnostics for variance components can lead to erroneous conclusions about the fitted curve and unstable confidence intervals. PMID:26626135

Absolute brightness modeling for improved measurement of electron temperature from soft x-rays on MST

NASA Astrophysics Data System (ADS)

Reusch, L. M.; Franz, P.; Goetz, J. A.; den Hartog, D. J.; Nornberg, M. D.; van Meter, P.

2017-10-01

The two-color soft x-ray tomography (SXT) diagnostic on MST is now capable of Te measurement down to 500 eV. The previous lower limit was 1 keV, due to the presence of SXR emission lines from Al sputtered from the MST wall. The two-color technique uses two filters of different thickness to form a coarse spectrometer to estimate the slope of the continuum x-ray spectrum, which depends on Te. The 1.6 - 2.0 keV Al emission lines were previously filtered out by using thick Be filters (400 µm and 800 µm), thus restricting the range of the SXT diagnostic to Te >= 1 keV. Absolute brightness modeling explicitly includes several sources of radiation in the analysis model, enabling the use of thinner filters and measurement of much lower Te. Models based on the atomic database and analysis structure (ADAS) agree very well with our experimental SXR measurements. We used ADAS to assess the effect of bremsstrahlung, recombination, dielectronic recombination, and line emission on the inferred Te. This assessment informed the choice of the optimum filter pair to extend the Te range of the SXT diagnostic. This material is based upon work supported by the U.S. Department of Energy Office of Science, Office of Fusion Energy Sciences program under Award Numbers DE-FC02-05ER54814 and DE-SC0015474.

Image analysis and modeling in medical image computing. Recent developments and advances.

PubMed

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

Development of diagnostic test instruments to reveal level student conception in kinematic and dynamics

NASA Astrophysics Data System (ADS)

Handhika, J.; Cari, C.; Suparmi, A.; Sunarno, W.; Purwandari, P.

2018-03-01

The purpose of this research was to develop a diagnostic test instrument to reveal students' conceptions in kinematics and dynamics. The diagnostic test was developed based on the content indicator the concept of (1) displacement and distance, (2) instantaneous and average velocity, (3) zero and constant acceleration, (4) gravitational acceleration (5) Newton's first Law, (6) and Newton's third Law. The diagnostic test development model includes: Diagnostic test requirement analysis, formulating test-making objectives, developing tests, checking the validity of the content and the performance of reliability, and application of tests. The Content Validation Index (CVI) results in the category are highly relevant, with a value of 0.85. Three questions get negative Content Validation Ratio CVR) (-0.6), after revised distractors and clarify visual presentation; the CVR become 1 (highly relevant). This test was applied, obtained 16 valid test items, with Cronbach Alpha value of 0.80. It can conclude that diagnostic test can be used to reveal the level of students conception in kinematics and dynamics.

NASA IVHM Technology Experiment for X-vehicles (NITEX)

NASA Technical Reports Server (NTRS)

Sandra, Hayden; Bajwa, Anupa

2001-01-01

The purpose of the NASA IVHM Technology Experiment for X-vehicles (NITEX) is to advance the development of selected IVHM technologies in a flight environment and to demonstrate the potential for reusable launch vehicle ground processing savings. The technologies to be developed and demonstrated include system-level and detailed diagnostics for real-time fault detection and isolation, prognostics for fault prediction, automated maintenance planning based on diagnostic and prognostic results, and a microelectronics hardware platform. Complete flight The Evolution of Flexible Insulation as IVHM consists of advanced sensors, distributed data acquisition, data processing that includes model-based diagnostics, prognostics and vehicle autonomy for control or suggested action, and advanced data storage. Complete ground IVHM consists of evolved control room architectures, advanced applications including automated maintenance planning and automated ground support equipment. This experiment will advance the development of a subset of complete IVHM.

Scheme for the selection of measurement uncertainty models in blood establishments' screening immunoassays.

PubMed

Pereira, Paulo; Westgard, James O; Encarnação, Pedro; Seghatchian, Jerard; de Sousa, Gracinda

2015-02-01

Blood establishments routinely perform screening immunoassays to assess safety of the blood components. As with any other screening test, results have an inherent uncertainty. In blood establishments the major concern is the chance of false negatives, due to its possible impact on patients' health. This article briefly reviews GUM and diagnostic accuracy models for screening immunoassays, recommending a scheme to support the screening laboratories' staffs on the selection of a model considering the intended use of the screening results (i.e., post-transfusion safety). The discussion is grounded on a "risk-based thinking", risk being considered from the blood donor selection to the screening immunoassays. A combination of GUM and diagnostic accuracy models to evaluate measurement uncertainty in blood establishments is recommended. Copyright © 2014 Elsevier Ltd. All rights reserved.

Developing a CD-CBM Anticipatory Approach for Cavitation - Defining a Model-Based Descriptor Consistent Across Processes, Phase 1 Final Report Context-Dependent Prognostics and Health Assessment: A New Paradigm for Condition-based Maintenance SBIR Topic No. N98-114

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

Allgood, G.O.; Dress, W.B.; Kercel, S.W.

1999-06-01

The objective of this research, and subsequent testing, was to identify specific features of cavitation that could be used as a model-based descriptor in a context-dependent condition-based maintenance (CD-CBM) anticipatory prognostic and health assessment model. This descriptor is based on the physics of the phenomena, capturing the salient features of the process dynamics. The test methodology and approach were developed to make the cavitation features the dominant effect in the process and collected signatures. This would allow the accurate characterization of the salient cavitation features at different operational states. By developing such an abstraction, these attributes can be used asmore » a general diagnostic for a system or any of its components. In this study, the particular focus will be pumps. As many as 90% of pump failures are catastrophic. They seem to be operating normally and fail abruptly without warning. This is true whether the failure is sudden hardware damage requiring repair, such as a gasket failure, or a transition into an undesired operating mode, such as cavitation. This means that conventional diagnostic methods fail to predict 90% of incipient failures and that in addressing this problem, model-based methods can add value where it is actually needed.« less

Predicting remaining life by fusing the physics of failure modeling with diagnostics

NASA Astrophysics Data System (ADS)

Kacprzynski, G. J.; Sarlashkar, A.; Roemer, M. J.; Hess, A.; Hardman, B.

2004-03-01

Technology that enables failure prediction of critical machine components (prognostics) has the potential to significantly reduce maintenance costs and increase availability and safety. This article summarizes a research effort funded through the U.S. Defense Advanced Research Projects Agency and Naval Air System Command aimed at enhancing prognostic accuracy through more advanced physics-of-failure modeling and intelligent utilization of relevant diagnostic information. H-60 helicopter gear is used as a case study to introduce both stochastic sub-zone crack initiation and three-dimensional fracture mechanics lifing models along with adaptive model updating techniques for tuning key failure mode variables at a local material/damage site based on fused vibration features. The overall prognostic scheme is aimed at minimizing inherent modeling and operational uncertainties via sensed system measurements that evolve as damage progresses.

A Model for Evidence Accumulation in the Lexical Decision Task

ERIC Educational Resources Information Center

Wagenmakers, Eric-Jan; Steyvers, Mark; Raaijmakers, Jeroen G. W.; Shiffrin, Richard M.; van Rijn, Hedderik; Zeelenberg, Rene

2004-01-01

We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes' rule) decision process that simultaneously considers the diagnosticity of the evidence for the 'WORD' response and the 'NONWORD' response. The model calculates the odds ratio that the presented…

A pilot study using laser-based technique for non-invasive diagnostics of hypertensive conditions in mice

NASA Astrophysics Data System (ADS)

Litvinova, Karina S.; Ahmad, Shakil; Wang, Keqing; Rafailov, Ilya E.; Sokolovski, Sergei G.; Zhang, Lin; Rafailov, Edik U.; Ahmed, Asif

2016-02-01

Endothelial dysfunction is directly linked to preeclampsia, a maternal hypertensive condition that is life threating for both the mother and the baby. Epidemiological studies show that women with a history of pre-eclampsia have an elevated risk for cardiovascular disease. Here we report a new non-invasive diagnostic test for preeclampsia in mice that allows us to non-invasively assess the condition of the animals during the experiment and treatment in established models of preeclampsia. A laser-based multifunctional diagnostics system (LAKK-M) was chosen to carry out non-invasive analysis of multiple parameters. The device was used to simultaneously record the microcirculatory blood flow and oxygen saturation, as well as fluorescence levels of endogenous fluorophores. Preliminary experiments were conducted on adenoviral (Ad-)- mediated overexpression of sFlt-1 (Ad-sFlt-1) to mimic preeclampsialike symptoms in mice. The recorded data displayed the ability of the LAKK-M diagnostics device to detect significant differences in perfusion measurements between the control and Ad-sFlt-1 treatment. Preliminary results provide a potential avenue to employ these diagnostics technology to monitor and aid in maintaining control of live animal conditions throughout the experiment and treatment.

Computer-assisted education and interdisciplinary breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Whatmough, Pamela; Gale, Alastair G.; Wilson, A. R. M.

1996-04-01

The diagnosis of breast disease for screening or symptomatic women is largely arrived at by a multi-disciplinary team. We report work on the development and assessment of an inter- disciplinary computer based learning system to support the diagnosis of this disease. The diagnostic process is first modelled from different viewpoints and then appropriate knowledge structures pertinent to the domains of radiologist, pathologist and surgeon are depicted. Initially the underlying inter-relationships of the mammographic diagnostic approach were detailed which is largely considered here. Ultimately a system is envisaged which will link these specialties and act as a diagnostic aid as well as a multi-media educational system.

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers

PubMed Central

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-01-01

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios. PMID:28398255

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers.

PubMed

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-04-11

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios.

Development of a Competency-Based Vocational Education IEP Model. Final Report.

ERIC Educational Resources Information Center

Bloomsburg Univ., PA.

A project completed and field tested the concept of vocational instructors and special educators jointly planning curriculum objectives for the handicapped/special needs learner. The diagnostic-prescriptive individualized education (IEP) model developed at Bloomsburg University (Pennsylvania) or IEP Planner was used. Eight occupational areas were…

Spatially targeted social interventions to improve BMP adoption in Maryland watersheds

USDA-ARS?s Scientific Manuscript database

The results of surveys of stakeholders knowledge and attitudes related to water resources, pollution and Best Management Practices (BMPs) are analyzed and used to develop a model of BMP adoption likelihood based on socio-economic factors. The model is integrated into a Diagnostic Decision Support Sy...

Ares I-X Ground Diagnostic Prototype

NASA Technical Reports Server (NTRS)

Schwabacher, Mark; Martin, Rodney; Waterman, Robert; Oostdyk, Rebecca; Ossenfort, John; Matthews, Bryan

2010-01-01

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. The full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes.

Software Tools to Support the Assessment of System Health

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.

2013-01-01

This presentation provides an overview of three software tools that were developed by the NASA Glenn Research Center to support the assessment of system health: the Propulsion Diagnostic Method Evaluation Strategy (ProDIMES), the Systematic Sensor Selection Strategy (S4), and the Extended Testability Analysis (ETA) tool. Originally developed to support specific NASA projects in aeronautics and space, these software tools are currently available to U.S. citizens through the NASA Glenn Software Catalog. The ProDiMES software tool was developed to support a uniform comparison of propulsion gas path diagnostic methods. Methods published in the open literature are typically applied to dissimilar platforms with different levels of complexity. They often address different diagnostic problems and use inconsistent metrics for evaluating performance. As a result, it is difficult to perform a one ]to ]one comparison of the various diagnostic methods. ProDIMES solves this problem by serving as a theme problem to aid in propulsion gas path diagnostic technology development and evaluation. The overall goal is to provide a tool that will serve as an industry standard, and will truly facilitate the development and evaluation of significant Engine Health Management (EHM) capabilities. ProDiMES has been developed under a collaborative project of The Technical Cooperation Program (TTCP) based on feedback provided by individuals within the aircraft engine health management community. The S4 software tool provides a framework that supports the optimal selection of sensors for health management assessments. S4 is structured to accommodate user ]defined applications, diagnostic systems, search techniques, and system requirements/constraints. One or more sensor suites that maximize this performance while meeting other user ]defined system requirements that are presumed to exist. S4 provides a systematic approach for evaluating combinations of sensors to determine the set or sets of sensors that optimally meet the performance goals and the constraints. It identifies optimal sensor suite solutions by utilizing a merit (i.e., cost) function with one of several available optimization approaches. As part of its analysis, S4 can expose fault conditions that are difficult to diagnose due to an incomplete diagnostic philosophy and/or a lack of sensors. S4 was originally developed and applied to liquid rocket engines. It was subsequently used to study the optimized selection of sensors for a simulation ]based aircraft engine diagnostic system. The ETA Tool is a software ]based analysis tool that augments the testability analysis and reporting capabilities of a commercial ]off ]the ]shelf (COTS) package. An initial diagnostic assessment is performed by the COTS software using a user ]developed, qualitative, directed ]graph model of the system being analyzed. The ETA Tool accesses system design information captured within the model and the associated testability analysis output to create a series of six reports for various system engineering needs. These reports are highlighted in the presentation. The ETA Tool was developed by NASA to support the verification of fault management requirements early in the Launch Vehicle process. Due to their early development during the design process, the TEAMS ]based diagnostic model and the ETA Tool were able to positively influence the system design by highlighting gaps in failure detection, fault isolation, and failure recovery.

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Circulating microRNA-based screening tool for breast cancer

PubMed Central

Boukerroucha, Meriem; Fasquelle, Corinne; Thiry, Jérôme; Bovy, Nicolas; Struman, Ingrid; Geurts, Pierre; Collignon, Joëlle; Schroeder, Hélène; Kridelka, Frédéric; Lifrange, Eric; Jossa, Véronique

2016-01-01

Circulating microRNAs (miRNAs) are increasingly recognized as powerful biomarkers in several pathologies, including breast cancer. Here, their plasmatic levels were measured to be used as an alternative screening procedure to mammography for breast cancer diagnosis. A plasma miRNA profile was determined by RT-qPCR in a cohort of 378 women. A diagnostic model was designed based on the expression of 8 miRNAs measured first in a profiling cohort composed of 41 primary breast cancers and 45 controls, and further validated in diverse cohorts composed of 108 primary breast cancers, 88 controls, 35 breast cancers in remission, 31 metastatic breast cancers and 30 gynecologic tumors. A receiver operating characteristic curve derived from the 8-miRNA random forest based diagnostic tool exhibited an area under the curve of 0.81. The accuracy of the diagnostic tool remained unchanged considering age and tumor stage. The miRNA signature correctly identified patients with metastatic breast cancer. The use of the classification model on cohorts of patients with breast cancers in remission and with gynecologic cancers yielded prediction distributions similar to that of the control group. Using a multivariate supervised learning method and a set of 8 circulating miRNAs, we designed an accurate, minimally invasive screening tool for breast cancer. PMID:26734993

Improving the Performance of the Structure-Based Connectionist Network for Diagnosis of Helicopter Gearboxes

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.

1996-01-01

A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

A simplified diagnostic model of orographic rainfall for enhancing satellite-based rainfall estimates in data-poor regions

USGS Publications Warehouse

Funk, Christopher C.; Michaelsen, Joel C.

2004-01-01

An extension of Sinclair's diagnostic model of orographic precipitation (“VDEL”) is developed for use in data-poor regions to enhance rainfall estimates. This extension (VDELB) combines a 2D linearized internal gravity wave calculation with the dot product of the terrain gradient and surface wind to approximate terrain-induced vertical velocity profiles. Slope, wind speed, and stability determine the velocity profile, with either sinusoidal or vertically decaying (evanescent) solutions possible. These velocity profiles replace the parameterized functions in the original VDEL, creating VDELB, a diagnostic accounting for buoyancy effects. A further extension (VDELB*) uses an on/off constraint derived from reanalysis precipitation fields. A validation study over 365 days in the Pacific Northwest suggests that VDELB* can best capture seasonal and geographic variations. A new statistical data-fusion technique is presented and is used to combine VDELB*, reanalysis, and satellite rainfall estimates in southern Africa. The technique, matched filter regression (MFR), sets the variance of the predictors equal to their squared correlation with observed gauge data and predicts rainfall based on the first principal component of the combined data. In the test presented here, mean absolute errors from the MFR technique were 35% lower than the satellite estimates alone. VDELB assumes a linear solution to the wave equations and a Boussinesq atmosphere, and it may give unrealistic responses under extreme conditions. Nonetheless, the results presented here suggest that diagnostic models, driven by reanalysis data, can be used to improve satellite rainfall estimates in data-sparse regions.

Representation of ocean-atmosphere processes associated with extended monsoon episodes over South Asia in CFSv2

NASA Astrophysics Data System (ADS)

Mohan, T. S.; Annamalai, H.; Marx, Larry; Huang, Bohua; Kinter, James

2018-02-01

In the present study, we analyze 30-years output from free run solutions of CFSv2 coupled model to assess the model’s representation of extended (>7 days) active and break monsoon episodes over south Asia. Process based diagnostics is applied to the individual and composite events to identify precursor signals in both ocean and atmospheric variables. Our examination suggests that CFSv2, like most coupled models, depict systematic biases in variables important for ocean-atmosphere interactions. Nevertheless, model solutions capture many aspects of monsoon extended break and active episodes realistically, encouraging us to apply process-based diagnostics. Diagnostics reveal that sea surface temperature (SST) variations over the northern Bay of Bengal where the climatological mixed-layer is thin, lead the in-situ precipitation anomalies by about 8 (10) days during extended active (break) episodes, and the precipitation anomalies over central India by 10-14 days. Mixed-layer heat budget analysis indicates for a close correspondence between SST tendency and net surface heat flux (Q_net). MSE budgets indicate that horizontal moisture advection to be a coherent precursor signal ( 10 days) during both extended break (dry advection) and active (moist advection) events. The lead timings in these precursor signals in CFSv2 solutions will be of potential use to monitor and predict extended monsoon episodes. Diagnostics, however, also indicate that for about 1/3 of the identified extended break and active episodes, inconsistencies in budget terms suggest precursor signals could lead to false alarms. Apart from false alarms, compared to observations, CFSv2 systematically simulates a greater number of extended monsoon active episodes.

Fuel Spray Diagnostics

NASA Technical Reports Server (NTRS)

Humenik, F. M.; Bosque, M. A.

1983-01-01

Fundamental experimental data base for turbulent flow mixing models is provided and better prediction of the more complex turbulent chemical reacting flows. Analytical application to combustor design is provided and a better fundamental understanding of the combustion process.

Visible Human Project

MedlinePlus

... used for teaching, modeling radiation absorption and therapy, equipment design, surgical simulation, and simulation of diagnostic procedures, ….” ... Project ® " by Michael J. Ackerman, Ph.D. Projects Based on the Visible Human Data Set Applications for ...

A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques

NASA Astrophysics Data System (ADS)

Schmidt, S.; Heyns, P. S.; de Villiers, J. P.

2018-02-01

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology.

ALK evaluation in the world of multiplex testing: Network Genomic Medicine (NGM): the Cologne model for implementing personalised oncology.

PubMed

Heydt, C; Kostenko, A; Merkelbach-Bruse, S; Wolf, J; Büttner, R

2016-09-01

Comprehensive molecular genotyping of lung cancers has become a key requirement for guiding therapeutic decisions. As a paradigm model of implementing next-generation comprehensive diagnostics, Network Genomic Medicine (NGM) has established central diagnostic and clinical trial platforms for centralised testing and decentralised personalised treatment in clinical practice. Here, we describe the structures of the NGM network and give a summary of technologies to identify patients with anaplastic lymphoma kinase (ALK) fusion-positive lung adenocarcinomas. As unifying test platforms will become increasingly important for delivering reliable, quick and affordable tests, the NGM diagnostic platform is currently implementing a comprehensive hybrid capture-based parallel sequencing pan-cancer assay. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

PubMed Central

Masood, Ammara; Al-Jumaily, Adel Ali

2013-01-01

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. PMID:24575126

Mechanisms for Induction of Pulmonary Capillary Hemorrhage by Diagnostic Ultrasound: Review and Consideration of Acoustical Radiation Surface Pressure

PubMed Central

Miller, Douglas L.

2016-01-01

Diagnostic ultrasound can induce pulmonary capillary hemorrhage (PCH) in rats and other mammals. This phenomenon represents the only clearly demonstrated biological effect of (non-contrast enhanced) diagnostic ultrasound and thus presents a uniquely important safety issue. However, the physical mechanism responsible for PCH remains uncertain more than 25 y after its discovery. Experimental research has indicated that neither heating nor acoustic cavitation, the predominant mechanisms for bioeffects of ultrasound, is responsible for PCH. Furthermore, proposed theoretical mechanisms based on gas body activation, on alveolar resonance and on impulsive generation of liquid droplets all appear unlikely to be responsible for PCH, owing to unrealistic model assumptions. Here, a simple model based on the acoustic radiation surface pressure (ARSP) at a tissue-air interface is hypothesized as the mechanism for PCH. The ARSP model seems to explain some features of PCH, including the approximate frequency independence of PCH thresholds, and the dependence of thresholds on biological factors. However, ARSP evaluated for experimental threshold conditions appear to be too weak to fully account for stress failure of pulmonary capillaries, gauging by known stresses for injurious physiological conditions. Furthermore, consideration of bulk properties of lung tissue suggests substantial transmission of ultrasound through the pleura, with reduced ARSP and potential involvement of additional mechanisms within the pulmonary interior. Although these recent findings advance our knowledge, only a full understanding of PCH mechanisms will allow development of science-based safety assurance for pulmonary ultrasound. PMID:27649878

Tri-city study of Ecstasy use problems: a latent class analysis.

PubMed

Scheier, Lawrence M; Ben Abdallah, Arbi; Inciardi, James A; Copeland, Jan; Cottler, Linda B

2008-12-01

This study used latent class analysis to examine distinctive subtypes of Ecstasy users based on 24 abuse and dependence symptoms underlying standard DSM-IV criteria. Data came from a three site, population-based, epidemiological study to examine diagnostic nosology for Ecstasy use. Subject inclusion criteria included lifetime Ecstasy use exceeding five times and once in the past year, with participants ranging in age between 16 and 47 years of age from St. Louis, Miami, U.S. and Sydney, Australia. A satisfactory model typified four latent classes representing clearly differentiated diagnostic clusters including: (1) a group of sub-threshold users endorsing few abuse and dependence symptoms (negatives), (2) a group of 'diagnostic orphans' who had characteristic features of dependence for a select group of symptoms (mild dependent), (3) a 'transitional group' mimicking the orphans with regard to their profile of dependence also but reporting some abuse symptoms (moderate dependent), and (4) a 'severe dependent' group with a distinct profile of abuse and dependence symptoms. A multinomial logistic regression model indicated that certain latent classes showed unique associations with external non-diagnostic markers. Controlling for demographic characteristics and lifetime quantity of Ecstasy pill use, criminal behavior and motivational cues for Ecstasy use were the most efficient predictors of cluster membership. This study reinforces the heuristic utility of DSM-IV criteria applied to Ecstasy but with a different collage of symptoms that produced four distinct classes of Ecstasy users.

Evaluating the accessibility and utility of HIV-related point-of-care diagnostics for maternal health in rural South Africa: a study protocol

PubMed Central

Mashamba-Thompson, T P; Drain, P K; Sartorius, B

2016-01-01

Introduction Poor healthcare access is a major barrier to receiving antenatal care and a cause of high maternal mortality in South Africa (SA). ‘Point-of-care’ (POC) diagnostics is a powerful emerging healthcare approach to improve healthcare access. This study focuses on evaluating the accessibility and utility of POC diagnostics for maternal health in rural SA primary healthcare (PHC) clinics in order to generate a model framework of implementation of POC diagnostics in rural South African clinics. Method and analyses We will use several research methods, including a systematic review, quasi-experiments, survey, key informant interviews and audits. We will conduct a systematic review and experimental study to determine the impact of POC diagnostics on maternal health. We will perform a cross-sectional case study of 100 randomly selected rural primary healthcare clinics in KwaZulu-Natal to measure the context and patterns of POC diagnostics access and usage by maternal health providers and patients. We will conduct interviews with relevant key stakeholders to determine the reasons for POC deficiencies regarding accessibility and utility of HIV-related POC diagnostics for maternal health. We will also conduct a vertical audit to investigate all the quality aspects of POC diagnostic services including diagnostic accuracy in a select number of clinics. On the basis of information gathered, we will propose a model framework for improved implementation of POC diagnostics in rural South African public healthcare clinics. Statistical (Stata-13) and thematic (NVIVO) data analysis will be used in this study. Ethics and dissemination The study protocol was approved by the Ethics Committee of the University of KwaZulu-Natal (BE 484/14) and the KwaZulu-Natal Department of Health based on the Helsinki Declaration (HRKM 40/15). Findings of this study will be disseminated electronically and in print. They will be presented to conferences related to HIV/AIDS, diagnostics, maternal health and strengthening of health systems. PMID:27354074

Admixture analysis of the diagnostic subtypes of social anxiety disorder: implications for the DSM-V.

PubMed

Aderka, Idan M; Nickerson, Angela; Hofmann, Stefan G

2012-06-01

Much controversy exists regarding diagnostic subtypes of social anxiety disorder (SAD). The present study used admixture analysis to examine whether individuals with generalized and nongeneralized SAD belong to the same or different populations of origin. This can inform diagnostic subtyping of SAD in the forthcoming DSM-V. Treatment-seeking individuals with generalized SAD (n = 154) and nongeneralized SAD (n = 48) completed a battery of questionnaires. Based on participants' responses to the Liebowitz Social Anxiety Scale (LSAS), we estimated log likelihood and chi-square goodness-of-fit for models with 1, 2, 3, or 4 populations of origin, and compared models using forward stepwise estimation and maximum likelihood ratio tests. Admixture analyses suggested that the two diagnostic subtypes of SAD belong to the same underlying population of origin. In addition, observable differences in depression, general anxiety, and comorbidity were no longer significant when controlling for social anxiety severity. Our sample was recruited in the U.S. and was a treatment-seeking sample. Future studies should examine whether our results generalize to different cultures, and community samples. Support for qualitative differences between SAD subtypes was not found. Rather, our findings support the notion that the diagnostic subtypes of SAD differ quantitatively, and that SAD exists on a continuum of severity. This finding informs diagnostic subtyping of SAD in the forthcoming DSM-V. Copyright © 2011 Elsevier Ltd. All rights reserved.

In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data

ERIC Educational Resources Information Center

Yi, Yeon-Sook

2017-01-01

This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model…

The diagnostic plot analysis of artesian aquifers with case studies in Table Mountain Group of South Africa

NASA Astrophysics Data System (ADS)

Sun, Xiaobin; Xu, Yongxin; Lin, Lixiang

2015-05-01

Parameter estimates of artesian aquifers where piezometric head is above ground level are largely made through free-flowing and recovery tests. The straight-line method proposed by Jacob-Lohman is often used for interpretation of flow rate measured at flowing artesian boreholes. However, the approach fails to interpret the free-flowing test data from two artesian boreholes in the fractured-rock aquifer in Table Mountain Group (TMG) of South Africa. The diagnostic plot method using the reciprocal rate derivative is adapted to evaluate the artesian aquifer properties. The variation of the derivative helps not only identify flow regimes and discern the boundary conditions, but also facilitates conceptualization of the aquifer system and selection of an appropriate model for data interpretation later on. Test data from two free-flowing tests conducted in different sites in TMG are analysed using the diagnostic plot method. Based on the results, conceptual models and appropriate approaches are developed to evaluate the aquifer properties. The advantages and limitations of using the diagnostic plot method on free-flowing test data are discussed.

Jack Rabbit Pretest Data For TATB Based IHE Model Development

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

Hart, M M; Strand, O T; Bosson, S T

The Jack Rabbit Pretest series consisted of 5 focused hydrodynamic experiments, 2021E PT3, PT4, PT5, PT6, and PT7. They were fired in March and April of 2008 at the Contained Firing Facility, Site 300, Lawrence Livermore National Laboratory, Livermore, California. These experiments measured dead-zone formation and impulse gradients created during the detonation of TATB based insensitive high explosive. This document contains reference data tables for all 5 experiments. These data tables include: (1) Measured laser velocimetry of the experiment diagnostic plate (2) Computed diagnostic plate profile contours through velocity integration (3) Computed center axis pressures through velocity differentiation. All timesmore » are in microseconds, referenced from detonator circuit current start. All dimensions are in millimeters. Schematic axi-symmetric cross sections are shown for each experiment. These schematics detail the materials used and dimensions of the experiment and component parts. This should allow anyone wanting to evaluate their TATB based insensitive high explosive detonation model against experiment. These data are particularly relevant in examining reactive flow detonation model prediction in computational simulation of dead-zone formation and resulting impulse gradients produced by detonating TATB based explosive.« less

Optical diagnostics of osteoblast cells and osteogenic drug screening

NASA Astrophysics Data System (ADS)

Kolanti, Elayaraja; Veerla, Sarath C.; Khajuria, Deepak K.; Roy Mahapatra, D.

2016-02-01

Microfluidic device based diagnostics involving optical fibre path, in situ imaging and spectroscopy are gaining importance due to recent advances in diagnostics instrumentation and methods, besides other factors such as low amount of reagent required for analysis, short investigation times, and potential possibilities to replace animal model based study in near future. It is possible to grow and monitor tissues in vitro in microfluidic lab-on-chip. It may become a transformative way of studying how cells interact with drugs, pathogens and biomaterials in physiologically relevant microenvironments. To a large extent, progress in developing clinically viable solutions has been constrained because of (i) contradiction between in vitro and in vivo results and (ii) animal model based and clinical studies which is very expensive. Our study here aims to evaluate the usefulness of microfluidic device based 3D tissue growth and monitoring approach to better emulate physiologically and clinically relevant microenvironments in comparison to conventional in vitro 2D culture. Moreover, the microfluidic methodology permits precise high-throughput investigations through real-time imaging while using very small amounts of reagents and cells. In the present study, we report on the details of an osteoblast cell based 3D microfluidic platform which we employ for osteogenic drug screening. The drug formulation is functionalized with fluorescence and other biomarkers for imaging and spectroscopy, respectively. Optical fibre coupled paths are used to obtain insight regarding the role of stress/flow pressure fluctuation and nanoparticle-drug concentration on the osteoblast growth and osteogenic properties of bone.

Consultation-Based Academic Intervention for Children with Attention Deficit Hyperactivity Disorder: School Functioning Outcomes

ERIC Educational Resources Information Center

Jitendra, Asha K.; DuPaul, George J.; Volpe, Robert J.; Tresco, Katy E.; Junod, Rosemary E. Vile; Lutz, J. Gary; Cleary, Kristi S.; Flammer-Rivera, Lizette M.; Manella, Mark C.

2007-01-01

This study evaluated the effectiveness of two consultation-based models for designing academic interventions to enhance the educational functioning of children with attention deficit hyperactivity disorder. Children (N = 167) meeting "Diagnostic and Statistical Manual" (4th ed.--text revision; American Psychiatric Association, 2000) criteria for…

Middle Atmosphere Program. Handbook for MAP, Volume 10

NASA Technical Reports Server (NTRS)

Taubenheim, J. (Editor)

1984-01-01

The contributions of ground based investigations to the study of middle atmospheric phenomena are addressed. General topics include diagnostics of the middle atmosphere from D region properties, winter anomaly, seasonal variations and disturbances, dynamics and theoretical models, ground based tracking of winds and waves, lower thermosphere phenomena, and solar-terrestrial influences.

Casting wider nets for anxiety and depression: disability-driven cross-diagnostic subtypes in a large cohort.

PubMed

Wanders, R B K; van Loo, H M; Vermunt, J K; Meijer, R R; Hartman, C A; Schoevers, R A; Wardenaar, K J; de Jonge, P

2016-12-01

In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures. A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables. The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms ['Somatic' (13.0%), and 'Worried' (14.0%)] and psychopathological symptoms ['Subclinical' (8.8%), and 'Clinical' (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1-12.3, and chronic stress: OR 3.7-4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found. An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

PubMed Central

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme.

PubMed

Yizhao, Chen; Jianyang, Xia; Zhengguo, Sun; Jianlong, Li; Yiqi, Luo; Chengcheng, Gang; Zhaoqi, Wang

2015-11-06

As a key factor that determines carbon storage capacity, residence time (τE) is not well constrained in terrestrial biosphere models. This factor is recognized as an important source of model uncertainty. In this study, to understand how τE influences terrestrial carbon storage prediction in diagnostic models, we introduced a model decomposition scheme in the Boreal Ecosystem Productivity Simulator (BEPS) and then compared it with a prognostic model. The result showed that τE ranged from 32.7 to 158.2 years. The baseline residence time (τ'E) was stable for each biome, ranging from 12 to 53.7 years for forest biomes and 4.2 to 5.3 years for non-forest biomes. The spatiotemporal variations in τE were mainly determined by the environmental scalar (ξ). By comparing models, we found that the BEPS uses a more detailed pool construction but rougher parameterization for carbon allocation and decomposition. With respect to ξ comparison, the global difference in the temperature scalar (ξt) averaged 0.045, whereas the moisture scalar (ξw) had a much larger variation, with an average of 0.312. We propose that further evaluations and improvements in τ'E and ξw predictions are essential to reduce the uncertainties in predicting carbon storage by the BEPS and similar diagnostic models.

The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme

PubMed Central

Yizhao, Chen; Jianyang, Xia; Zhengguo, Sun; Jianlong, Li; Yiqi, Luo; Chengcheng, Gang; Zhaoqi, Wang

2015-01-01

As a key factor that determines carbon storage capacity, residence time (τE) is not well constrained in terrestrial biosphere models. This factor is recognized as an important source of model uncertainty. In this study, to understand how τE influences terrestrial carbon storage prediction in diagnostic models, we introduced a model decomposition scheme in the Boreal Ecosystem Productivity Simulator (BEPS) and then compared it with a prognostic model. The result showed that τE ranged from 32.7 to 158.2 years. The baseline residence time (τ′E) was stable for each biome, ranging from 12 to 53.7 years for forest biomes and 4.2 to 5.3 years for non-forest biomes. The spatiotemporal variations in τE were mainly determined by the environmental scalar (ξ). By comparing models, we found that the BEPS uses a more detailed pool construction but rougher parameterization for carbon allocation and decomposition. With respect to ξ comparison, the global difference in the temperature scalar (ξt) averaged 0.045, whereas the moisture scalar (ξw) had a much larger variation, with an average of 0.312. We propose that further evaluations and improvements in τ′E and ξw predictions are essential to reduce the uncertainties in predicting carbon storage by the BEPS and similar diagnostic models. PMID:26541245

Examining the DSM-5 alternative personality disorder model operationalization of antisocial personality disorder and psychopathy in a male correctional sample.

PubMed

Wygant, Dustin B; Sellbom, Martin; Sleep, Chelsea E; Wall, Tina D; Applegate, Kathryn C; Krueger, Robert F; Patrick, Christopher J

2016-07-01

For decades, it has been known that the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnosis of Antisocial Personality Disorder (ASPD) is a nonadequate operationalization of psychopathy (Crego & Widiger, 2015). The DSM-5 alternative model of personality disorders provides an opportunity to rectify some of these long held concerns. The current study compared the Section III alternative model's trait-based conception of ASPD with the categorical model from the main diagnostic codes section of DSM-5 in terms of associations with differing models of psychopathy. We also evaluated the validity of the trait-based conception more broadly in relation to measures of antisocial tendencies as well as psychopathy. Participants were 200 male inmates who were administered a battery of self-report and interview-based researcher rating measures of relevant constructs. Analyses showed that Section III ASPD outperformed Section II ASPD in predicting scores on Hare's (2003) Psychopathy Checklist-Revised (PCL-R; r = .88 vs. .59). Additionally, aggregate scores for Section III ASPD performed well in capturing variance in differing ASPD and psychopathy measures. Finally, we found that the Section III ASPD impairment criteria added incrementally to the Section III ASPD traits in predicting PCL-R psychopathy and SCID-II ASPD. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Base rates, multiple indicators, and comprehensive forensic evaluations: why sexualized behavior still counts in assessments of child sexual abuse allegations.

PubMed

Everson, Mark D; Faller, Kathleen Coulborn

2012-01-01

Developmentally inappropriate sexual behavior has long been viewed as a possible indicator of child sexual abuse. In recent years, however, the utility of sexualized behavior in forensic assessments of alleged child sexual abuse has been seriously challenged. This article addresses a number of the concerns that have been raised about the diagnostic value of sexualized behavior, including the claim that when population base rates for abuse are properly taken into account, the diagnostic value of sexualized behavior is insignificant. This article also identifies a best practice comprehensive evaluation model with a methodology that is effective in mitigating such concerns.

An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2010-01-01

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related technologies that assist operators in managing the health of their gas turbine engine assets. Trend monitoring is the process of monitoring the gradual performance change that an aircraft engine will naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of detecting and isolating the occurrence of any faults impacting engine flow-path performance. Today, performance trend monitoring and gas path fault diagnostic functions are performed by a combination of on-board and off-board strategies. On-board engine control computers contain logic that monitors for anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine trend monitoring and fault diagnostics based on data collected from each engine each flight. Continuing advances in avionics are enabling the migration of portions of the ground-based functionality on-board, giving rise to more sophisticated on-board engine health management capabilities. This paper reviews the conventional engine performance trend monitoring and gas path fault diagnostic architecture commonly applied today, and presents a proposed enhanced on-board architecture for future applications. The enhanced architecture gains real-time access to an expanded quantity of engine parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the enhanced architecture include the real-time continuous monitoring of engine health, the early diagnosis of fault conditions, and the estimation of unmeasured engine performance parameters. A future vision to advance the enhanced architecture is also presented and discussed

Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling

PubMed Central

Houben, R.; Cohen, T.; Pai, M.; Cobelens, F.; Vassall, A.; Menzies, N. A.; Gomez, G. B.; Langley, I.; Squire, S. B.; White, R.

2014-01-01

SUMMARY The landscape of diagnostic testing for tuberculosis (TB) is changing rapidly, and stakeholders need urgent guidance on how to develop, deploy and optimize TB diagnostics in a way that maximizes impact and makes best use of available resources. When decisions must be made with only incomplete or preliminary data available, modelling is a useful tool for providing such guidance. Following a meeting of modelers and other key stakeholders organized by the TB Modelling and Analysis Consortium, we propose a conceptual framework for positioning models of TB diagnostics. We use that framework to describe modelling priorities in four key areas: Xpert® MTB/RIF scale-up, target product profiles for novel assays, drug susceptibility testing to support new drug regimens, and the improvement of future TB diagnostic models. If we are to maximize the impact and cost-effectiveness of TB diagnostics, these modelling priorities should figure prominently as targets for future research. PMID:25189546

The Method of Fundamental Solutions using the Vector Magnetic Dipoles for Calculation of the Magnetic Fields in the Diagnostic Problems Based on Full-Scale Modelling Experiment

NASA Astrophysics Data System (ADS)

Bakhvalov, Yu A.; Grechikhin, V. V.; Yufanova, A. L.

2016-04-01

The article describes the calculation of the magnetic fields in the problems diagnostic of technical systems based on the full-scale modeling experiment. Use of gridless fundamental solution method and its variants in combination with grid methods (finite differences and finite elements) are allowed to considerably reduce the dimensionality task of the field calculation and hence to reduce calculation time. When implementing the method are used fictitious magnetic charges. In addition, much attention is given to the calculation accuracy. Error occurs when wrong choice of the distance between the charges. The authors are proposing to use vector magnetic dipoles to improve the accuracy of magnetic fields calculation. Examples of this approacharegiven. The article shows the results of research. They are allowed to recommend the use of this approach in the method of fundamental solutions for the full-scale modeling tests of technical systems.

An SSME High Pressure Oxidizer Turbopump diagnostic system using G2 real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2 real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for the SSME. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach has been adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

Lyme disease: the promise of Big Data, companion diagnostics and precision medicine

PubMed Central

Stricker, Raphael B; Johnson, Lorraine

2016-01-01

Lyme disease caused by the spirochete Borrelia burgdorferi has become a major worldwide epidemic. Recent studies based on Big Data registries show that >300,000 people are diagnosed with Lyme disease each year in the USA, and up to two-thirds of individuals infected with B. burgdorferi will fail conventional 30-year-old antibiotic therapy for Lyme disease. In addition, animal and human evidence suggests that sexual transmission of the Lyme spirochete may occur. Improved companion diagnostic tests for Lyme disease need to be implemented, and novel treatment approaches are urgently needed to combat the epidemic. In particular, therapies based on the principles of precision medicine could be modeled on successful “designer drug” treatment for HIV/AIDS and hepatitis C virus infection featuring targeted protease inhibitors. The use of Big Data registries, companion diagnostics and precision medicine will revolutionize the diagnosis and treatment of Lyme disease. PMID:27672336

Design of a Neutron Temporal Diagnostic for measuring DD or DT burn histories at the NIF

NASA Astrophysics Data System (ADS)

Lahmann, B.; Frenje, J. A.; Sio, H.; Petrasso, R. D.; Bradley, D. K.; Le Pape, S.; MacKinnon, A. J.; Isumi, N.; Macphee, A.; Zayas, C.; Spears, B. K.; Hermann, H.; Hilsabeck, T. J.; Kilkenny, J. D.

2015-11-01

The DD or DT burn history in Inertial Confinement Fusion (ICF) implosions provides essential information about implosion performance and helps to constrain numerical modeling. The capability of measuring this burn history is thus important for the NIF in its pursuit of ignition. Currently, the Gamma Reaction History (GRH) diagnostic is the only system capable of measuring the burn history for DT implosions with yields greater than ~ 1e14. To complement GRH, a new NIF Neutron Temporal Diagnostic (NTD) is being designed for measuring the DD or DT burn history with yields greater than ~ 1e10. A traditional scintillator-based design and a pulse-dilation-based design are being considered. Using MCNPX simulations, both designs have been optimized, validated and contrasted for various types of implosions at the NIF. This work was supported in part by the U.S. DOE, LLNL and LLE.

An SSME high pressure oxidizer turbopump diagnostic system using G2(TM) real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2(TM) real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for Space Shuttle Main Engine. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach was adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

Development of a diagnosis- and procedure-based risk model for 30-day outcome after pediatric cardiac surgery.

PubMed

Crowe, Sonya; Brown, Kate L; Pagel, Christina; Muthialu, Nagarajan; Cunningham, David; Gibbs, John; Bull, Catherine; Franklin, Rodney; Utley, Martin; Tsang, Victor T

2013-05-01

The study objective was to develop a risk model incorporating diagnostic information to adjust for case-mix severity during routine monitoring of outcomes for pediatric cardiac surgery. Data from the Central Cardiac Audit Database for all pediatric cardiac surgery procedures performed in the United Kingdom between 2000 and 2010 were included: 70% for model development and 30% for validation. Units of analysis were 30-day episodes after the first surgical procedure. We used logistic regression for 30-day mortality. Risk factors considered included procedural information based on Central Cardiac Audit Database "specific procedures," diagnostic information defined by 24 "primary" cardiac diagnoses and "univentricular" status, and other patient characteristics. Of the 27,140 30-day episodes in the development set, 25,613 were survivals, 834 were deaths, and 693 were of unknown status (mortality, 3.2%). The risk model includes procedure, cardiac diagnosis, univentricular status, age band (neonate, infant, child), continuous age, continuous weight, presence of non-Down syndrome comorbidity, bypass, and year of operation 2007 or later (because of decreasing mortality). A risk score was calculated for 95% of cases in the validation set (weight missing in 5%). The model discriminated well; the C-index for validation set was 0.77 (0.81 for post-2007 data). Removal of all but procedural information gave a reduced C-index of 0.72. The model performed well across the spectrum of predicted risk, but there was evidence of underestimation of mortality risk in neonates undergoing operation from 2007. The risk model performs well. Diagnostic information added useful discriminatory power. A future application is risk adjustment during routine monitoring of outcomes in the United Kingdom to assist quality assurance. Copyright © 2013 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.

Clinical usefulness of a biomarker-based diagnostic test for acute stroke: the Biomarker Rapid Assessment in Ischemic Injury (BRAIN) study.

PubMed

Laskowitz, Daniel T; Kasner, Scott E; Saver, Jeffrey; Remmel, Kerri S; Jauch, Edward C

2009-01-01

One of the significant limitations in the evaluation and management of patients with suspected acute cerebral ischemia is the absence of a widely available, rapid, and sensitive diagnostic test. The objective of the current study was to assess whether a test using a panel of biomarkers might provide useful diagnostic information in the early evaluation of stroke by differentiating patients with cerebral ischemia from other causes of acute neurological deficit. A total of 1146 patients presenting with neurological symptoms consistent with possible stroke were prospectively enrolled at 17 different sites. Timed blood samples were assayed for matrix metalloproteinase 9, brain natriuretic factor, d-dimer, and protein S100beta. A separate cohort of 343 patients was independently enrolled to validate the multiple biomarker model approach. A diagnostic tool incorporating the values of matrix metalloproteinase 9, brain natriuretic factor, d-dimer, and S-100beta into a composite score was sensitive for acute cerebral ischemia. The multivariate model demonstrated modest discriminative capabilities with an area under the receiver operating characteristic curve of 0.76 for hemorrhagic stroke and 0.69 for all stroke (likelihood test P<0.001). When the threshold for the logistic model was set at the first quartile, this resulted in a sensitivity of 86% for detecting all stroke and a sensitivity of 94% for detecting hemorrhagic stroke. Moreover, results were reproducible in a separate cohort tested on a point-of-care platform. These results suggest that a biomarker panel may add valuable and time-sensitive diagnostic information in the early evaluation of stroke. Such an approach is feasible on a point-of-care platform. The rapid identification of patients with suspected stroke would expand the availability of time-limited treatment strategies. Although the diagnostic accuracy of the current panel is clearly imperfect, this study demonstrates the feasibility of incorporating a biomarker based point-of-care algorithm with readily available clinical data to aid in the early evaluation and management of patients at high risk for cerebral ischemia.

Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B

PubMed Central

Cheng, Jieyao; Hou, Jinlin; Ding, Huiguo; Chen, Guofeng; Xie, Qing; Wang, Yuming; Zeng, Minde; Ou, Xiaojuan; Ma, Hong; Jia, Jidong

2015-01-01

Background and Aims Noninvasive models have been developed for fibrosis assessment in patients with chronic hepatitis B. However, the sensitivity, specificity and diagnostic accuracy in evaluating liver fibrosis of these methods have not been validated and compared in the same group of patients. The aim of this study was to verify the diagnostic performance and reproducibility of ten reported noninvasive models in a large cohort of Asian CHB patients. Methods The diagnostic performance of ten noninvasive models (HALF index, FibroScan, S index, Zeng model, Youyi model, Hui model, APAG, APRI, FIB-4 and FibroTest) was assessed against the liver histology by ROC curve analysis in CHB patients. The reproducibility of the ten models were evaluated by recalculating the diagnostic values at the given cut-off values defined by the original studies. Results Six models (HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest) had AUROCs higher than 0.70 in predicting any fibrosis stage and 2 of them had best diagnostic performance with AUROCs to predict F≥2, F≥3 and F4 being 0.83, 0.89 and 0.89 for HALF index, 0.82, 0.87 and 0.87 for FibroScan, respectively. Four models (HALF index, FibroScan, Zeng model and Youyi model) showed good diagnostic values at given cut-offs. Conclusions HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest show a good diagnostic performance and all of them, except S index and FibroTest, have good reproducibility for evaluating liver fibrosis in CHB patients. Registration Number ChiCTR-DCS-07000039. PMID:26709706

Development of Methods to Predict the Effects of Test Media in Ground-Based Propulsion Testing

NASA Technical Reports Server (NTRS)

Drummond, J. Philip; Danehy, Paul M.; Gaffney, Richard L., Jr.; Parker, Peter A.; Tedder, Sarah A.; Chelliah, Harsha K.; Cutler, Andrew D.; Bivolaru, Daniel; Givi, Peyman; Hassan, Hassan A.

2009-01-01

This report discusses work that began in mid-2004 sponsored by the Office of the Secretary of Defense (OSD) Test & Evaluation/Science & Technology (T&E/S&T) Program. The work was undertaken to improve the state of the art of CFD capabilities for predicting the effects of the test media on the flameholding characteristics in scramjet engines. The program had several components including the development of advanced algorithms and models for simulating engine flowpaths as well as a fundamental experimental and diagnostic development effort to support the formulation and validation of the mathematical models. This report provides details of the completed work, involving the development of phenomenological models for Reynolds averaged Navier-Stokes codes, large-eddy simulation techniques and reduced-kinetics models. Experiments that provided data for the modeling efforts are also described, along with with the associated nonintrusive diagnostics used to collect the data.

Predicting the Effects of Test Media in Ground-Based Propulsion Testing

NASA Technical Reports Server (NTRS)

Drummond, J. Philip; Danehy, Paul M.; Bivolaru, Daniel; Gaffney, Richard L.; Parker, Peter A.; Chelliah, Harsha K.; Cutler, Andrew D.; Givi, Peyman; Hassan, Hassan, A.

2006-01-01

This paper discusses the progress of work which began in mid-2004 sponsored by the Office of the Secretary of Defense (OSD) Test & Evaluation/Science & Technology (T&E/S&T) Program. The purpose of the work is to improve the state of the art of CFD capabilities for predicting the effects of the test media on the flameholding characteristics in scramjet engines. The program has several components including the development of advance algorithms and models for simulating engine flowpaths as well as a fundamental experimental and diagnostic development effort to support the formulation and validation of the mathematical models. The paper will provide details of current work involving the development of phenomenological models for Reynolds averaged Navier-Stokes codes, large-eddy simulation techniques and reduced-kinetics models. Experiments that will provide data for the modeling efforts will also be described, along with with the associated nonintrusive diagnostics used to collect the data.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Deep Learning Role in Early Diagnosis of Prostate Cancer

PubMed Central

Reda, Islam; Khalil, Ashraf; Elmogy, Mohammed; Abou El-Fetouh, Ahmed; Shalaby, Ahmed; Abou El-Ghar, Mohamed; Elmaghraby, Adel; Ghazal, Mohammed; El-Baz, Ayman

2018-01-01

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system. PMID:29804518

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

DOE PAGES

Tsushima, Yoko; Brient, Florent; Klein, Stephen A.; ...

2017-11-27

The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. Here, this paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments willmore » also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds – including cloud–circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.« less

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

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

Tsushima, Yoko; Brient, Florent; Klein, Stephen A.

The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. Here, this paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments willmore » also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds – including cloud–circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.« less

Final-year diagnostic radiography students' perception of role models within the profession.

PubMed

Conway, Alinya; Lewis, Sarah; Robinson, John

2008-01-01

Within a clinical education setting, the value of role models and prescribed mentors can be seen as an important influence in shaping the student's future as a diagnostic radiographer. A study was undertaken to create a new understanding of how diagnostic radiography students perceive role models and professional behavior in the workforce. The study aimed to determine the impact of clinical education in determining modeling expectations, role model identification and attributes, and the integration of academic education and "hands-on" clinical practice in preparing diagnostic radiography students to enter the workplace. Thirteen final-year (third-year) diagnostic radiography students completed an hour-long interview regarding their experiences and perceptions of role models while on clinical placement. The key concepts that emerged illustrated that students gravitate toward radiographers who enjoy sharing practical experiences with students and are good communicators. Unique to diagnostic radiography, students made distinctions about the presence of role models in private versus public service delivery. This study gives insight to clinical educators in diagnostic radiography and wider allied health into how students perceive role models, interact with preceptors, and combine real-life experiences with formal learning.

Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives.

PubMed

Handels, H; Ehrhardt, J

2009-01-01

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

Rasch Model Based Analysis of the Force Concept Inventory

ERIC Educational Resources Information Center

Planinic, Maja; Ivanjek, Lana; Susac, Ana

2010-01-01

The Force Concept Inventory (FCI) is an important diagnostic instrument which is widely used in the field of physics education research. It is therefore very important to evaluate and monitor its functioning using different tools for statistical analysis. One of such tools is the stochastic Rasch model, which enables construction of linear…

Effect of Worked Examples on Mental Model Progression in a Computer-Based Simulation Learning Environment

ERIC Educational Resources Information Center

Darabi, Aubteen; Nelson, David W.; Meeker, Richard; Liang, Xinya; Boulware, Wilma

2010-01-01

In a diagnostic problem solving operation of a computer-simulated chemical plant, chemical engineering students were randomly assigned to two groups: one studying product-oriented worked examples, the other practicing conventional problem solving. Effects of these instructional strategies on the progression of learners' mental models were examined…

A Memory Based Model of Posttraumatic Stress Disorder: Evaluating Basic Assumptions Underlying the PTSD Diagnosis

PubMed Central

Rubin, David C.; Berntsen, Dorthe; Johansen, Malene Klindt

2009-01-01

In the mnemonic model of PTSD, the current memory of a negative event, not the event itself determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the DSM. The model accounts for important and reliable findings that are often inconsistent with the current diagnostic view and that have been neglected by theoretical accounts of the disorder, including the following observations. The diagnosis needs objective information about the trauma and peritraumatic emotions, but uses retrospective memory reports that can have substantial biases. Negative events and emotions that do not satisfy the current diagnostic criteria for a trauma can be followed by symptoms that would otherwise qualify for PTSD. Predisposing factors that affect the current memory have large effects on symptoms. The inability-to-recall-an-important-aspect-of-the-trauma symptom does not correlate with other symptoms. Loss or enhancement of the trauma memory affects PTSD symptoms in predictable ways. Special mechanisms that apply only to traumatic memories are not needed, increasing parsimony and the knowledge that can be applied to understanding PTSD. PMID:18954211

A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means.

PubMed

Polak, Marike; de Rooij, Mark; Heiser, Willem J

2012-09-01

In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) criterion of irrelevance, which is a graphical, exploratory method for evaluating the "relevance" of dichotomous attitude items. We generalized this criterion to graded response items and quantified the relevance by fitting a unimodal smoother. The resulting goodness-of-fit was used to determine item fit and aggregated scale fit. Based on a simulation procedure, cutoff values were proposed for the measures of item fit. These cutoff values showed high power rates and acceptable Type I error rates. We present 2 applications of the OCM method. First, we apply the OCM method to personality data from the Developmental Profile; second, we analyze attitude data collected by Roberts and Laughlin (1996) concerning opinions of capital punishment.

Stage Separation Failure: Model Based Diagnostics and Prognostics

NASA Technical Reports Server (NTRS)

Luchinsky, Dmitry; Hafiychuk, Vasyl; Kulikov, Igor; Smelyanskiy, Vadim; Patterson-Hine, Ann; Hanson, John; Hill, Ashley

2010-01-01

Safety of the next-generation space flight vehicles requires development of an in-flight Failure Detection and Prognostic (FD&P) system. Development of such system is challenging task that involves analysis of many hard hitting engineering problems across the board. In this paper we report progress in the development of FD&P for the re-contact fault between upper stage nozzle and the inter-stage caused by the first stage and upper stage separation failure. A high-fidelity models and analytical estimations are applied to analyze the following sequence of events: (i) structural dynamics of the nozzle extension during the impact; (ii) structural stability of the deformed nozzle in the presence of the pressure and temperature loads induced by the hot gas flow during engine start up; and (iii) the fault induced thrust changes in the steady burning regime. The diagnostic is based on the measurements of the impact torque. The prognostic is based on the analysis of the correlation between the actuator signal and fault-induced changes in the nozzle structural stability and thrust.

Ophthalmologic diagnostic tool using MR images for biomechanically-based muscle volume deformation

NASA Astrophysics Data System (ADS)

Buchberger, Michael; Kaltofen, Thomas

2003-05-01

We would like to give a work-in-progress report on our ophthalmologic diagnostic software system which performs biomechanically-based muscle volume deformations using MR images. For reconstructing a three-dimensional representation of an extraocular eye muscle, a sufficient amount of high resolution MR images is used, each representing a slice of the muscle. In addition, threshold values are given, which restrict the amount of data used from the MR images. The Marching Cube algorithm is applied to the polygons, resulting in a 3D representation of the muscle, which can efficiently be rendered. A transformation to a dynamic, deformable model is applied by calculating the center of gravity of each muscle slice, approximating the muscle path and subsequently adding Hermite splines through the centers of gravity of all slices. Then, a radius function is defined for each slice, completing the transformation of the static 3D polygon model. Finally, this paper describes future extensions to our system. One of these extensions is the support for additional calculations and measurements within the reconstructed 3D muscle representation. Globe translation, localization of muscle pulleys by analyzing the 3D reconstruction in two different gaze positions and other diagnostic measurements will be available.

Molecular diagnosis and precision medicine in allergy management.

PubMed

Riccio, Anna Maria; De Ferrari, Laura; Chiappori, Alessandra; Ledda, Sabina; Passalacqua, Giovanni; Melioli, Giovanni; Canonica, Giorgio Walter

2016-11-01

Precision medicine (PM) can be defined as a structural model aimed at customizing healthcare, with medical decisions/products tailored on an individual patient at a highly detailed level. In this sense, allergy diagnostics based on molecular allergen components allows to accurately define the patient's IgE repertoire. The availability of highly specialized singleplexed and multiplexed platforms support allergists with an advanced diagnostic armamentarium. The therapeutic intervention, driven by the standard diagnostic approach, but further supported by these innovative tools may result, for instance, in a more appropriate prescription of allergen immunotherapy (AIT). Also, the phenotyping of patients, which may have relevant effects on the treatment strategy, could be take advantage by the molecular allergy diagnosis.

Revived STIS. II. Properties of Stars in the Next Generation Spectral Library

NASA Technical Reports Server (NTRS)

Heap, Sara R.; Lindler, D.

2010-01-01

Spectroscopic surveys of galaxies at high redshift will bring the rest-frame ultraviolet into view of large, ground-based telescopes. The UV-blue spectral region is rich in diagnostics, but these diagnostics have not yet been calibrated in terms of the properties of the responsible stellar population(s). Such calibrations are now possible with Hubble's Next Generation Spectral Library (NGSL). The NGSL contains UV-optical spectra (0.2 - 1.0 microns) of 374 stars having a wide range in temperature, luminosity, and metallicity. We will describe our work to derive basic stellar parameters from NGSL spectra using modern model spectra and to use these stellar parameters to develop UV-blue spectral diagnostics.

A twin study of specific bulimia nervosa symptoms.

PubMed

Mazzeo, S E; Mitchell, K S; Bulik, C M; Aggen, S H; Kendler, K S; Neale, M C

2010-07-01

Twin studies have suggested that additive genetic factors significantly contribute to liability to bulimia nervosa (BN). However, the diagnostic criteria for BN remain controversial. In this study, an item-factor model was used to examine the BN diagnostic criteria and the genetic and environmental contributions to BN in a population-based twin sample. The validity of the equal environment assumption (EEA) for BN was also tested. Participants were 1024 female twins (MZ n=614, DZ n=410) from the population-based Mid-Atlantic Twin Registry. BN was assessed using symptom-level (self-report) items consistent with DSM-IV and ICD-10 diagnostic criteria. Items assessing BN were included in an item-factor model. The EEA was measured by items assessing similarity of childhood and adolescent environment, which have demonstrated construct validity. Scores on the EEA factor were used to specify the degree to which twins shared environmental experiences in this model. The EEA was not violated for BN. Modeling results indicated that the majority of the variance in BN was due to additive genetic factors. There was substantial variability in additive genetic and environmental contributions to specific BN symptoms. Most notably, vomiting was very strongly influenced by additive genetic factors, while other symptoms were much less heritable, including the influence of weight on self-evaluation. These results highlight the importance of assessing eating disorders at the symptom level. Refinement of eating disorder phenotypes could ultimately lead to improvements in treatment and targeted prevention, by clarifying sources of variation for specific components of symptomatology.

[Case finding in early prevention networks - a heuristic for ambulatory care settings].

PubMed

Barth, Michael; Belzer, Florian

2016-06-01

One goal of early prevention is the support of families with small children up to three years who are exposed to psychosocial risks. The identification of these cases is often complex and not well-directed, especially in the ambulatory care setting. Development of a model of a feasible and empirical based strategy for case finding in ambulatory care. Based on the risk factors of postpartal depression, lack of maternal responsiveness, parental stress with regulation disorders and poverty a lexicographic and non-compensatory heuristic model with simple decision rules, will be constructed and empirically tested. Therefore the original data set from an evaluation of the pediatric documentary form on psychosocial issues of families with small children in well-child visits will be used and reanalyzed. The first diagnostic step in the non-compensatory and hierarchical classification process is the assessment of postpartal depression followed by maternal responsiveness, parental stress and poverty. The classification model identifies 89.0 % cases from the original study. Compared to the original study the decision process becomes clearer and more concise. The evidence-based and data-driven model exemplifies a strategy for the assessment of psychosocial risk factors in ambulatory care settings. It is based on four evidence-based risk factors and offers a quick and reliable classification. A further advantage of this model is that after a risk factor is identified the diagnostic procedure will be stopped and the counselling process can commence. For further validation of the model studies, in well suited early prevention networks are needed.

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

2011-01-01

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Explicit awareness supports conditional visual search in the retrieval guidance paradigm.

PubMed

Buttaccio, Daniel R; Lange, Nicholas D; Hahn, Sowon; Thomas, Rick P

2014-01-01

In four experiments we explored whether participants would be able to use probabilistic prompts to simplify perceptually demanding visual search in a task we call the retrieval guidance paradigm. On each trial a memory prompt appeared prior to (and during) the search task and the diagnosticity of the prompt(s) was manipulated to provide complete, partial, or non-diagnostic information regarding the target's color on each trial (Experiments 1-3). In Experiment 1 we found that the more diagnostic prompts was associated with faster visual search performance. However, similar visual search behavior was observed in Experiment 2 when the diagnosticity of the prompts was eliminated, suggesting that participants in Experiment 1 were merely relying on base rate information to guide search and were not utilizing the prompts. In Experiment 3 participants were informed of the relationship between the prompts and the color of the target and this was associated with faster search performance relative to Experiment 1, suggesting that the participants were using the prompts to guide search. Additionally, in Experiment 3 a knowledge test was implemented and performance in this task was associated with qualitative differences in search behavior such that participants that were able to name the color(s) most associated with the prompts were faster to find the target than participants who were unable to do so. However, in Experiments 1-3 diagnosticity of the memory prompt was manipulated via base rate information, making it possible that participants were merely relying on base rate information to inform search in Experiment 3. In Experiment 4 we manipulated diagnosticity of the prompts without manipulating base rate information and found a similar pattern of results as Experiment 3. Together, the results emphasize the importance of base rate and diagnosticity information in visual search behavior. In the General discussion section we explore how a recent computational model of hypothesis generation (HyGene; Thomas, Dougherty, Sprenger, & Harbison, 2008), linking attention with long-term and working memory, accounts for the present results and provides a useful framework of cued recall visual search. Copyright © 2013 Elsevier B.V. All rights reserved.

Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

NASA Technical Reports Server (NTRS)

Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be developed easily.

Similarities and Differences in Diagnostic Criterion.

PubMed

Wei, Zhengde; Zhang, Xiaochu

2017-01-01

In this chapter, the main content is to discuss the similarities and differences in diagnostic criteria between substance and non-substance addictions. Firstly, diagnostic criteria of substance addiction were introduced, mainly focused on Diagnostic and Statistical Manual for the Mental Disorders, fifth edition (DSM-5). Then, we described the diagnostic criteria of several non-substance addictions, including gambling disorder, internet addiction, food addiction and hypersexual disorder. Depending on the proof, substance and non-substance addictions have many similarities in symptoms. Though the proposed diagnostic criteria of many non-substance addictions are currently most useful as survey instruments to access the prevalence of the problem, there is little or no validating proof for these diagnostic criteria. Finally, animal model is useful tool for addiction research. But, present animal models for gambling studying do not meet enough diagnostic criteria and could not be regarded as gambling disorder. By introducing the animal models evolved to resemble the diagnostic criteria of substance addiction and two classical paradigms for substance addiction, self-administration and conditioned place preference, we hope it is helpful to improve the validation of animal model of gambling disorder.

Wearable Inertial Sensors Allow for Quantitative Assessment of Shoulder and Elbow Kinematics in a Cadaveric Knee Arthroscopy Model.

PubMed

Rose, Michael; Curtze, Carolin; O'Sullivan, Joseph; El-Gohary, Mahmoud; Crawford, Dennis; Friess, Darin; Brady, Jacqueline M

2017-12-01

To develop a model using wearable inertial sensors to assess the performance of orthopaedic residents while performing a diagnostic knee arthroscopy. Fourteen subjects performed a diagnostic arthroscopy on a cadaveric right knee. Participants were divided into novices (5 postgraduate year 3 residents), intermediates (5 postgraduate year 4 residents), and experts (4 faculty) based on experience. Arm movement data were collected by inertial measurement units (Opal sensors) by securing 2 sensors to each upper extremity (dorsal forearm and lateral arm) and 2 sensors to the trunk (sternum and lumbar spine). Kinematics of the elbow and shoulder joints were calculated from the inertial data by biomechanical modeling based on a sequence of links connected by joints. Range of motion required to complete the procedure was calculated for each group. Histograms were used to compare the distribution of joint positions for an expert, intermediate, and novice. For both the right and left upper extremities, skill level corresponded well with shoulder abduction-adduction and elbow prono-supination. Novices required on average 17.2° more motion in the right shoulder abduction-adduction plane than experts to complete the diagnostic arthroscopy (P = .03). For right elbow prono-supination (probe hand), novices required on average 23.7° more motion than experts to complete the procedure (P = .03). Histogram data showed novices had markedly more variability in shoulder abduction-adduction and elbow prono-supination compared with the other groups. Our data show wearable inertial sensors can measure joint kinematics during diagnostic knee arthroscopy. Range-of-motion data in the shoulder and elbow correlated inversely with arthroscopic experience. Motion pattern-based analysis shows promise as a metric of resident skill acquisition and development in arthroscopy. Wearable inertial sensors show promise as metrics of arthroscopic skill acquisition among residents. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model

NASA Astrophysics Data System (ADS)

Vira, J.; Sofiev, M.

2015-02-01

This paper describes the assimilation of trace gas observations into the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) using the 3D-Var method. Assimilation results for the year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the AirBase observation database, which provides the observational data set used in this study. Attention was paid to the background and observation error covariance matrices, which were obtained primarily by the iterative application of a posteriori diagnostics. The diagnostics were computed separately for 2 months representing summer and winter conditions, and further disaggregated by time of day. This enabled the derivation of background and observation error covariance definitions, which included both seasonal and diurnal variation. The consistency of the obtained covariance matrices was verified using χ2 diagnostics. The analysis scores were computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values was improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

Development and utilization of new diagnostics for dense-phase pneumatic transport. Quarterly technical progress report, October 1-December 31, 1989

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

Louge, M. Y.; Jenkins, J. T.

The main objective of this work is to develop probes for local measurements of solid velocity and holdup in dense gas-solid flows. In particular, capacitance probes are designed to measure local, time-dependent particle concentrations. In addition, a new optical fiber probe based on laser-induced-phosphorescence is developed to measure particle velocities. The principles for the capacitance and optical diagnostics were given in our first and second quarterly reports. In this reporting period, we have demonstrated with success the feasibility of the optical fiber probe. Another objective of this work is to develop a model of dense-phase conveying and to test thismore » model in a setup that incorporates our diagnostics. In this period, as a prelude to these modeling efforts scheduled for the third year of the contract, we have carried out additional computer simulations of rapid granular flows to verify the theories of Jenkins and Richman (1988) on the anisotropy of the second moment in simple shear. 2 refs., 5 figs.« less

A General Architecture for Intelligent Tutoring of Diagnostic Classification Problem Solving

PubMed Central

Crowley, Rebecca S.; Medvedeva, Olga

2003-01-01

We report on a general architecture for creating knowledge-based medical training systems to teach diagnostic classification problem solving. The approach is informed by our previous work describing the development of expertise in classification problem solving in Pathology. The architecture envelops the traditional Intelligent Tutoring System design within the Unified Problem-solving Method description Language (UPML) architecture, supporting component modularity and reuse. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. We outline the advantages and limitations of this general approach, and describe it’s implementation in SlideTutor–a developing Intelligent Tutoring System in Dermatopathology. PMID:14728159

Conveying Clinical Reasoning Based on Visual Observation via Eye-Movement Modelling Examples

ERIC Educational Resources Information Center

Jarodzka, Halszka; Balslev, Thomas; Holmqvist, Kenneth; Nystrom, Marcus; Scheiter, Katharina; Gerjets, Peter; Eika, Berit

2012-01-01

Complex perceptual tasks, like clinical reasoning based on visual observations of patients, require not only conceptual knowledge about diagnostic classes but also the skills to visually search for symptoms and interpret these observations. However, medical education so far has focused very little on how visual observation skills can be…

The Design of an ITS-Based Business Simulation: A New Epistemology for Learning.

ERIC Educational Resources Information Center

Gold, Steven C.

1998-01-01

Discusses the design and use of intelligent tutoring systems (ITS) for computerized business simulations. Reviews the use of ITS as an instructional technology; presents a model for ITS-based business simulations; examines the user interface and link between the ITS and simulation; and recommends expert-consultant diagnostic testing, and…

Transdiagnostic Theory and Application of Family-Based Treatment for Youth with Eating Disorders

ERIC Educational Resources Information Center

Loeb, Katharine L.; Lock, James; Greif, Rebecca; le Grange, Daniel

2012-01-01

This paper describes the transdiagnostic theory and application of family-based treatment (FBT) for children and adolescents with eating disorders. We review the fundamentals of FBT, a transdiagnostic theoretical model of FBT and the literature supporting its clinical application, adaptations across developmental stages and the diagnostic spectrum…

Ares I-X Ground Diagnostic Prototype

NASA Technical Reports Server (NTRS)

Schwabacher, Mark A.; Martin, Rodney Alexander; Waterman, Robert D.; Oostdyk, Rebecca Lynn; Ossenfort, John P.; Matthews, Bryan

2010-01-01

The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period of time before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.

Space applications of artificial intelligence; Proceedings of the Annual Goddard Conference, Greenbelt, MD, May 16, 17, 1989

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor); Dent, Carolyn P. (Editor)

1989-01-01

Theoretical and implementation aspects of AI systems for space applications are discussed in reviews and reports. Sections are devoted to planning and scheduling, fault isolation and diagnosis, data management, modeling and simulation, and development tools and methods. Particular attention is given to a situated reasoning architecture for space repair and replace tasks, parallel plan execution with self-processing networks, the electrical diagnostics expert system for Spacelab life-sciences experiments, diagnostic tolerance for missing sensor data, the integration of perception and reasoning in fast neural modules, a connectionist model for dynamic control, and applications of fuzzy sets to the development of rule-based expert systems.

Fetal and maternal dose assessment for diagnostic scans during pregnancy

NASA Astrophysics Data System (ADS)

Rafat Motavalli, Laleh; Miri Hakimabad, Hashem; Hoseinian Azghadi, Elie

2016-05-01

Despite the concerns about prenatal exposure to ionizing radiation, the number of nuclear medicine examinations performed for pregnant women increased in the past decade. This study attempts to better quantify radiation doses due to diagnostic nuclear medicine procedures during pregnancy with the help of our recently developed 3, 6, and 9 month pregnant hybrid phantoms. The reference pregnant models represent the adult female international commission on radiological protection (ICRP) reference phantom as a base template with a fetus in her gravid uterus. Six diagnostic scintigraphy scans using different radiopharmaceuticals were selected as typical diagnostic nuclear medicine procedures. Furthermore, the biokinetic data of radioiodine was updated in this study. A compartment representing iodide in fetal thyroid was addressed explicitly in the biokinetic model. Calculations were performed using the Monte Carlo transport method. Tabulated dose coefficients for both maternal and fetal organs are provided. The comparison was made with the previously published fetal doses calculated for stylized pregnant female phantoms. In general, the fetal dose in previous studies suffers from an underestimation of up to 100% compared to fetal dose at organ level in this study. A maximum of difference in dose was observed for the fetal thyroid compared to the previous studies, in which the traditional models did not contain the fetal thyroid. Cumulated activities of major source organs are primarily responsible for the discrepancies in the organ doses. The differences in fetal dose depend on several other factors including chord length distribution between fetal organs and maternal major source organs, and anatomical differences according to gestation periods. Finally, considering the results of this study, which was based on the realistic pregnant female phantoms, a more informed evaluation of the risks and benefits of the different procedures could be made.

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

DOE PAGES

Sippel, Sebastian; Lange, Holger; Mahecha, Miguel D.; ...

2016-10-20

Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observedmore » and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. Here we demonstrate that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.« less

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

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

Sippel, Sebastian; Lange, Holger; Mahecha, Miguel D.

Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observedmore » and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. Here we demonstrate that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.« less

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

PubMed Central

Sippel, Sebastian; Mahecha, Miguel D.; Hauhs, Michael; Bodesheim, Paul; Kaminski, Thomas; Gans, Fabian; Rosso, Osvaldo A.

2016-01-01

Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics. PMID:27764187

Latent class analysis of diagnostic science assessment data using Bayesian networks

NASA Astrophysics Data System (ADS)

Steedle, Jeffrey Thomas

2008-10-01

Diagnostic science assessments seek to draw inferences about student understanding by eliciting evidence about the mental models that underlie students' reasoning about physical systems. Measurement techniques for analyzing data from such assessments embody one of two contrasting assessment programs: learning progressions and facet-based assessments. Learning progressions assume that students have coherent theories that they apply systematically across different problem contexts. In contrast, the facet approach makes no such assumption, so students should not be expected to reason systematically across different problem contexts. A systematic comparison of these two approaches is of great practical value to assessment programs such as the National Assessment of Educational Progress as they seek to incorporate small clusters of related items in their tests for the purpose of measuring depth of understanding. This dissertation describes an investigation comparing learning progression and facet models. Data comprised student responses to small clusters of multiple-choice diagnostic science items focusing on narrow aspects of understanding of Newtonian mechanics. Latent class analysis was employed using Bayesian networks in order to model the relationship between students' science understanding and item responses. Separate models reflecting the assumptions of the learning progression and facet approaches were fit to the data. The technical qualities of inferences about student understanding resulting from the two models were compared in order to determine if either modeling approach was more appropriate. Specifically, models were compared on model-data fit, diagnostic reliability, diagnostic certainty, and predictive accuracy. In addition, the effects of test length were evaluated for both models in order to inform the number of items required to obtain adequately reliable latent class diagnoses. Lastly, changes in student understanding over time were studied with a longitudinal model in order to provide educators and curriculum developers with a sense of how students advance in understanding over the course of instruction. Results indicated that expected student response patterns rarely reflected the assumptions of the learning progression approach. That is, students tended not to systematically apply a coherent set of ideas across different problem contexts. Even those students expected to express scientifically-accurate understanding had substantial probabilities of reporting certain problematic ideas. The learning progression models failed to make as many substantively-meaningful distinctions among students as the facet models. In statistical comparisons, model-data fit was better for the facet model, but the models were quite comparable on all other statistical criteria. Studying the effects of test length revealed that approximately 8 items are needed to obtain adequate diagnostic certainty, but more items are needed to obtain adequate diagnostic reliability. The longitudinal analysis demonstrated that students either advance in their understanding (i.e., switch to the more advanced latent class) over a short period of instruction or stay at the same level. There was no significant relationship between the probability of changing latent classes and time between testing occasions. In all, this study is valuable because it provides evidence informing decisions about modeling and reporting on student understanding, it assesses the quality of measurement available from short clusters of diagnostic multiple-choice items, and it provides educators with knowledge of the paths that student may take as they advance from novice to expert understanding over the course of instruction.

High resolution, MRI-based, segmented, computerized head phantom

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

Zubal, I.G.; Harrell, C.R.; Smith, E.O.

1999-01-01

The authors have created a high-resolution software phantom of the human brain which is applicable to voxel-based radiation transport calculations yielding nuclear medicine simulated images and/or internal dose estimates. A software head phantom was created from 124 transverse MRI images of a healthy normal individual. The transverse T2 slices, recorded in a 256x256 matrix from a GE Signa 2 scanner, have isotropic voxel dimensions of 1.5 mm and were manually segmented by the clinical staff. Each voxel of the phantom contains one of 62 index numbers designating anatomical, neurological, and taxonomical structures. The result is stored as a 256x256x128 bytemore » array. Internal volumes compare favorably to those described in the ICRP Reference Man. The computerized array represents a high resolution model of a typical human brain and serves as a voxel-based anthropomorphic head phantom suitable for computer-based modeling and simulation calculations. It offers an improved realism over previous mathematically described software brain phantoms, and creates a reference standard for comparing results of newly emerging voxel-based computations. Such voxel-based computations lead the way to developing diagnostic and dosimetry calculations which can utilize patient-specific diagnostic images. However, such individualized approaches lack fast, automatic segmentation schemes for routine use; therefore, the high resolution, typical head geometry gives the most realistic patient model currently available.« less

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Damage tolerance modeling and validation of a wireless sensory composite panel for a structural health monitoring system

NASA Astrophysics Data System (ADS)

Talagani, Mohamad R.; Abdi, Frank; Saravanos, Dimitris; Chrysohoidis, Nikos; Nikbin, Kamran; Ragalini, Rose; Rodov, Irena

2013-05-01

The paper proposes the diagnostic and prognostic modeling and test validation of a Wireless Integrated Strain Monitoring and Simulation System (WISMOS). The effort verifies a hardware and web based software tool that is able to evaluate and optimize sensorized aerospace composite structures for the purpose of Structural Health Monitoring (SHM). The tool is an extension of an existing suite of an SHM system, based on a diagnostic-prognostic system (DPS) methodology. The goal of the extended SHM-DPS is to apply multi-scale nonlinear physics-based Progressive Failure analyses to the "as-is" structural configuration to determine residual strength, remaining service life, and future inspection intervals and maintenance procedures. The DPS solution meets the JTI Green Regional Aircraft (GRA) goals towards low weight, durable and reliable commercial aircraft. It will take advantage of the currently developed methodologies within the European Clean sky JTI project WISMOS, with the capability to transmit, store and process strain data from a network of wireless sensors (e.g. strain gages, FBGA) and utilize a DPS-based methodology, based on multi scale progressive failure analysis (MS-PFA), to determine structural health and to advice with respect to condition based inspection and maintenance. As part of the validation of the Diagnostic and prognostic system, Carbon/Epoxy ASTM coupons were fabricated and tested to extract the mechanical properties. Subsequently two composite stiffened panels were manufactured, instrumented and tested under compressive loading: 1) an undamaged stiffened buckling panel; and 2) a damaged stiffened buckling panel including an initial diamond cut. Next numerical Finite element models of the two panels were developed and analyzed under test conditions using Multi-Scale Progressive Failure Analysis (an extension of FEM) to evaluate the damage/fracture evolution process, as well as the identification of contributing failure modes. The comparisons between predictions and test results were within 10% accuracy.

Health economic evaluation of a serum-based blood test for brain tumour diagnosis: exploration of two clinical scenarios.

PubMed

Gray, Ewan; Butler, Holly J; Board, Ruth; Brennan, Paul M; Chalmers, Anthony J; Dawson, Timothy; Goodden, John; Hamilton, Willie; Hegarty, Mark G; James, Allan; Jenkinson, Michael D; Kernick, David; Lekka, Elvira; Livermore, Laurent J; Mills, Samantha J; O'Neill, Kevin; Palmer, David S; Vaqas, Babar; Baker, Matthew J

2018-05-24

To determine the potential costs and health benefits of a serum-based spectroscopic triage tool for brain tumours, which could be developed to reduce diagnostic delays in the current clinical pathway. A model-based health pre-trial economic assessment. Decision tree models were constructed based on simplified diagnostic pathways. Models were populated with parameters identified from rapid reviews of the literature and clinical expert opinion. Explored as a test in both primary and secondary care (neuroimaging) in the UK health service, as well as application to the USA. Calculations based on an initial cohort of 10 000 patients. In primary care, it is estimated that the volume of tests would approach 75 000 per annum. The volume of tests in secondary care is estimated at 53 000 per annum. The primary outcome measure was quality-adjusted life-years (QALY), which were employed to derive incremental cost-effectiveness ratios (ICER) in a cost-effectiveness analysis. Results indicate that using a blood-based spectroscopic test in both scenarios has the potential to be highly cost-effective in a health technology assessment agency decision-making process, as ICERs were well below standard threshold values of £20 000-£30 000 per QALY. This test may be cost-effective in both scenarios with test sensitivities and specificities as low as 80%; however, the price of the test would need to be lower (less than approximately £40). Use of this test as triage tool in primary care has the potential to be both more effective and cost saving for the health service. In secondary care, this test would also be deemed more effective than the current diagnostic pathway. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Augmenting epidemiological models with point-of-care diagnostics data

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

Pullum, Laura L.; Ramanathan, Arvind; Nutaro, James J.

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnosticsmore » data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.« less

Augmenting epidemiological models with point-of-care diagnostics data

DOE PAGES

Pullum, Laura L.; Ramanathan, Arvind; Nutaro, James J.; ...

2016-04-20

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnosticsmore » data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.« less

Rapid diagnostic test for G6PD deficiency in Plasmodium vivax-infected men: a budget impact analysis based in Brazilian Amazon.

PubMed

Peixoto, Henry Maia; Brito, Marcelo Augusto Mota; Romero, Gustavo Adolfo Sierra; Monteiro, Wuelton Marcelo; de Lacerda, Marcus Vinícius Guimarães; de Oliveira, Maria Regina Fernandes

2017-01-01

The aim of this study was to estimate the incremental budget impact (IBI) of a rapid diagnostic test to detect G6PDd in male patients infected with Plasmodium vivax in the Brazilian Amazon, as compared with the routine protocol recommended in Brazil which does not include G6PDd testing. The budget impact analysis was performed from the perspective of the Brazilian health system, in the Brazilian Amazon for the years 2013, 2014 and 2015. The analysis used a decision model to compare two scenarios: the first consisting of the routine recommended in Brazil which does not include prior diagnosis of dG6PD, and the second based on the use of RDT CareStart™ G6PD (CS-G6PD) in all male subjects diagnosed with vivax malaria. The expected implementation of the diagnostic test was 30% in the first year, 70% the second year and 100% in the third year. The analysis identified negative IBIs which were progressively smaller in the 3 years evaluated. The sensitivity analysis showed that the uncertainties associated with the analytical model did not significantly affect the results. A strategy based on the use of CS-G6PD would result in better use of public resources in the Brazilian Amazon. © 2016 John Wiley & Sons Ltd.

Biomarker discovery and development in pediatric critical care medicine

PubMed Central

Kaplan, Jennifer M.; Wong, Hector R.

2010-01-01

Objective To frame the general process of biomarker discovery and development, and to describe a proposal for the development of a multi-biomarker based risk model for pediatric septic shock. Data Source Narrative literature review and author generated data. Main Results Biomarkers can be grouped into four broad classes, based on the intended function: diagnostic, monitoring, surrogate, and stratification. Biomarker discovery and development requires a rigorous process, which is frequently not well followed in the critical care medicine literature. Very few biomarkers have successfully transitioned from the candidate stage to the true biomarker stage. There is great interest in developing diagnostic and stratification biomarkers for sepsis. Procalcitonin is currently the most promising diagnostic biomarker for sepsis. Recent evidence suggests that interleukin-8 can be used to stratify children with septic shock having a high likelihood of survival with standard care. Currently, there is a multi-institutional effort to develop a multi-biomarker based sepsis risk model intended to predict outcome and illness severity for individual children with septic shock. Conclusions Biomarker discovery and development is an important portion of the pediatric critical care medicine translational research agenda. This effort will require collaboration across multiple institutions and investigators. Rigorous conduct of biomarker-focused research holds the promise of transforming our ability to care for individual patients and our ability to conduct clinical trials in a more effective manner. PMID:20473243

Mechanisms for Induction of Pulmonary Capillary Hemorrhage by Diagnostic Ultrasound: Review and Consideration of Acoustical Radiation Surface Pressure.

PubMed

Miller, Douglas L

2016-12-01

Diagnostic ultrasound can induce pulmonary capillary hemorrhage (PCH) in rats and other mammals. This phenomenon represents the only clearly demonstrated biological effect of (non-contrast enhanced) diagnostic ultrasound and thus presents a uniquely important safety issue. However, the physical mechanism responsible for PCH remains uncertain more than 25 y after its discovery. Experimental research has indicated that neither heating nor acoustic cavitation, the predominant mechanisms for bioeffects of ultrasound, is responsible for PCH. Furthermore, proposed theoretical mechanisms based on gas-body activation, on alveolar resonance and on impulsive generation of liquid droplets all appear unlikely to be responsible for PCH, owing to unrealistic model assumptions. Here, a simple model based on the acoustical radiation surface pressure (ARSP) at a tissue-air interface is hypothesized as the mechanism for PCH. The ARSP model seems to explain some features of PCH, including the approximate frequency independence of PCH thresholds and the dependence of thresholds on biological factors. However, ARSP evaluated for experimental threshold conditions appear to be too weak to fully account for stress failure of pulmonary capillaries, gauging by known stresses for injurious physiologic conditions. Furthermore, consideration of bulk properties of lung tissue suggests substantial transmission of ultrasound through the pleura, with reduced ARSP and potential involvement of additional mechanisms within the pulmonary interior. Although these recent findings advance our knowledge, only a full understanding of PCH mechanisms will allow development of science-based safety assurance for pulmonary ultrasound. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite

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

Agarwal, Vivek; Lybeck, Nancy J.; Pham, Binh

Research and development efforts are required to address aging and reliability concerns of the existing fleet of nuclear power plants. As most plants continue to operate beyond the license life (i.e., towards 60 or 80 years), plant components are more likely to incur age-related degradation mechanisms. To assess and manage the health of aging plant assets across the nuclear industry, the Electric Power Research Institute has developed a web-based Fleet-Wide Prognostic and Health Management (FW-PHM) Suite for diagnosis and prognosis. FW-PHM is a set of web-based diagnostic and prognostic tools and databases, comprised of the Diagnostic Advisor, the Asset Faultmore » Signature Database, the Remaining Useful Life Advisor, and the Remaining Useful Life Database, that serves as an integrated health monitoring architecture. The main focus of this paper is the implementation of prognostic models for generator step-up transformers in the FW-PHM Suite. One prognostic model discussed is based on the functional relationship between degree of polymerization, (the most commonly used metrics to assess the health of the winding insulation in a transformer) and furfural concentration in the insulating oil. The other model is based on thermal-induced degradation of the transformer insulation. By utilizing transformer loading information, established thermal models are used to estimate the hot spot temperature inside the transformer winding. Both models are implemented in the Remaining Useful Life Database of the FW-PHM Suite. The Remaining Useful Life Advisor utilizes the implemented prognostic models to estimate the remaining useful life of the paper winding insulation in the transformer based on actual oil testing and operational data.« less

A standard test case suite for two-dimensional linear transport on the sphere: results from a collection of state-of-the-art schemes

NASA Astrophysics Data System (ADS)

Lauritzen, P. H.; Ullrich, P. A.; Jablonowski, C.; Bosler, P. A.; Calhoun, D.; Conley, A. J.; Enomoto, T.; Dong, L.; Dubey, S.; Guba, O.; Hansen, A. B.; Kaas, E.; Kent, J.; Lamarque, J.-F.; Prather, M. J.; Reinert, D.; Shashkin, V. V.; Skamarock, W. C.; Sørensen, B.; Taylor, M. A.; Tolstykh, M. A.

2013-09-01

Recently, a standard test case suite for 2-D linear transport on the sphere was proposed to assess important aspects of accuracy in geophysical fluid dynamics with a "minimal" set of idealized model configurations/runs/diagnostics. Here we present results from 19 state-of-the-art transport scheme formulations based on finite-difference/finite-volume methods as well as emerging (in the context of atmospheric/oceanographic sciences) Galerkin methods. Discretization grids range from traditional regular latitude-longitude grids to more isotropic domain discretizations such as icosahedral and cubed-sphere tessellations of the sphere. The schemes are evaluated using a wide range of diagnostics in idealized flow environments. Accuracy is assessed in single- and two-tracer configurations using conventional error norms as well as novel diagnostics designed for climate and climate-chemistry applications. In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent, The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data-sets are made available to facilitate the process of model evaluation and scheme intercomparison.

A standard test case suite for two-dimensional linear transport on the sphere: results from a collection of state-of-the-art schemes

NASA Astrophysics Data System (ADS)

Lauritzen, P. H.; Ullrich, P. A.; Jablonowski, C.; Bosler, P. A.; Calhoun, D.; Conley, A. J.; Enomoto, T.; Dong, L.; Dubey, S.; Guba, O.; Hansen, A. B.; Kaas, E.; Kent, J.; Lamarque, J.-F.; Prather, M. J.; Reinert, D.; Shashkin, V. V.; Skamarock, W. C.; Sørensen, B.; Taylor, M. A.; Tolstykh, M. A.

2014-01-01

Recently, a standard test case suite for 2-D linear transport on the sphere was proposed to assess important aspects of accuracy in geophysical fluid dynamics with a "minimal" set of idealized model configurations/runs/diagnostics. Here we present results from 19 state-of-the-art transport scheme formulations based on finite-difference/finite-volume methods as well as emerging (in the context of atmospheric/oceanographic sciences) Galerkin methods. Discretization grids range from traditional regular latitude-longitude grids to more isotropic domain discretizations such as icosahedral and cubed-sphere tessellations of the sphere. The schemes are evaluated using a wide range of diagnostics in idealized flow environments. Accuracy is assessed in single- and two-tracer configurations using conventional error norms as well as novel diagnostics designed for climate and climate-chemistry applications. In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent. The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data sets are made available to facilitate the process of model evaluation and scheme intercomparison.

Model-Based Diagnosis and Prognosis of a Water Recycling System

NASA Technical Reports Server (NTRS)

Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

2013-01-01

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

ADAM: An Accident Diagnostic,Analysis and Management System - Applications to Severe Accident Simulation and Management

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

Zavisca, M.J.; Khatib-Rahbar, M.; Esmaili, H.

2002-07-01

The Accident Diagnostic, Analysis and Management (ADAM) computer code has been developed as a tool for on-line applications to accident diagnostics, simulation, management and training. ADAM's severe accident simulation capabilities incorporate a balance of mechanistic, phenomenologically based models with simple parametric approaches for elements including (but not limited to) thermal hydraulics; heat transfer; fuel heatup, meltdown, and relocation; fission product release and transport; combustible gas generation and combustion; and core-concrete interaction. The overall model is defined by a relatively coarse spatial nodalization of the reactor coolant and containment systems and is advanced explicitly in time. The result is to enablemore » much faster than real time (i.e., 100 to 1000 times faster than real time on a personal computer) applications to on-line investigations and/or accident management training. Other features of the simulation module include provision for activation of water injection, including the Engineered Safety Features, as well as other mechanisms for the assessment of accident management and recovery strategies and the evaluation of PSA success criteria. The accident diagnostics module of ADAM uses on-line access to selected plant parameters (as measured by plant sensors) to compute the thermodynamic state of the plant, and to predict various margins to safety (e.g., times to pressure vessel saturation and steam generator dryout). Rule-based logic is employed to classify the measured data as belonging to one of a number of likely scenarios based on symptoms, and a number of 'alarms' are generated to signal the state of the reactor and containment. This paper will address the features and limitations of ADAM with particular focus on accident simulation and management. (authors)« less

Antibody-controlled actuation of DNA-based molecular circuits.

PubMed

Engelen, Wouter; Meijer, Lenny H H; Somers, Bram; de Greef, Tom F A; Merkx, Maarten

2017-02-17

DNA-based molecular circuits allow autonomous signal processing, but their actuation has relied mostly on RNA/DNA-based inputs, limiting their application in synthetic biology, biomedicine and molecular diagnostics. Here we introduce a generic method to translate the presence of an antibody into a unique DNA strand, enabling the use of antibodies as specific inputs for DNA-based molecular computing. Our approach, antibody-templated strand exchange (ATSE), uses the characteristic bivalent architecture of antibodies to promote DNA-strand exchange reactions both thermodynamically and kinetically. Detailed characterization of the ATSE reaction allowed the establishment of a comprehensive model that describes the kinetics and thermodynamics of ATSE as a function of toehold length, antibody-epitope affinity and concentration. ATSE enables the introduction of complex signal processing in antibody-based diagnostics, as demonstrated here by constructing molecular circuits for multiplex antibody detection, integration of multiple antibody inputs using logic gates and actuation of enzymes and DNAzymes for signal amplification.

Antibody-controlled actuation of DNA-based molecular circuits

NASA Astrophysics Data System (ADS)

Engelen, Wouter; Meijer, Lenny H. H.; Somers, Bram; de Greef, Tom F. A.; Merkx, Maarten

2017-02-01

DNA-based molecular circuits allow autonomous signal processing, but their actuation has relied mostly on RNA/DNA-based inputs, limiting their application in synthetic biology, biomedicine and molecular diagnostics. Here we introduce a generic method to translate the presence of an antibody into a unique DNA strand, enabling the use of antibodies as specific inputs for DNA-based molecular computing. Our approach, antibody-templated strand exchange (ATSE), uses the characteristic bivalent architecture of antibodies to promote DNA-strand exchange reactions both thermodynamically and kinetically. Detailed characterization of the ATSE reaction allowed the establishment of a comprehensive model that describes the kinetics and thermodynamics of ATSE as a function of toehold length, antibody-epitope affinity and concentration. ATSE enables the introduction of complex signal processing in antibody-based diagnostics, as demonstrated here by constructing molecular circuits for multiplex antibody detection, integration of multiple antibody inputs using logic gates and actuation of enzymes and DNAzymes for signal amplification.

Does size really matter? A multisite study assessing the latent structure of the proposed ICD-11 and DSM-5 diagnostic criteria for PTSD

PubMed Central

Hansen, Maj; Hyland, Philip; Karstoft, Karen-Inge; Vaegter, Henrik B.; Bramsen, Rikke H.; Nielsen, Anni B. S.; Armour, Cherie; Andersen, Søren B.; Høybye, Mette Terp; Larsen, Simone Kongshøj; Andersen, Tonny E.

2017-01-01

ABSTRACT Background: Researchers and clinicians within the field of trauma have to choose between different diagnostic descriptions of posttraumatic stress disorder (PTSD) in the DSM-5 and the proposed ICD-11. Several studies support different competing models of the PTSD structure according to both diagnostic systems; however, findings show that the choice of diagnostic systems can affect the estimated prevalence rates. Objectives: The present study aimed to investigate the potential impact of using a large (i.e. the DSM-5) compared to a small (i.e. the ICD-11) diagnostic description of PTSD. In other words, does the size of PTSD really matter? Methods: The aim was investigated by examining differences in diagnostic rates between the two diagnostic systems and independently examining the model fit of the competing DSM-5 and ICD-11 models of PTSD across three trauma samples: university students (N = 4213), chronic pain patients (N = 573), and military personnel (N = 118). Results: Diagnostic rates of PTSD were significantly lower according to the proposed ICD-11 criteria in the university sample, but no significant differences were found for chronic pain patients and military personnel. The proposed ICD-11 three-factor model provided the best fit of the tested ICD-11 models across all samples, whereas the DSM-5 seven-factor Hybrid model provided the best fit in the university and pain samples, and the DSM-5 six-factor Anhedonia model provided the best fit in the military sample of the tested DSM-5 models. Conclusions: The advantages and disadvantages of using a broad or narrow set of symptoms for PTSD can be debated, however, this study demonstrated that choice of diagnostic system may influence the estimated PTSD rates both qualitatively and quantitatively. In the current described diagnostic criteria only the ICD-11 model can reflect the configuration of symptoms satisfactorily. Thus, size does matter when assessing PTSD. PMID:29201287

Does size really matter? A multisite study assessing the latent structure of the proposed ICD-11 and DSM-5 diagnostic criteria for PTSD.

PubMed

Hansen, Maj; Hyland, Philip; Karstoft, Karen-Inge; Vaegter, Henrik B; Bramsen, Rikke H; Nielsen, Anni B S; Armour, Cherie; Andersen, Søren B; Høybye, Mette Terp; Larsen, Simone Kongshøj; Andersen, Tonny E

2017-01-01

Background : Researchers and clinicians within the field of trauma have to choose between different diagnostic descriptions of posttraumatic stress disorder (PTSD) in the DSM-5 and the proposed ICD-11. Several studies support different competing models of the PTSD structure according to both diagnostic systems; however, findings show that the choice of diagnostic systems can affect the estimated prevalence rates. Objectives : The present study aimed to investigate the potential impact of using a large (i.e. the DSM-5) compared to a small (i.e. the ICD-11) diagnostic description of PTSD. In other words, does the size of PTSD really matter? Methods: The aim was investigated by examining differences in diagnostic rates between the two diagnostic systems and independently examining the model fit of the competing DSM-5 and ICD-11 models of PTSD across three trauma samples: university students ( N  = 4213), chronic pain patients ( N  = 573), and military personnel ( N  = 118). Results : Diagnostic rates of PTSD were significantly lower according to the proposed ICD-11 criteria in the university sample, but no significant differences were found for chronic pain patients and military personnel. The proposed ICD-11 three-factor model provided the best fit of the tested ICD-11 models across all samples, whereas the DSM-5 seven-factor Hybrid model provided the best fit in the university and pain samples, and the DSM-5 six-factor Anhedonia model provided the best fit in the military sample of the tested DSM-5 models. Conclusions : The advantages and disadvantages of using a broad or narrow set of symptoms for PTSD can be debated, however, this study demonstrated that choice of diagnostic system may influence the estimated PTSD rates both qualitatively and quantitatively. In the current described diagnostic criteria only the ICD-11 model can reflect the configuration of symptoms satisfactorily. Thus, size does matter when assessing PTSD.

Parent Ratings of ADHD Symptoms: Generalized Partial Credit Model Analysis of Differential Item Functioning across Gender

ERIC Educational Resources Information Center

Gomez, Rapson

2012-01-01

Objective: Generalized partial credit model, which is based on item response theory (IRT), was used to test differential item functioning (DIF) for the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed.), inattention (IA), and hyperactivity/impulsivity (HI) symptoms across boys and girls. Method: To accomplish this, parents completed…

"DSM IV," "DSM-5," and the Five-Factor Model: the Diagnosis of Personality Disorder with Intellectual and Developmental Disabilities

ERIC Educational Resources Information Center

Lindsay, William R.; Steptoe, Lesley; McVicker, Ronnie; Haut, Fabian; Robertson, Colette

2018-01-01

In "DSM-5" there has been a move to dimensional personality disorder (PD) diagnosis, incorporating personality theory in the form of the five-factor model (FFM). It proposes an alternative assessment system based on diagnostic indicators and the FFM, while retaining "DSM-IV" categorical criteria. Four individuals with…

Wave processes in the human cardiovascular system: The measuring complex, computing models, and diagnostic analysis

NASA Astrophysics Data System (ADS)

Ganiev, R. F.; Reviznikov, D. L.; Rogoza, A. N.; Slastushenskiy, Yu. V.; Ukrainskiy, L. E.

2017-03-01

A description of a complex approach to investigation of nonlinear wave processes in the human cardiovascular system based on a combination of high-precision methods of measuring a pulse wave, mathematical methods of processing the empirical data, and methods of direct numerical modeling of hemodynamic processes in an arterial tree is given.

Testing Structural Models of DSM-IV Symptoms of Common Forms of Child and Adolescent Psychopathology

ERIC Educational Resources Information Center

Lahey, Benjamin B.; Rathouz, Paul J.; Van Hulle, Carol; Urbano, Richard C.; Krueger, Robert F.; Applegate, Brooks; Garriock, Holly A.; Chapman, Derek A.; Waldman, Irwin D.

2008-01-01

Confirmatory factor analyses were conducted of "Diagnostic and Statistical Manual of Mental Disorders", Fourth Edition (DSM-IV) symptoms of common mental disorders derived from structured interviews of a representative sample of 4,049 twin children and adolescents and their adult caretakers. A dimensional model based on the assignment of symptoms…

Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework

NASA Astrophysics Data System (ADS)

Solazzo, Efisio; Hogrefe, Christian; Colette, Augustin; Garcia-Vivanco, Marta; Galmarini, Stefano

2017-09-01

The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America. The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition. To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance) of the base case and of the sensitivity runs are analysed in conjunction with timescale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration. The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study), allowing the detection of the timescale and the fields that the two models are most sensitive to. The representation of planetary boundary layer (PBL) dynamics is pivotal to both models. In particular, (i) the fluctuations slower than ˜ 1.5 days account for 70-85 % of the mean square error of the full (undecomposed) ozone time series; (ii) a recursive, systematic error with daily periodicity is detected, responsible for 10-20 % of the quadratic total error; (iii) errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network-average ozone observations in summer in both Europe and North America); (iv) the CMAQ ozone error has a weak/negligible dependence on the errors in NO2, while the error in NO2 significantly impacts the ozone error produced by Chimere; (v) the response of the models to variations of anthropogenic emissions and boundary conditions show a pronounced spatial heterogeneity, while the seasonal variability of the response is found to be less marked. Only during the winter season does the zeroing of boundary values for North America produce a spatially uniform deterioration of the model accuracy across the majority of the continent.

Ontology-based tools to expedite predictive model construction.

PubMed

Haug, Peter; Holmen, John; Wu, Xinzi; Mynam, Kumar; Ebert, Matthew; Ferraro, Jeffrey

2014-01-01

Large amounts of medical data are collected electronically during the course of caring for patients using modern medical information systems. This data presents an opportunity to develop clinically useful tools through data mining and observational research studies. However, the work necessary to make sense of this data and to integrate it into a research initiative can require substantial effort from medical experts as well as from experts in medical terminology, data extraction, and data analysis. This slows the process of medical research. To reduce the effort required for the construction of computable, diagnostic predictive models, we have developed a system that hybridizes a medical ontology with a large clinical data warehouse. Here we describe components of this system designed to automate the development of preliminary diagnostic models and to provide visual clues that can assist the researcher in planning for further analysis of the data behind these models.

A fully-integrated aptamer-based affinity assay platform for monitoring astronaut health in space.

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

Yang, Xianbin; Durland, Ross H.; Hecht, Ariel H.

2010-07-01

Here we demonstrate the suitability of robust nucleic acid affinity reagents in an integrated point-of-care diagnostic platform for monitoring proteomic biomarkers indicative of astronaut health in spaceflight applications. A model thioaptamer targeting nuclear factor-kappa B (NF-{kappa}B) is evaluated in an on-chip electrophoretic gel-shift assay for human serum. Key steps of (i) mixing sample with the aptamer, (ii) buffer exchange, and (iii) preconcentration of sample were successfully integrated upstream of fluorescence-based detection. Challenges due to (i) nonspecific interactions with serum, and (ii) preconcentration at a nanoporous membrane are discussed and successfully resolved to yield a robust, rapid, and fully-integrated diagnostic system.

Development of optical diagnostics for performance evaluation of arcjet thrusters

NASA Technical Reports Server (NTRS)

Cappelli, Mark A.

1995-01-01

Laser and optical emission-based measurements have been developed and implemented for use on low-power hydrogen arcjet thrusters and xenon-propelled electric thrusters. In the case of low power hydrogen arcjets, these laser induce fluorescence measurements constitute the first complete set of data that characterize the velocity and temperature field of such a device. The research performed under the auspices of this NASA grant includes laser-based measurements of atomic hydrogen velocity and translational temperature, ultraviolet absorption measurements of ground state atomic hydrogen, Raman scattering measurements of the electronic ground state of molecular hydrogen, and optical emission based measurements of electronically excited atomic hydrogen, electron number density, and electron temperature. In addition, we have developed a collisional-radiative model of atomic hydrogen for use in conjunction with magnetohydrodynamic models to predict the plasma radiative spectrum, and near-electrode plasma models to better understand current transfer from the electrodes to the plasma. In the final year of the grant, a new program aimed at developing diagnostics for xenon plasma thrusters was initiated, and results on the use of diode lasers for interrogating Hall accelerator plasmas has been presented at recent conferences.

Diagnosing Diagnostic Models: From Von Neumann's Elephant to Model Equivalencies and Network Psychometrics

ERIC Educational Resources Information Center

von Davier, Matthias

2018-01-01

This article critically reviews how diagnostic models have been conceptualized and how they compare to other approaches used in educational measurement. In particular, certain assumptions that have been taken for granted and used as defining characteristics of diagnostic models are reviewed and it is questioned whether these assumptions are the…

An International Comparison Using a Diagnostic Testing Model: Turkish Students' Profile of Mathematical Skills on TIMSS-R

ERIC Educational Resources Information Center

Dogan, Enis; Tatsuoka, Kikumi

2008-01-01

This study illustrates how a diagnostic testing model can be used to make detailed comparisons between student populations participating in international assessments. The performance of Turkish students on the TIMSS-R mathematics test was reanalyzed with a diagnostic testing model called the Rule Space Model. First, mathematical and cognitive…

Development of an EMC3-EIRENE Synthetic Imaging Diagnostic

NASA Astrophysics Data System (ADS)

Meyer, William; Allen, Steve; Samuell, Cameron; Lore, Jeremy

2017-10-01

2D and 3D flow measurements are critical for validating numerical codes such as EMC3-EIRENE. Toroidal symmetry assumptions preclude tomographic reconstruction of 3D flows from single camera views. In addition, the resolution of the grids utilized in numerical code models can easily surpass the resolution of physical camera diagnostic geometries. For these reasons we have developed a Synthetic Imaging Diagnostic capability for forward projection comparisons of EMC3-EIRENE model solutions with the line integrated images from the Doppler Coherence Imaging diagnostic on DIII-D. The forward projection matrix is 2.8 Mpixel by 6.4 Mcells for the non-axisymmetric case we present. For flow comparisons, both simple line integral, and field aligned component matrices must be calculated. The calculation of these matrices is a massive embarrassingly parallel problem and performed with a custom dispatcher that allows processing platforms to join mid-problem as they become available, or drop out if resources are needed for higher priority tasks. The matrices are handled using standard sparse matrix techniques. Prepared by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. DOE, Office of Science, Office of Fusion Energy Sciences. LLNL-ABS-734800.

The Association between Parameters of Malnutrition and Diagnostic Measures of Sarcopenia in Geriatric Outpatients

PubMed Central

Reijnierse, Esmee M.; Trappenburg, Marijke C.; Leter, Morena J.; Blauw, Gerard Jan; de van der Schueren, Marian A. E.; Meskers, Carel G. M.; Maier, Andrea B.

2015-01-01

Objectives Diagnostic criteria for sarcopenia include measures of muscle mass, muscle strength and physical performance. Consensus on the definition of sarcopenia has not been reached yet. To improve insight into the most clinically valid definition of sarcopenia, this study aimed to compare the association between parameters of malnutrition, as a risk factor in sarcopenia, and diagnostic measures of sarcopenia in geriatric outpatients. Material and Methods This study is based on data from a cross-sectional study conducted in a geriatric outpatient clinic including 185 geriatric outpatients (mean age 82 years). Parameters of malnutrition included risk of malnutrition (assessed by the Short Nutritional Assessment Questionnaire), loss of appetite, unintentional weight loss and underweight (body mass index <22 kg/m2). Diagnostic measures of sarcopenia included relative muscle mass (lean mass and appendicular lean mass [ALM] as percentages), absolute muscle mass (total lean mass and ALM/height2), handgrip strength and walking speed. All diagnostic measures of sarcopenia were standardized. Associations between parameters of malnutrition (independent variables) and diagnostic measures of sarcopenia (dependent variables) were analysed using multivariate linear regression models adjusted for age, body mass, fat mass and height in separate models. Results None of the parameters of malnutrition was consistently associated with diagnostic measures of sarcopenia. The strongest associations were found for both relative and absolute muscle mass; less stronger associations were found for muscle strength and physical performance. Underweight (p = <0.001) and unintentional weight loss (p = 0.031) were most strongly associated with higher lean mass percentage after adjusting for age. Loss of appetite (p = 0.003) and underweight (p = 0.021) were most strongly associated with lower total lean mass after adjusting for age and fat mass. Conclusion Parameters of malnutrition relate differently to diagnostic measures of sarcopenia in geriatric outpatients. The association between parameters of malnutrition and diagnostic measures of sarcopenia was strongest for both relative and absolute muscle mass, while less strong associations were found with muscle strength and physical performance. PMID:26284368

Circulating Tumor Cells: What Is in It for the Patient? A Vision towards the Future

PubMed Central

van de Stolpe, Anja; den Toonder, Jaap M. J.

2014-01-01

Knowledge on cellular signal transduction pathways as drivers of cancer growth and metastasis has fuelled development of “targeted therapy” which “targets” aberrant oncogenic signal transduction pathways. These drugs require nearly invariably companion diagnostic tests to identify the tumor-driving pathway and the cause of the abnormal pathway activity in a tumor sample, both for therapy response prediction as well as for monitoring of therapy response and emerging secondary drug resistance. Obtaining sufficient tumor material for this analysis in the metastatic setting is a challenge, and circulating tumor cells (CTCs) may provide an attractive alternative to biopsy on the premise that they can be captured from blood and the companion diagnostic test results are correctly interpreted. We discuss novel companion diagnostic directions, including the challenges, to identify the tumor driving pathway in CTCs, which in combination with a digital pathology platform and algorithms to quantitatively interpret complex CTC diagnostic results may enable optimized therapy response prediction and monitoring. In contrast to CTC-based companion diagnostics, CTC enumeration is envisioned to be largely replaced by cell free tumor DNA measurements in blood for therapy response and recurrence monitoring. The recent emergence of novel in vitro human model systems in the form of cancer-on-a-chip may enable elucidation of some of the so far elusive characteristics of CTCs, and is expected to contribute to more efficient CTC capture and CTC-based diagnostics. PMID:24879438

Diagnosis of periprosthetic joint infection in Medicare patients: multicriteria decision analysis.

PubMed

Diaz-Ledezma, Claudio; Lichstein, Paul M; Dolan, James G; Parvizi, Javad

2014-11-01

In the setting of finite healthcare resources, developing cost-efficient strategies for periprosthetic joint infection (PJI) diagnosis is paramount. The current levels of knowledge allow for PJI diagnostic recommendations based on scientific evidence but do not consider the benefits, opportunities, costs, and risks of the different diagnostic alternatives. We determined the best diagnostic strategy for knee and hip PJI in the ambulatory setting for Medicare patients, utilizing benefits, opportunities, costs, and risks evaluation through multicriteria decision analysis (MCDA). The PJI diagnostic definition supported by the Musculoskeletal Infection Society was employed for the MCDA. Using a preclinical model, we evaluated three diagnostic strategies that can be conducted in a Medicare patient seen in the outpatient clinical setting complaining of a painful TKA or THA. Strategies were (1) screening with serum markers (erythrocyte sedimentation rate [ESR]/C-reactive protein [CRP]) followed by arthrocentesis in positive cases, (2) immediate arthrocentesis, and (3) serum markers requested simultaneously with arthrocentesis. MCDA was conducted through the analytic hierarchy process, comparing the diagnostic strategies in terms of benefits, opportunities, costs, and risks. Strategy 1 was the best alternative to diagnose knee PJI among Medicare patients (normalized value: 0.490), followed by Strategy 3 (normalized value: 0.403) and then Strategy 2 (normalized value: 0.106). The same ranking of alternatives was observed for the hip PJI model (normalized value: 0.487, 0.405, and 0.107, respectively). The sensitivity analysis found this sequence to be robust with respect to benefits, opportunities, and risks. However, if during the decision-making process, cost savings was given a priority of higher than 54%, the ranking for the preferred diagnostic strategy changed. After considering the benefits, opportunities, costs, and risks of the different available alternatives, our preclinical model supports the American Academy of Orthopaedic Surgeons recommendations regarding the use of serum markers (ESR/CRP) before arthrocentesis as the best diagnostic strategy for PJI among Medicare patients. Level II, economic and decision analysis. See Instructions to Authors for a complete description of levels of evidence.

Evidentiary Reasoning in Diagnostic Classification Models

ERIC Educational Resources Information Center

Levy, Roy

2009-01-01

In "Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art," Rupp and Templin (2008) undertake the ambitious task of providing a thorough portrait of the current state of diagnostic classification models (DCM). In this commentary, the author applauds Rupp and Templin for their…

Stochastic modelling of the monthly average maximum and minimum temperature patterns in India 1981-2015

NASA Astrophysics Data System (ADS)

Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.

2018-04-01

The paper investigates the stochastic modelling and forecasting of monthly average maximum and minimum temperature patterns through suitable seasonal auto regressive integrated moving average (SARIMA) model for the period 1981-2015 in India. The variations and distributions of monthly maximum and minimum temperatures are analyzed through Box plots and cumulative distribution functions. The time series plot indicates that the maximum temperature series contain sharp peaks in almost all the years, while it is not true for the minimum temperature series, so both the series are modelled separately. The possible SARIMA model has been chosen based on observing autocorrelation function (ACF), partial autocorrelation function (PACF), and inverse autocorrelation function (IACF) of the logarithmic transformed temperature series. The SARIMA (1, 0, 0) × (0, 1, 1)12 model is selected for monthly average maximum and minimum temperature series based on minimum Bayesian information criteria. The model parameters are obtained using maximum-likelihood method with the help of standard error of residuals. The adequacy of the selected model is determined using correlation diagnostic checking through ACF, PACF, IACF, and p values of Ljung-Box test statistic of residuals and using normal diagnostic checking through the kernel and normal density curves of histogram and Q-Q plot. Finally, the forecasting of monthly maximum and minimum temperature patterns of India for the next 3 years has been noticed with the help of selected model.

Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling

NASA Technical Reports Server (NTRS)

Gupta, Hoshin V.; Kling, Harald; Yilmaz, Koray K.; Martinez-Baquero, Guillermo F.

2009-01-01

The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification.

A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis.

PubMed

Ji, Jun; Ling, Xuefeng B; Zhao, Yingzhen; Hu, Zhongkai; Zheng, Xiaolin; Xu, Zhening; Wen, Qiaojun; Kastenberg, Zachary J; Li, Ping; Abdullah, Fizan; Brandt, Mary L; Ehrenkranz, Richard A; Harris, Mary Catherine; Lee, Timothy C; Simpson, B Joyce; Bowers, Corinna; Moss, R Lawrence; Sylvester, Karl G

2014-01-01

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

Functional reasoning in diagnostic problem solving

NASA Technical Reports Server (NTRS)

Sticklen, Jon; Bond, W. E.; Stclair, D. C.

1988-01-01

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field.

Nanostructured sensors for biomedical applications--a current perspective.

PubMed

Krishnamoorthy, Sivashankar

2015-08-01

Nanostructured sensors have unique capabilities that can be tailored to advantage in advancing the diagnosis, monitoring and cure of several diseases and health conditions. This report aims at providing a current perspective on, (a) the emerging clinical needs that defines the challenges to be addressed by nanostructured sensors, with specific emphasis on early stage diagnosis, drug-diagnostic combinations, and predictive models to design therapy, (b) the emerging industry trends in in vitro diagnostics, mobile health care, high-throughput molecular and cell-based diagnostic platforms, and (c) recent instances of nanostructured biosensors, including promising sensing concepts that can be enhanced using nanostructures that carry high promise towards catering to the emerging clinical needs, as well as the market/industry trends. Copyright © 2014 Elsevier Ltd. All rights reserved.

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution

PubMed Central

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-01-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images. PMID:29062159

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.

PubMed

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-03-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.

Embedded Professional Development and Classroom-Based Early Reading Intervention: Early Diagnostic Reading Intervention through Coaching

ERIC Educational Resources Information Center

Amendum, Steven J.

2014-01-01

The purpose of the current mixed-methods study was to investigate a model of professional development and classroom-based early reading intervention implemented by the 1st-grade teaching team in a large urban/suburban school district in the southeastern United States. The intervention provided teachers with ongoing embedded professional…

Space Laboratory on a Table Top: A Next Generative ECLSS design and diagnostic tool

NASA Technical Reports Server (NTRS)

Ramachandran, N.

2005-01-01

This paper describes the development plan for a comprehensive research and diagnostic tool for aspects of advanced life support systems in space-based laboratories. Specifically it aims to build a high fidelity tabletop model that can be used for the purpose of risk mitigation, failure mode analysis, contamination tracking, and testing reliability. We envision a comprehensive approach involving experimental work coupled with numerical simulation to develop this diagnostic tool. It envisions a 10% scale transparent model of a space platform such as the International Space Station that operates with water or a specific matched index of refraction liquid as the working fluid. This allows the scaling of a 10 ft x 10 ft x 10 ft room with air flow to 1 ft x 1 ft x 1 ft tabletop model with water/liquid flow. Dynamic similitude for this length scale dictates model velocities to be 67% of full-scale and thereby the time scale of the model to represent 15% of the full- scale system; meaning identical processes in the model are completed in 15% of the full- scale-time. The use of an index matching fluid (fluid that matches the refractive index of cast acrylic, the model material) allows making the entire model (with complex internal geometry) transparent and hence conducive to non-intrusive optical diagnostics. So using such a system one can test environment control parameters such as core flows (axial flows), cross flows (from registers and diffusers), potential problem areas such as flow short circuits, inadequate oxygen content, build up of other gases beyond desirable levels, test mixing processes within the system at local nodes or compartments and assess the overall system performance. The system allows quantitative measurements of contaminants introduced in the system and allows testing and optimizing the tracking process and removal of contaminants. The envisaged system will be modular and hence flexible for quick configuration change and subsequent testing. The data and inferences from the tests will allow for improvements in the development and design of next generation life support systems and configurations. Preliminary experimental and modeling work in this area will be presented. This involves testing of a single inlet-exit model with detailed 3-D flow visualization and quantitative diagnostics and computational modeling of the system.

Adding Four- Dimensional Data Assimilation (aka grid ...

EPA Pesticide Factsheets

Adding four-dimensional data assimilation (a.k.a. grid nudging) to MPAS.The U.S. Environmental Protection Agency is investigating the use of MPAS as the meteorological driver for its next-generation air quality model. To function as such, MPAS needs to operate in a diagnostic mode in much the same manner as the current meteorological driver, the Weather Research and Forecasting (WRF) model. The WRF operates in diagnostic mode using Four-Dimensional Data Assimilation, also known as "grid nudging". MPAS version 4.0 has been modified with the addition of an FDDA routine to the standard physics drivers to nudge the state variables for wind, temperature and water vapor towards MPAS initialization fields defined at 6-hour intervals from GFS-derived data. The results to be shown demonstrate the ability to constrain MPAS simulations to known historical conditions and thus provide the U.S. EPA with a practical meteorological driver for global-scale air quality simulations. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use bo

Diagnostic reasoning: where we've been, where we're going.

PubMed

Monteiro, Sandra M; Norman, Geoffrey

2013-01-01

Recently, clinical diagnostic reasoning has been characterized by "dual processing" models, which postulate a fast, unconscious (System 1) component and a slow, logical, analytical (System 2) component. However, there are a number of variants of this basic model, which may lead to conflicting claims. This paper critically reviews current theories and evidence about the nature of clinical diagnostic reasoning. We begin by briefly discussing the history of research in clinical reasoning. We then focus more specifically on the evidence to support dual-processing models. We conclude by identifying knowledge gaps about clinical reasoning and provide suggestions for future research. In contrast to work on analytical and nonanalytical knowledge as a basis for reasoning, these theories focus on the thinking process, not the nature of the knowledge retrieved. Ironically, this appears to be a revival of an outdated concept. Rather than defining diagnostic performance by problem-solving skills, it is now being defined by processing strategy. The version of dual processing that has received most attention in the literature in medical diagnosis might be labeled a "default/interventionist" model,(17) which suggests that a default system of cognitive processes (System 1) is responsible for cognitive biases that lead to diagnostic errors and that System 2 intervenes to correct these errors. Consequently, from this model, the best strategy for reducing errors is to make students aware of the biases and to encourage them to rely more on System 2. However, an accumulation of evidence suggests that (a) strategies directed at increasing analytical (System 2) processing, by slowing down, reducing distractions, paying conscious attention, and (b) strategies directed at making students aware of the effect of cognitive biases, have no impact on error rates. Conversely, strategies based on increasing application of relevant knowledge appear to have some success and are consistent with basic research on concept formation.

Can and should value-based pricing be applied to molecular diagnostics?

PubMed

Garau, Martina; Towse, Adrian; Garrison, Louis; Housman, Laura; Ossa, Diego

2013-01-01

Current pricing and reimbursement systems for diagnostics are not efficient. Prices for diagnostics are often driven by administrative practices and expected production cost. The purpose of the paper is to discuss how a value-based pricing framework being used to ensure efficient use and price of medicines could also be applied to diagnostics. Diagnostics not only facilitates health gain and cost savings, but also information to guide patients' decisions on interventions and their future 'behaviors'. For value assessment processes we recommend a two-part approach. Companion diagnostics introduced at the launch of the drug should be assessed through new drug assessment processes considering a broad range of value elements and a balanced analysis of diagnostic impacts. A separate diagnostic-dedicated committee using value-based pricing principles should review other diagnostics lying outside the companion diagnostics-and-drug 'at-launch' situation.

Examining the dimensional structure models of secondary traumatic stress based on DSM-5 symptoms.

PubMed

Mordeno, Imelu G; Go, Geraldine P; Yangson-Serondo, April

2017-02-01

Latent factor structure of Secondary Traumatic Stress (STS) has been examined using Diagnostic Statistic Manual-IV (DSM-IV)'s Posttraumatic Stress Disorder (PTSD) nomenclature. With the advent of Diagnostic Statistic Manual-5 (DSM-5), there is an impending need to reexamine STS using DSM-5 symptoms in light of the most updated PTSD models in the literature. The study investigated and determined the best fitted PTSD models using DSM-5 PTSD criteria symptoms. Confirmatory factor analysis (CFA) was conducted to examine model fit using the Secondary Traumatic Stress Scale in 241 registered and practicing Filipino nurses (166 females and 75 males) who worked in the Philippines and gave direct nursing services to patients. Based on multiple fit indices, the results showed the 7-factor hybrid model, comprising of intrusion, avoidance, negative affect, anhedonia, externalizing behavior, anxious arousal, and dysphoric arousal factors has excellent fit to STS. This model asserts that: (1) hyperarousal criterion needs to be divided into anxious and dysphoric arousal factors; (2) symptoms characterizing negative and positive affect need to be separated to two separate factors, and; (3) a new factor would categorize externalized, self-initiated impulse and control-deficit behaviors. Comparison of nested and non-nested models showed Hybrid model to have superior fit over other models. The specificity of the symptom structure of STS based on DSM-5 PTSD criteria suggests having more specific interventions addressing the more elaborate symptom-groupings that would alleviate the condition of nurses exposed to STS on a daily basis. Copyright © 2016 Elsevier B.V. All rights reserved.

Local-Level Prognostics Health Management Systems Framework for Passive AdvSMR Components. Interim Report

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

Ramuhalli, Pradeep; Roy, Surajit; Hirt, Evelyn H.

2014-09-12

This report describes research results to date in support of the integration and demonstration of diagnostics technologies for prototypical AdvSMR passive components (to establish condition indices for monitoring) with model-based prognostics methods. The focus of the PHM methodology and algorithm development in this study is at the localized scale. Multiple localized measurements of material condition (using advanced nondestructive measurement methods), along with available measurements of the stressor environment, enhance the performance of localized diagnostics and prognostics of passive AdvSMR components and systems.

[Modeling and implementation method for the automatic biochemistry analyzer control system].

PubMed

Wang, Dong; Ge, Wan-cheng; Song, Chun-lin; Wang, Yun-guang

2009-03-01

In this paper the system structure The automatic biochemistry analyzer is a necessary instrument for clinical diagnostics. First of is analyzed. The system problems description and the fundamental principles for dispatch are brought forward. Then this text puts emphasis on the modeling for the automatic biochemistry analyzer control system. The objects model and the communications model are put forward. Finally, the implementation method is designed. It indicates that the system based on the model has good performance.

Can CT and MR Shape and Textural Features Differentiate Benign Versus Malignant Pleural Lesions?

PubMed

Pena, Elena; Ojiaku, MacArinze; Inacio, Joao R; Gupta, Ashish; Macdonald, D Blair; Shabana, Wael; Seely, Jean M; Rybicki, Frank J; Dennie, Carole; Thornhill, Rebecca E

2017-10-01

The study aimed to identify a radiomic approach based on CT and or magnetic resonance (MR) features (shape and texture) that may help differentiate benign versus malignant pleural lesions, and to assess if the radiomic model may improve confidence and accuracy of radiologists with different subspecialty backgrounds. Twenty-nine patients with pleural lesions studied on both contrast-enhanced CT and MR imaging were reviewed retrospectively. Three texture and three shape features were extracted. Combinations of features were used to generate logistic regression models using histopathology as outcome. Two thoracic and two abdominal radiologists evaluated their degree of confidence in malignancy. Diagnostic accuracy of radiologists was determined using contingency tables. Cohen's kappa coefficient was used to assess inter-reader agreement. Using optimal threshold criteria, sensitivity, specificity, and accuracy of each feature and combination of features were obtained and compared to the accuracy and confidence of radiologists. The CT model that best discriminated malignant from benign lesions revealed an AUC CT  = 0.92 ± 0.05 (P < 0.0001). The most discriminative MR model showed an AUC MR  = 0.87 ± 0.09 (P < 0.0001). The CT model was compared to the diagnostic confidence of all radiologists and the model outperformed both abdominal radiologists (P < 0.002), whereas the top discriminative MR model outperformed one of the abdominal radiologists (P = 0.02). The most discriminative MR model was more accurate than one abdominal (P = 0.04) and one thoracic radiologist (P = 0.02). Quantitative textural and shape analysis may help distinguish malignant from benign lesions. A radiomics-based approach may increase diagnostic confidence of abdominal radiologists on CT and MR and may potentially improve radiologists' accuracy in the assessment of pleural lesions characterized by MR. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-06-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Diagnostic Classification Models: Are They Necessary? Commentary on Rupp and Templin (2008)

ERIC Educational Resources Information Center

Gorin, Joanna S.

2009-01-01

In their paper "Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art," Andre Rupp and Jonathan Templin (2008) provide a comparative analysis of selected psychometric models useful for the analysis of multidimensional data for purposes of diagnostic score reporting. Recent assessment…

Extreme ultraviolet spectroscopy diagnostics of low-temperature plasmas based on a sliced multilayer grating and glass capillary optics.

PubMed

Kantsyrev, V L; Safronova, A S; Williamson, K M; Wilcox, P; Ouart, N D; Yilmaz, M F; Struve, K W; Voronov, D L; Feshchenko, R M; Artyukov, I A; Vinogradov, A V

2008-10-01

New extreme ultraviolet (EUV) spectroscopic diagnostics of relatively low-temperature plasmas based on the application of an EUV spectrometer and fast EUV diodes combined with glass capillary optics is described. An advanced high resolution dispersive element sliced multilayer grating was used in the compact EUV spectrometer. For monitoring of the time history of radiation, filtered fast EUV diodes were used in the same spectral region (>13 nm) as the EUV spectrometer. The radiation from the plasma was captured by using a single inexpensive glass capillary that was transported onto the spectrometer entrance slit and EUV diode. The use of glass capillary optics allowed placement of the spectrometer and diodes behind the thick radiation shield outside the direction of a possible hard x-ray radiation beam and debris from the plasma source. The results of the testing and application of this diagnostic for a compact laser plasma source are presented. Examples of modeling with parameters of plasmas are discussed.

Utility of non-rule-based visual matching as a strategy to allow novices to achieve skin lesion diagnosis.

PubMed

Aldridge, R Benjamin; Glodzik, Dominik; Ballerini, Lucia; Fisher, Robert B; Rees, Jonathan L

2011-05-01

Non-analytical reasoning is thought to play a key role in dermatology diagnosis. Considering its potential importance, surprisingly little work has been done to research whether similar identification processes can be supported in non-experts. We describe here a prototype diagnostic support software, which we have used to examine the ability of medical students (at the beginning and end of a dermatology attachment) and lay volunteers, to diagnose 12 images of common skin lesions. Overall, the non-experts using the software had a diagnostic accuracy of 98% (923/936) compared with 33% for the control group (215/648) (Wilcoxon p < 0.0001). We have demonstrated, within the constraints of a simplified clinical model, that novices' diagnostic scores are significantly increased by the use of a structured image database coupled with matching of index and referent images. The novices achieve this high degree of accuracy without any use of explicit definitions of likeness or rule-based strategies.

Diagnostic articulation tables

NASA Astrophysics Data System (ADS)

Mikhailov, V. G.

2002-09-01

In recent years, considerable progress has been made in the development of instrumental methods for general speech quality and intelligibility evaluation on the basis of modeling the auditory perception of speech and measuring the signal-to-noise ratio. Despite certain advantages (fast measurement procedures with a low labor consumption), these methods are not universal and, in essence, secondary, because they rely on the calibration based on subjective-statistical measurements. At the same time, some specific problems of speech quality evaluation, such as the diagnostics of the factors responsible for the deviation of the speech quality from standard (e.g., accent features of a speaker or individual voice distortions), can be solved by psycholinguistic methods. This paper considers different kinds of diagnostic articulation tables: tables of minimal pairs of monosyllabic words (DRT) based on the Jacobson differential features, tables consisting of multisyllabic quartets of Russian words (the choice method), and tables of incomplete monosyllables of the _VC/CV_ type (the supplementary note method). Comparative estimates of the tables are presented along with the recommendations concerning their application.

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

Lu, Tianfeng

The goal of the proposed research is to create computational flame diagnostics (CFLD) that are rigorous numerical algorithms for systematic detection of critical flame features, such as ignition, extinction, and premixed and non-premixed flamelets, and to understand the underlying physicochemical processes controlling limit flame phenomena, flame stabilization, turbulence-chemistry interactions and pollutant emissions etc. The goal has been accomplished through an integrated effort on mechanism reduction, direct numerical simulations (DNS) of flames at engine conditions and a variety of turbulent flames with transport fuels, computational diagnostics, turbulence modeling, and DNS data mining and data reduction. The computational diagnostics are primarily basedmore » on the chemical explosive mode analysis (CEMA) and a recently developed bifurcation analysis using datasets from first-principle simulations of 0-D reactors, 1-D laminar flames, and 2-D and 3-D DNS (collaboration with J.H. Chen and S. Som at Argonne, and C.S. Yoo at UNIST). Non-stiff reduced mechanisms for transportation fuels amenable for 3-D DNS are developed through graph-based methods and timescale analysis. The flame structures, stabilization mechanisms, local ignition and extinction etc., and the rate controlling chemical processes are unambiguously identified through CFLD. CEMA is further employed to segment complex turbulent flames based on the critical flame features, such as premixed reaction fronts, and to enable zone-adaptive turbulent combustion modeling.« less

Contrasting Models of Posttraumatic Stress Disorder: Reply to Monroe and Mineka (2008)

ERIC Educational Resources Information Center

Berntsen, Dorthe; Rubin, David C.; Bohni, Malene Klindt

2008-01-01

The authors address the 4 main points in S. M. Monroe and S. Mineka's comment. First, the authors show that the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000) posttraumatic stress disorder (PTSD) diagnosis includes an etiology and that it is based on a theoretical model with a…

Differential Item Functioning Assessment in Cognitive Diagnostic Modeling: Application of the Wald Test to Investigate DIF in the DINA Model

ERIC Educational Resources Information Center

Hou, Likun; de la Torre, Jimmy; Nandakumar, Ratna

2014-01-01

Analyzing examinees' responses using cognitive diagnostic models (CDMs) has the advantage of providing diagnostic information. To ensure the validity of the results from these models, differential item functioning (DIF) in CDMs needs to be investigated. In this article, the Wald test is proposed to examine DIF in the context of CDMs. This study…

Action-angle formulation of generalized, orbit-based, fast-ion diagnostic weight functions

NASA Astrophysics Data System (ADS)

Stagner, L.; Heidbrink, W. W.

2017-09-01

Due to the usually complicated and anisotropic nature of the fast-ion distribution function, diagnostic velocity-space weight functions, which indicate the sensitivity of a diagnostic to different fast-ion velocities, are used to facilitate the analysis of experimental data. Additionally, when velocity-space weight functions are discretized, a linear equation relating the fast-ion density and the expected diagnostic signal is formed. In a technique known as velocity-space tomography, many measurements can be combined to create an ill-conditioned system of linear equations that can be solved using various computational methods. However, when velocity-space weight functions (which by definition ignore spatial dependencies) are used, velocity-space tomography is restricted, both by the accuracy of its forward model and also by the availability of spatially overlapping diagnostic measurements. In this work, we extend velocity-space weight functions to a full 6D generalized coordinate system and then show how to reduce them to a 3D orbit-space without loss of generality using an action-angle formulation. Furthermore, we show how diagnostic orbit-weight functions can be used to infer the full fast-ion distribution function, i.e., orbit tomography. In depth derivations of orbit weight functions for the neutron, neutral particle analyzer, and fast-ion D-α diagnostics are also shown.

A new diagnostic for tropospheric ozone production

NASA Astrophysics Data System (ADS)

Edwards, Peter M.; Evans, Mathew J.

2017-11-01

Tropospheric ozone is important for the Earth's climate and air quality. It is produced during the oxidation of organics in the presence of nitrogen oxides. Due to the range of organic species emitted and the chain-like nature of their oxidation, this chemistry is complex and understanding the role of different processes (emission, deposition, chemistry) is difficult. We demonstrate a new methodology for diagnosing ozone production based on the processing of bonds contained within emitted molecules, the fate of which is determined by the conservation of spin of the bonding electrons. Using this methodology to diagnose ozone production in the GEOS-Chem chemical transport model, we demonstrate its advantages over the standard diagnostic. We show that the number of bonds emitted, their chemistry and lifetime, and feedbacks on OH are all important in determining the ozone production within the model and its sensitivity to changes. This insight may allow future model-model comparisons to better identify the root causes of model differences.

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

PubMed

Dinov, Ivo D; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W; Price, Nathan D; Van Horn, John D; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M; Dauer, William; Toga, Arthur W

2016-01-01

A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson's disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson's disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer's, Huntington's, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Efficient genome-wide association in biobanks using topic modeling identifies multiple novel disease loci.

PubMed

McCoy, Thomas H; Castro, Victor M; Snapper, Leslie A; Hart, Kamber L; Perlis, Roy H

2017-08-31

Biobanks and national registries represent a powerful tool for genomic discovery, but rely on diagnostic codes that may be unreliable and fail to capture the relationship between related diagnoses. We developed an efficient means of conducting genome-wide association studies using combinations of diagnostic codes from electronic health records (EHR) for 10845 participants in a biobanking program at two large academic medical centers. Specifically, we applied latent Dirichilet allocation to fit 50 disease topics based on diagnostic codes, then conducted genome-wide common-variant association for each topic. In sensitivity analysis, these results were contrasted with those obtained from traditional single-diagnosis phenome-wide association analysis, as well as those in which only a subset of diagnostic codes are included per topic. In meta-analysis across three biobank cohorts, we identified 23 disease-associated loci with p<1e-15, including previously associated autoimmune disease loci. In all cases, observed significant associations were of greater magnitude than for single phenome-wide diagnostic codes, and incorporation of less strongly-loading diagnostic codes enhanced association. This strategy provides a more efficient means of phenome-wide association in biobanks with coded clinical data.

Efficient Genome-wide Association in Biobanks Using Topic Modeling Identifies Multiple Novel Disease Loci

PubMed Central

McCoy, Thomas H; Castro, Victor M; Snapper, Leslie A; Hart, Kamber L; Perlis, Roy H

2017-01-01

Biobanks and national registries represent a powerful tool for genomic discovery, but rely on diagnostic codes that can be unreliable and fail to capture relationships between related diagnoses. We developed an efficient means of conducting genome-wide association studies using combinations of diagnostic codes from electronic health records for 10,845 participants in a biobanking program at two large academic medical centers. Specifically, we applied latent Dirichilet allocation to fit 50 disease topics based on diagnostic codes, then conducted a genome-wide common-variant association for each topic. In sensitivity analysis, these results were contrasted with those obtained from traditional single-diagnosis phenome-wide association analysis, as well as those in which only a subset of diagnostic codes were included per topic. In meta-analysis across three biobank cohorts, we identified 23 disease-associated loci with p < 1e-15, including previously associated autoimmune disease loci. In all cases, observed significant associations were of greater magnitude than single phenome-wide diagnostic codes, and incorporation of less strongly loading diagnostic codes enhanced association. This strategy provides a more efficient means of identifying phenome-wide associations in biobanks with coded clinical data. PMID:28861588

Systematic Review of Economic Models Used to Compare Techniques for Detecting Peripheral Arterial Disease.

PubMed

Moloney, Eoin; O'Connor, Joanne; Craig, Dawn; Robalino, Shannon; Chrysos, Alexandros; Javanbakht, Mehdi; Sims, Andrew; Stansby, Gerard; Wilkes, Scott; Allen, John

2018-04-23

Peripheral arterial disease (PAD) is a common condition, in which atherosclerotic narrowing in the arteries restricts blood supply to the leg muscles. In order to support future model-based economic evaluations comparing methods of diagnosis in this area, a systematic review of economic modelling studies was conducted. A systematic literature review was performed in June 2017 to identify model-based economic evaluations of diagnostic tests to detect PAD, with six individual databases searched. The review was conducted in accordance with the methods outlined in the Centre for Reviews and Dissemination's guidance for undertaking reviews in healthcare, and appropriate inclusion criteria were applied. Relevant data were extracted, and studies were quality assessed. Seven studies were included in the final review, all of which were published between 1995 and 2014. There was wide variation in the types of diagnostic test compared. The majority of the studies (six of seven) referenced the sources used to develop their model, and all studies stated and justified the structural assumptions. Reporting of the data within the included studies could have been improved. Only one identified study focused on the cost-effectiveness of a test typically used in primary care. This review brings together all applied modelling methods for tests used in the diagnosis of PAD, which could be used to support future model-based economic evaluations in this field. The limited modelling work available on tests typically used for the detection of PAD in primary care, in particular, highlights the importance of future work in this area.

Economic evaluation of test-and-treat and empirical treatment strategies in the eradication of Helicobacter pylori infection; A Markov model in an Iranian adult population.

PubMed

Mazdaki, Alireza; Ghiasvand, Hesam; Sarabi Asiabar, Ali; Naghdi, Seyran; Aryankhesal, Aidin

2016-01-01

Helicobacter pylori may cause many gastrointestinal problems in developing countries such as Iran. We aimed to analyze the cost- effectiveness and cost- utility of the test-and-treat and empirical treatment strategies in managing Helicobacter pylori infection. This was a Markov based economic evaluation. Effectiveness was defined as the symptoms free numbers and QALYs in 100,000 hypothetical adults. The sensitivity analysis was based on Monte Carlo approach. In the test- and- treat strategy, if the serology is the first diagnostic test vs. histology, the cost per symptoms free number would be 291,736.1 Rials while the cost per QALYs would be 339,226.1 Rials. The cost per symptoms free number and cost per QALYs when the 13 C-UBT was used as the first diagnostic test vs. serology was 1,283,200 and 1,492,103 Rials, respectively. In the empirical strategy, if histology is used as the first diagnostic test vs. 13 CUBT, the cost per symptoms free numbers and cost per QALYs would be 793,234 and 955,698 Rials, respectively. If serology were used as the first diagnostic test vs. histology, the cost per symptoms free and QALYs would be 793,234 and 368941 Rials, respectively. There was no significant and considerable dominancy between the alternatives and the diagnostic tests.

Vacuum system design and tritium inventory for the charge exchange diagnostic on the Tokamak Fusion Test Reactor

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

Medley, S.S.

The application of charge exchange analyzers for the measurement of ion temperature in fusion plasma experiments requires a direct connection between the diagnostic and plasma-discharge vacuum chambers. Differential pumping of the gas load from the diagnostic stripping cell operated at > or approx. = 10/sup -3/ Torr is required to maintain the analyzer chamber at a pressure of < or approx. = 10/sup -6/ Torr. The migration of gases between the diagnostic and plasma vacuum chambers must be minimized. In particular, introduction of the analyzer stripping cell gas into the plasma chamber having a base pressure of < or approx.more » = 10/sup -8/ Torr must be suppressed. The charge exchange diagnostic for the Tokamak Fusion Test Reactor (TFTR) is comprised of two analyzer systems designed to contain a total of 18 independent mass/energy analyzers and one diagnostic neutral beam rated at 80 keV, 15 A. The associated arrays of multiple, interconnected vacuum systems were analyzed using the Vacuum System Transient Simulator (Vsts) computer program which models the transient transport of multigas species through complex networks of ducts, valves, traps, vacuum pumps, and other related vacuum system components. In addition to providing improved design performance at reduced costs, the analysis yields estimates for the exchange of tritium from the torus to the diagnostic components and of the diagnostic working gases to the torus.« less

National Centers for Environmental Prediction

Science.gov Websites

Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model Configuration /EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION / DIAGNOSTICS Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

Research and development studies for MHD/coal power flow train components. Part II. Diagnostics and instrumentation MHD channel combutor. Progres report. [Flow calculations for combustors

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

Bloom, M.H.; Lederman, S.; Sforza, P.

1980-01-01

This is Part II of the Technical Progress Report on Tasks II-IV of the subject contract. It deals sequentially with Diagnostics and Instrumentation, the MHD Channel and the Combustor. During this period, a significant effort has gone into establishing a schematic design of a laser diagnostic system which can be applied to the flow-train of the MHD system, and to acquiring, assembling and shaking down a laboratory set-up upon which a prototype can be based. With further reference to the MHD Channel, a model analysis has been initiated of the two-dimensional MHD boundary layer between two electrodes in the limitmore » of small magnetic Reynolds numbers with negligible effect of the flow on the applied magnetic field. An objective of this model study is the assessment of variations in initial conditions on the boundary layer behavior. Finally, the problem of combustion modeling has been studied on an initial basis. The open reports on this subject depict a high degree of empiricism, centering attention on global behavior mainly. A quasi-one-dimensional model code has been set-up to check some of the existing estimates. Also a code for equilibrium combustion has been activated.« less

Clinical applicability of Tokyo guidelines 2018/2013 in diagnosis and severity evaluation of acute cholangitis and determination of a new severity model.

PubMed

Gravito-Soares, Elisa; Gravito-Soares, Marta; Gomes, Dário; Almeida, Nuno; Tomé, Luís

2018-03-01

To determine the diagnostic accuracy of Tokyo guidelines (TG) 2018/2013 (TG18/TG13) and predictors of poor prognosis in acute cholangitis. Retrospective 1-year study of consecutive hospital admissions for acute cholangitis. Prognosis was defined in terms of 30 d in-hospital mortality. Of the 183 patients with acute cholangitis, diagnostic accuracy based on Charcot's triad, TG07 and TG18/TG13 was 67.8, 86.9 and 92.3% (p < .001), respectively. Regarding severity based on TG18/TG13, 30.6% of cases were severe. A poor prognosis was found in 10.9% of patients. After multivariate analysis, systolic blood pressure <90 mmHg (OR 11.010; p < .001), serum albumin <3 g/dL (OR 1.355; p = .006), active oncology disease (OR 3.818; p = .006) and malignant aetiology of obstructive jaundice (OR 2.224; p = .021) were independent predictors of poor prognosis. The discriminative ability of the model with these four variables was high (AUROC 0.842; p < .001), being superior to TG18/TG13 (AUROC 0.693; p = .005). TG18/TG13 showed high diagnostic accuracy in acute cholangitis. Compared with TG18/TG13, the simplified severity model ≥2 allows easy selection of patients who will benefit from admission to the intensive care unit and early biliary decompression.

Diagnostic Profiles: A Standard Setting Method for Use with a Cognitive Diagnostic Model

ERIC Educational Resources Information Center

Skaggs, Gary; Hein, Serge F.; Wilkins, Jesse L. M.

2016-01-01

This article introduces the Diagnostic Profiles (DP) standard setting method for setting a performance standard on a test developed from a cognitive diagnostic model (CDM), the outcome of which is a profile of mastered and not-mastered skills or attributes rather than a single test score. In the DP method, the key judgment task for panelists is a…

Defining Characteristics of Diagnostic Classification Models and the Problem of Retrofitting in Cognitive Diagnostic Assessment

ERIC Educational Resources Information Center

Gierl, Mark J.; Cui, Ying

2008-01-01

One promising application of diagnostic classification models (DCM) is in the area of cognitive diagnostic assessment in education. However, the successful application of DCM in educational testing will likely come with a price--and this price may be in the form of new test development procedures and practices required to yield data that satisfy…

Status of Real-Time Laser Based Ion Engine Diagnostics at NASA Glenn Research Center

NASA Technical Reports Server (NTRS)

Domonkos, Matthew T.; Williams, George J., Jr.

2001-01-01

The development status of laser based erosion diagnostics for ion engines at the NASA Glenn Research Center is discussed. The diagnostics are being developed to enhance component life-prediction capabilities. A direct measurement of the erosion product density using laser induced fluorescence (LIF) is described. Erosion diagnostics based upon evaluation of the ion dynamics are also under development, and the basic approach is presented. The planned implementation of the diagnostics is discussed.

The SUCCESS model for laboratory performance and execution of rapid molecular diagnostics in patients with sepsis.

PubMed

Dekmezian, Mhair; Beal, Stacy G; Damashek, Mary Jane; Benavides, Raul; Dhiman, Neelam

2015-04-01

Successful performance and execution of rapid diagnostics in a clinical laboratory hinges heavily on careful validation, accurate and timely communication of results, and real-time quality monitoring. Laboratories must develop strategies to integrate diagnostics with stewardship and evidence-based clinical practice guidelines. We present a collaborative SUCCESS model for execution and monitoring of rapid sepsis diagnostics to facilitate timely treatment. Six months after execution of the Verigene Gram-Positive Blood Culture (BC-GP) and the AdvanDx PNA-FISH assays, data were collected on 579 and 28 episodes of bacteremia and fungemia, respectively. Clinical testing was executed using a SUCCESS model comprising the following components: stewardship, utilization of resources, core strategies, concierge services, education, support, and surveillance. Stewardship needs were identified by evaluating the specialty services benefiting from new testing. Utilization of resources was optimized by reviewing current treatment strategies and antibiogram and formulary options. Core strategies consisted of input from infectious disease leadership, pharmacy, and laboratory staff. Concierge services included automated Micro-eUpdate and physician-friendly actionable reports. Education modules were user-specific, and support was provided through a dedicated 24/7 microbiology hotline. Surveillance was performed by daily audit by the director. Using the SUCCESS model, the turnaround time for the detailed report with actionable guidelines to the physician was ∼3 hours from the time of culture positivity. The overall correlation between rapid methods and culture was 94% (546/579). Discrepant results were predominantly contaminants such as a coagulase-negative staphylococci or viridans streptococci in mixed cultures. SUCCESS is a cost-effective and easily adaptable model for clinical laboratories with limited stewardship resources.

The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

PubMed

Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

2013-01-01

A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P < .001). In the updating process, age, history, and additional candidate predictors did not significantly increase discrimination, being 94%, and leaving only 4 predictors of the original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Electron Beam Transport in Advanced Plasma Wave Accelerators

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

Williams, Ronald L

2013-01-31

The primary goal of this grant was to develop a diagnostic for relativistic plasma wave accelerators based on injecting a low energy electron beam (5-50keV) perpendicular to the plasma wave and observing the distortion of the electron beam's cross section due to the plasma wave's electrostatic fields. The amount of distortion would be proportional to the plasma wave amplitude, and is the basis for the diagnostic. The beat-wave scheme for producing plasma waves, using two CO2 laser beam, was modeled using a leap-frog integration scheme to solve the equations of motion. Single electron trajectories and corresponding phase space diagrams weremore » generated in order to study and understand the details of the interaction dynamics. The electron beam was simulated by combining thousands of single electrons, whose initial positions and momenta were selected by random number generators. The model was extended by including the interactions of the electrons with the CO2 laser fields of the beat wave, superimposed with the plasma wave fields. The results of the model were used to guide the design and construction of a small laboratory experiment that may be used to test the diagnostic idea.« less

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

Han, S; Ji, Y; Kim, K

Purpose: A diagnostics Multileaf Collimator (MLC) was designed for diagnostic radiography dose reduction. Monte Carlo simulation was used to evaluate efficiency of shielding material for producing leaves of Multileaf collimator. Material & Methods: The general radiography unit (Rex-650R, Listem, Korea) was modeling with Monte Carlo simulation (MCNPX, LANL, USA) and we used SRS-78 program to calculate the energy spectrum of tube voltage (80, 100, 120 kVp). The shielding materials was SKD 11 alloy tool steel that is composed of 1.6% carbon(C), 0.4% silicon (Si), 0.6% manganese (Mn), 5% chromium (Cr), 1% molybdenum (Mo), and vanadium (V). The density of itmore » was 7.89 g/m3. We simulated leafs diagnostic MLC using SKD 11 with general radiography unit. We calculated efficiency of diagnostic MLC using tally6 card of MCNPX depending on energy. Results: The diagnostic MLC consisted of 25 individual metal shielding leaves on both sides, with dimensions of 10 × 0.5 × 0.5 cm3. The leaves of MLC were controlled by motors positioned on both sides of the MLC. According to energy (tube voltage), the shielding efficiency of MLC in Monte Carlo simulation was 99% (80 kVp), 96% (100 kVp) and 93% (120 kVp). Conclusion: We certified efficiency of diagnostic MLC fabricated from SKD11 alloy tool steel. Based on the results, the diagnostic MLC was designed. We will make the diagnostic MLC for dose reduction of diagnostic radiography.« less

Cost-effectiveness of a new urinary biomarker-based risk score compared to standard of care in prostate cancer diagnostics - a decision analytical model.

PubMed

Dijkstra, Siebren; Govers, Tim M; Hendriks, Rianne J; Schalken, Jack A; Van Criekinge, Wim; Van Neste, Leander; Grutters, Janneke P C; Sedelaar, John P Michiel; van Oort, Inge M

2017-11-01

To assess the cost-effectiveness of a new urinary biomarker-based risk score (SelectMDx; MDxHealth, Inc., Irvine, CA, USA) to identify patients for transrectal ultrasonography (TRUS)-guided biopsy and to compare this with the current standard of care (SOC), using only prostate-specific antigen (PSA) to select for TRUS-guided biopsy. A decision tree and Markov model were developed to evaluate the cost-effectiveness of SelectMDx as a reflex test vs SOC in men with a PSA level of >3 ng/mL. Transition probabilities, utilities and costs were derived from the literature and expert opinion. Cost-effectiveness was expressed in quality-adjusted life years (QALYs) and healthcare costs of both diagnostic strategies, simulating the course of patients over a time horizon representing 18 years. Deterministic sensitivity analyses were performed to address uncertainty in assumptions. A diagnostic strategy including SelectMDx with a cut-off chosen at a sensitivity of 95.7% for high-grade prostate cancer resulted in savings of €128 and a gain of 0.025 QALY per patient compared to the SOC strategy. The sensitivity analyses showed that the disutility assigned to active surveillance had a high impact on the QALYs gained and the disutility attributed to TRUS-guided biopsy only slightly influenced the outcome of the model. Based on the currently available evidence, the reduction of over diagnosis and overtreatment due to the use of the SelectMDx test in men with PSA levels of >3 ng/mL may lead to a reduction in total costs per patient and a gain in QALYs. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

The Application of Cognitive Diagnostic Approaches via Neural Network Analysis of Serious Educational Games

NASA Astrophysics Data System (ADS)

Lamb, Richard L.

Serious Educational Games (SEGs) have been a topic of increased popularity within the educational realm since the early millennia. SEGs are generalized form of Serious Games to mean games for purposes other than entertainment but, that also specifically include training, educational purpose and pedagogy within their design. This rise in popularity (for SEGs) has occurred at a time when school systems have increased the type, number, and presentations of student achievement tests for decision-making purposes. These tests often task the form of end of course (year) tests and periodic benchmark testing. As the use of these tests, has increased policymakers have suggested their use as a measure for teacher accountability. The change in testing resulted from a push by school districts and policy makers at various component levels for a data-driven decision-making (D3M) approach. With the data-driven decision making approaches by school districts, there has been an increased focus on the measurement and assessment of student content knowledge with little focus on the contributing factors and cognitive attributes within learning that cross multiple-content areas. One-way to increase the focus on these aspects of learning (factors and attributes) that are additional to content learning is through assessments based in cognitive diagnostics. Cognitive diagnostics are a family of methodological approaches in which tasks tie to specific cognitive attributes for analytical purposes. This study explores data derived from computer data logging (n=158,000) in an observational design, using traditional statistical techniques such as clustering (exploratory and confirmatory), item response theory and through data mining techniques such as artificial neural network analysis. From these analyses, a model of student learning emerges illustrating student thinking and learning while engaged in SEG Design. This study seeks to use cognitive diagnostic type approaches to measure student learning while designing science task based SEGs. In addition, the study suggests that it may be possible to use SEGs to provide a means to administer cognitive diagnostic based assessments in real time. Results of this study suggest the confirmation of four families (factors) of traits illustrating a simple factor loading structure. Item response theory (IRT) results illustrate a 2-parameter logistic model (2PLM) fit allowing for parameterization using the IRT-True Score Method (chi2=1.70, df=1, p=0.19). Finally, fit statistics for the artificial neural network suggest the developed model adequately fits the current data set and provides a means to explore cognitive attributes and their effect on task outcomes. This study has developed a justification for combining and developing two distinct areas of research related to student learning. The first is the use of cognitive diagnostic approaches to assess student learning as it relates to the cognitive attributes used during science processing. The second area is an examination and modeling of the relationship between attributes as propagated in an artificial neural network. Results of the study provide for an ANN model of student cognition while designing science based SEGs (r 2=0.73, RMSE= 0.21) at a convergence of 1000 training iterations. The literature presented in this dissertation work integrates work from multiple field areas. Fields represented in this work range from science education, educational psychology, measurement, and computational psychology.

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study.

PubMed

Walker, Martin; Basáñez, María-Gloria; Ouédraogo, André Lin; Hermsen, Cornelus; Bousema, Teun; Churcher, Thomas S

2015-01-16

Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens.

Long-term Cost-Effectiveness of Diagnostic Tests for Assessing Stable Chest Pain: Modeled Analysis of Anatomical and Functional Strategies.

PubMed

Bertoldi, Eduardo G; Stella, Steffan F; Rohde, Luis E; Polanczyk, Carisi A

2016-05-01

Several tests exist for diagnosing coronary artery disease, with varying accuracy and cost. We sought to provide cost-effectiveness information to aid physicians and decision-makers in selecting the most appropriate testing strategy. We used the state-transitions (Markov) model from the Brazilian public health system perspective with a lifetime horizon. Diagnostic strategies were based on exercise electrocardiography (Ex-ECG), stress echocardiography (ECHO), single-photon emission computed tomography (SPECT), computed tomography coronary angiography (CTA), or stress cardiac magnetic resonance imaging (C-MRI) as the initial test. Systematic review provided input data for test accuracy and long-term prognosis. Cost data were derived from the Brazilian public health system. Diagnostic test strategy had a small but measurable impact in quality-adjusted life-years gained. Switching from Ex-ECG to CTA-based strategies improved outcomes at an incremental cost-effectiveness ratio of 3100 international dollars per quality-adjusted life-year. ECHO-based strategies resulted in cost and effectiveness almost identical to CTA, and SPECT-based strategies were dominated because of their much higher cost. Strategies based on stress C-MRI were most effective, but the incremental cost-effectiveness ratio vs CTA was higher than the proposed willingness-to-pay threshold. Invasive strategies were dominant in the high pretest probability setting. Sensitivity analysis showed that results were sensitive to costs of CTA, ECHO, and C-MRI. Coronary CT is cost-effective for the diagnosis of coronary artery disease and should be included in the Brazilian public health system. Stress ECHO has a similar performance and is an acceptable alternative for most patients, but invasive strategies should be reserved for patients at high risk. © 2016 Wiley Periodicals, Inc.

Diagnostic uncertainty, guilt, mood, and disability in back pain.

PubMed

Serbic, Danijela; Pincus, Tamar; Fife-Schaw, Chris; Dawson, Helen

2016-01-01

In the majority of patients a definitive cause for low back pain (LBP) cannot be established, and many patients report feeling uncertain about their diagnosis, accompanied by guilt. The relationship between diagnostic uncertainty, guilt, mood, and disability is currently unknown. This study tested 3 theoretical models to explore possible pathways between these factors. In Model 1, diagnostic uncertainty was hypothesized to correlate with pain-related guilt, which in turn would positively correlate with depression, anxiety and disability. Two alternative models were tested: (a) a path from depression and anxiety to guilt, from guilt to diagnostic uncertainty, and finally to disability; (b) a model in which depression and anxiety, and independently, diagnostic uncertainty, were associated with guilt, which in turn was associated with disability. Structural equation modeling was employed on data from 413 participants with chronic LBP. All 3 models showed a reasonable-to-good fit with the data, with the 2 alternative models providing marginally better fit indices. Guilt, and especially social guilt, was associated with disability in all 3 models. Diagnostic uncertainty was associated with guilt, but only moderately. Low mood was also associated with guilt. Two newly defined factors, pain related guilt and diagnostic uncertainty, appear to be linked to disability and mood in people with LBP. The causal path of these links cannot be established in this cross sectional study. However, pain-related guilt especially appears to be important, and future research should examine whether interventions directly targeting guilt improve outcomes. (c) 2015 APA, all rights reserved).

Electromagnetic Nanoparticles for Sensing and Medical Diagnostic Applications

PubMed Central

Vegni, Lucio

2018-01-01

A modeling and design approach is proposed for nanoparticle-based electromagnetic devices. First, the structure properties were analytically studied using Maxwell’s equations. The method provides us a robust link between nanoparticles electromagnetic response (amplitude and phase) and their geometrical characteristics (shape, geometry, and dimensions). Secondly, new designs based on “metamaterial” concept are proposed, demonstrating great performances in terms of wide-angle range functionality and multi/wide behavior, compared to conventional devices working at the same frequencies. The approach offers potential applications to build-up new advanced platforms for sensing and medical diagnostics. Therefore, in the final part of the article, some practical examples are reported such as cancer detection, water content measurements, chemical analysis, glucose concentration measurements and blood diseases monitoring. PMID:29652853

Potential Cost-effectiveness of Early Identification of Hospital-acquired Infection in Critically Ill Patients.

PubMed

Tsalik, Ephraim L; Li, Yanhong; Hudson, Lori L; Chu, Vivian H; Himmel, Tiffany; Limkakeng, Alex T; Katz, Jason N; Glickman, Seth W; McClain, Micah T; Welty-Wolf, Karen E; Fowler, Vance G; Ginsburg, Geoffrey S; Woods, Christopher W; Reed, Shelby D

2016-03-01

Limitations in methods for the rapid diagnosis of hospital-acquired infections often delay initiation of effective antimicrobial therapy. New diagnostic approaches offer potential clinical and cost-related improvements in the management of these infections. We developed a decision modeling framework to assess the potential cost-effectiveness of a rapid biomarker assay to identify hospital-acquired infection in high-risk patients earlier than standard diagnostic testing. The framework includes parameters representing rates of infection, rates of delayed appropriate therapy, and impact of delayed therapy on mortality, along with assumptions about diagnostic test characteristics and their impact on delayed therapy and length of stay. Parameter estimates were based on contemporary, published studies and supplemented with data from a four-site, observational, clinical study. Extensive sensitivity analyses were performed. The base-case analysis assumed 17.6% of ventilated patients and 11.2% of nonventilated patients develop hospital-acquired infection and that 28.7% of patients with hospital-acquired infection experience delays in appropriate antibiotic therapy with standard care. We assumed this percentage decreased by 50% (to 14.4%) among patients with true-positive results and increased by 50% (to 43.1%) among patients with false-negative results using a hypothetical biomarker assay. Cost of testing was set at $110/d. In the base-case analysis, among ventilated patients, daily diagnostic testing starting on admission reduced inpatient mortality from 12.3 to 11.9% and increased mean costs by $1,640 per patient, resulting in an incremental cost-effectiveness ratio of $21,389 per life-year saved. Among nonventilated patients, inpatient mortality decreased from 7.3 to 7.1% and costs increased by $1,381 with diagnostic testing. The resulting incremental cost-effectiveness ratio was $42,325 per life-year saved. Threshold analyses revealed the probabilities of developing hospital-acquired infection in ventilated and nonventilated patients could be as low as 8.4 and 9.8%, respectively, to maintain incremental cost-effectiveness ratios less than $50,000 per life-year saved. Development and use of serial diagnostic testing that reduces the proportion of patients with delays in appropriate antibiotic therapy for hospital-acquired infections could reduce inpatient mortality. The model presented here offers a cost-effectiveness framework for future test development.

Increased diagnostic activity in general practice during the year preceding colorectal cancer diagnosis.

PubMed

Hansen, Pernille Libach; Hjertholm, Peter; Vedsted, Peter

2015-08-01

Accurate diagnostic activity in general practice before colorectal cancer (CRC) diagnosis is crucial for an early detection of CRC. This study aimed to investigate the rates of daytime consultations, hemoglobin (Hb) measurements and medicine prescriptions for hemorrhoids in general practice in the year preceding CRC diagnosis. Using Danish registries, we conducted a population-based matched cohort study including CRC patients aged 40-80 years (n = 19,209) and matched references (n = 192,090). We calculated odds ratios (ORs) using a conditional logistical regression model and incidence rate ratios (IRRs) using a negative binomial regression model. The CRC patients had significantly more consultations from 9 months before diagnosis and significantly increased rates of Hb measurements from up to 17 months before diagnosis compared with references. Furthermore, up to 18 months before diagnosis, CRC patients had significantly higher rates of prescriptions for hemorrhoids; and 2 months before diagnosis, the IRR was 12.24 (95% confidence interval (CI): 10.29-14.55) for men. The positive predictive value (PPV) of CRC for having a first-time prescription for hemorrhoids was highest among men aged 70-80 years [PPV = 3.2% (95% CI: 2.8-3.7)]. High prescription rates were predominantly seen among rectal cancer patients, whereas colon cancer patients had higher rates of consultations and Hb measurements. This study revealed a significant increase in healthcare seeking and diagnostic activity in general practice in the year prior to CRC diagnosis, which indicates the presence of a "diagnostic time window" and a potential for earlier diagnosis of CRC based on clinical signs and symptoms. © 2015 UICC.

[Factors Influencing Quality of Life of Alcoholics Anonymous Members in Korea].

PubMed

Yoo, Jae Soon; Lee, Jongeun; Park, Woo Young

2016-04-01

The purpose of this study was to determine quality of life (QOL) related factors in Alcoholics Anonymous (AA) members based on PRECEDE Model. A cross sectional survey was conducted with participants (N =203) from AA meeting in 11 alcohol counsel centers all over South Korea. Data were collected using a specially designed questionnaire based on the PRECEDE model and including QOL, epidemiological factors (including depression and perceived health status), behavioral factors (continuous abstinence and physical health status and practice), predisposing factors (abstinence self-efficacy and self-esteem), reinforcing factors (social capital and family functioning), and enabling factors. Data were analyzed using t-test, one way ANOVA, Tukey HSD test and hierarchical multiple regression analysis with SPSS (ver. 21.0). Of the educational diagnostic variables, self-esteem (β=.23), family functioning (β=.12), abstinence self-efficacy (β=.12) and social capital (β=.11) were strong influential factors in AA members' QOL. In addition, epidemiological diagnostic variables such as depression (β=-.44) and perceived health status (β=.35) were the main factors in QOL. Also, physical health status and practice (β=.106), one of behavioral diagnostic variables was a beneficial factor in QOL. Hierarchical multiple regression analysis showed the determinant variables accounted for 44.0% of the variation in QOL (F=25.76, p<.001). The finding of the study can be used as a framework for planning interventions in order to promote the quality of life of AA members. It is necessary to develop nursing intervention strategies for strengthening educational and epidemiological diagnostic variables in order to improve AA members' QOL.

Efficient fault diagnosis of helicopter gearboxes

NASA Technical Reports Server (NTRS)

Chin, H.; Danai, K.; Lewicki, D. G.

1993-01-01

Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-fault data. To test this diagnostic system, vibration measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox faults so long as they are included in training.

Large biases in regression-based constituent flux estimates: causes and diagnostic tools

USGS Publications Warehouse

Hirsch, Robert M.

2014-01-01

It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

Assessment of the White Salmon watershed using the ecosystem diagnosis and treatment model

USGS Publications Warehouse

Allen, Brady; Connolly, Patrick J.

2005-01-01

Salmon habitat models provide managers the ability to identify habitat limitations and prioritize restoration activities. Ecosystem Diagnosis and Treatment (EDT) has become a widely used tool for salmonid habitat analysis in the Pacific Northwest. The EDT model is a rule-based habitat rating system that provides reach-level diagnosis of habitat conditions for the major salmonid species of the Pacific Northwest. The EDT process itself is a complex modeling program with defined data needs. The program is a product developed by Mobrand Biometrics Incorporated (MBI) largely through funding by the Northwest Power and Conservation Council (NPCC). The NPCC had provided a free version of the program accessible through a website that required user registration. The EDT model allows the user to rate the quality, quantity, and diversity of fish habitat along a waterway. The model uses diagnostic species such as steelhead and Chinook salmon to identify the most significant limiting factors in a river and to help identify reaches for protection and restoration. The model includes a set of tools to help organize environmental information and rate the habitat elements that pertain to specific life stages of the diagnostic species. A major benefit of EDT is that it can show the potential of a river under current conditions and possible future conditions. The result is a scientifically-based assessment of fish habitat and a prioritization of restoration needs.

A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder

PubMed Central

Yu, J S; Xue, A Y; Redei, E E; Bagheri, N

2016-01-01

Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A notable barrier in addressing this public health threat involves reliable identification of the disorder, as many affected individuals remain undiagnosed or misdiagnosed. An objective blood-based diagnostic test using transcript levels of a panel of markers would provide an invaluable tool for MDD as the infrastructure—including equipment, trained personnel, billing, and governmental approval—for similar tests is well established in clinics worldwide. Here we present a supervised classification model utilizing support vector machines (SVMs) for the analysis of transcriptomic data readily obtained from a peripheral blood specimen. The model was trained on data from subjects with MDD (n=32) and age- and gender-matched controls (n=32). This SVM model provides a cross-validated sensitivity and specificity of 90.6% for the diagnosis of MDD using a panel of 10 transcripts. We applied a logistic equation on the SVM model and quantified a likelihood of depression score. This score gives the probability of a MDD diagnosis and allows the tuning of specificity and sensitivity for individual patients to bring personalized medicine closer in psychiatry. PMID:27779627

Customization of a generic 3D model of the distal femur using diagnostic radiographs.

PubMed

Schmutz, B; Reynolds, K J; Slavotinek, J P

2008-01-01

A method for the customization of a generic 3D model of the distal femur is presented. The customization method involves two steps: acquisition of calibrated orthogonal planar radiographs; and linear scaling of the generic model based on the width of a subject's femoral condyles as measured on the planar radiographs. Planar radiographs of seven intact lower cadaver limbs were obtained. The customized generic models were validated by comparing their surface geometry with that of CT-reconstructed reference models. The overall mean error was 1.2 mm. The results demonstrate that uniform scaling as a first step in the customization process produced a base model of accuracy comparable to other models reported in the literature.

PathEdEx – Uncovering High-explanatory Visual Diagnostics Heuristics Using Digital Pathology and Multiscale Gaze Data

PubMed Central

Shin, Dmitriy; Kovalenko, Mikhail; Ersoy, Ilker; Li, Yu; Doll, Donald; Shyu, Chi-Ren; Hammer, Richard

2017-01-01

Background: Visual heuristics of pathology diagnosis is a largely unexplored area where reported studies only provided a qualitative insight into the subject. Uncovering and quantifying pathology visual and nonvisual diagnostic patterns have great potential to improve clinical outcomes and avoid diagnostic pitfalls. Methods: Here, we present PathEdEx, an informatics computational framework that incorporates whole-slide digital pathology imaging with multiscale gaze-tracking technology to create web-based interactive pathology educational atlases and to datamine visual and nonvisual diagnostic heuristics. Results: We demonstrate the capabilities of PathEdEx for mining visual and nonvisual diagnostic heuristics using the first PathEdEx volume of a hematopathology atlas. We conducted a quantitative study on the time dynamics of zooming and panning operations utilized by experts and novices to come to the correct diagnosis. We then performed association rule mining to determine sets of diagnostic factors that consistently result in a correct diagnosis, and studied differences in diagnostic strategies across different levels of pathology expertise using Markov chain (MC) modeling and MC Monte Carlo simulations. To perform these studies, we translated raw gaze points to high-explanatory semantic labels that represent pathology diagnostic clues. Therefore, the outcome of these studies is readily transformed into narrative descriptors for direct use in pathology education and practice. Conclusion: PathEdEx framework can be used to capture best practices of pathology visual and nonvisual diagnostic heuristics that can be passed over to the next generation of pathologists and have potential to streamline implementation of precision diagnostics in precision medicine settings. PMID:28828200

Addressing the challenges of diagnostics demand and supply: insights from an online global health discussion platform.

PubMed

Engel, Nora; Wachter, Keri; Pai, Madhukar; Gallarda, Jim; Boehme, Catharina; Celentano, Isabelle; Weintraub, Rebecca

2016-01-01

Several barriers challenge development, adoption and scale-up of diagnostics in low and middle income countries. An innovative global health discussion platform allows capturing insights from the global health community on factors driving demand and supply for diagnostics. We conducted a qualitative content analysis of the online discussion 'Advancing Care Delivery: Driving Demand and Supply of Diagnostics' organised by the Global Health Delivery Project (GHD) (http://www.ghdonline.org/) at Harvard University. The discussion, driven by 12 expert panellists, explored what must be done to develop delivery systems, business models, new technologies, interoperability standards, and governance mechanisms to ensure that patients receive the right diagnostic at the right time. The GHD Online (GHDonline) platform reaches over 19 000 members from 185 countries. Participants (N=99) in the diagnostics discussion included academics, non-governmental organisations, manufacturers, policymakers, and physicians. Data was coded and overarching categories analysed using qualitative data analysis software. Participants considered technical characteristics of diagnostics as smaller barriers to effective use of diagnostics compared with operational and health system challenges, such as logistics, poor fit with user needs, cost, workforce, infrastructure, access, weak regulation and political commitment. Suggested solutions included: health system strengthening with patient-centred delivery; strengthened innovation processes; improved knowledge base; harmonised guidelines and evaluation; supply chain innovations; and mechanisms for ensuring quality and capacity. Engaging and connecting different actors involved with diagnostic development and use is paramount for improving diagnostics. While the discussion participants were not representative of all actors involved, the platform enabled a discussion between globally acknowledged experts and physicians working in different countries.

PathEdEx - Uncovering High-explanatory Visual Diagnostics Heuristics Using Digital Pathology and Multiscale Gaze Data.

PubMed

Shin, Dmitriy; Kovalenko, Mikhail; Ersoy, Ilker; Li, Yu; Doll, Donald; Shyu, Chi-Ren; Hammer, Richard

2017-01-01

Visual heuristics of pathology diagnosis is a largely unexplored area where reported studies only provided a qualitative insight into the subject. Uncovering and quantifying pathology visual and nonvisual diagnostic patterns have great potential to improve clinical outcomes and avoid diagnostic pitfalls. Here, we present PathEdEx, an informatics computational framework that incorporates whole-slide digital pathology imaging with multiscale gaze-tracking technology to create web-based interactive pathology educational atlases and to datamine visual and nonvisual diagnostic heuristics. We demonstrate the capabilities of PathEdEx for mining visual and nonvisual diagnostic heuristics using the first PathEdEx volume of a hematopathology atlas. We conducted a quantitative study on the time dynamics of zooming and panning operations utilized by experts and novices to come to the correct diagnosis. We then performed association rule mining to determine sets of diagnostic factors that consistently result in a correct diagnosis, and studied differences in diagnostic strategies across different levels of pathology expertise using Markov chain (MC) modeling and MC Monte Carlo simulations. To perform these studies, we translated raw gaze points to high-explanatory semantic labels that represent pathology diagnostic clues. Therefore, the outcome of these studies is readily transformed into narrative descriptors for direct use in pathology education and practice. PathEdEx framework can be used to capture best practices of pathology visual and nonvisual diagnostic heuristics that can be passed over to the next generation of pathologists and have potential to streamline implementation of precision diagnostics in precision medicine settings.

Modeling experimental plasma diagnostics in the FLASH code: Thomson scattering

NASA Astrophysics Data System (ADS)

Weide, Klaus; Flocke, Norbert; Feister, Scott; Tzeferacos, Petros; Lamb, Donald

2017-10-01

Spectral analysis of the Thomson scattering of laser light sent into a plasma provides an experimental method to quantify plasma properties in laser-driven plasma experiments. We have implemented such a synthetic Thomson scattering diagnostic unit in the FLASH code, to emulate the probe-laser propagation, scattering and spectral detection. User-defined laser rays propagate into the FLASH simulation region and experience scattering (change in direction and frequency) based on plasma parameters. After scattering, the rays propagate out of the interaction region and are spectrally characterized. The diagnostic unit can be used either during a physics simulation or in post-processing of simulation results. FLASH is publicly available at flash.uchicago.edu. U.S. DOE NNSA, U.S. DOE NNSA ASC, U.S. DOE Office of Science and NSF.

Combined use of laser Doppler flowmetry and skin thermometry for functional diagnostics of intradermal finger vessels.

PubMed

Zherebtsov, Evgeny A; Zherebtsova, Angelina I; Doronin, Alexander; Dunaev, Andrey V; Podmasteryev, Konstantin V; Bykov, Alexander; Meglinski, Igor

2017-04-01

We introduce a noninvasive diagnostic approach for functional monitoring of blood microflows in capillaries and thermoregulatory vessels within the skin. The measuring system is based on the combined use of laser Doppler flowmetry and skin contact thermometry. The obtained results suggest that monitoring of blood microcirculation during the occlusion, performed in conjunction with the skin temperature measurements in the thermally stabilized medium, has a great potential for quantitative assessment of angiospatic dysfunctions of the peripheral blood vessels. The indices of blood flow reserve and temperature response were measured and used as the primarily parameters of the functional diagnostics of the peripheral vessels of skin. Utilizing these parameters, a simple phenomenological model has been suggested to identify patients with angiospastic violations in the vascular system.

Combined use of laser Doppler flowmetry and skin thermometry for functional diagnostics of intradermal finger vessels

NASA Astrophysics Data System (ADS)

Zherebtsov, Evgeny A.; Zherebtsova, Angelina I.; Doronin, Alexander; Dunaev, Andrey V.; Podmasteryev, Konstantin V.; Bykov, Alexander; Meglinski, Igor

2017-04-01

We introduce a noninvasive diagnostic approach for functional monitoring of blood microflows in capillaries and thermoregulatory vessels within the skin. The measuring system is based on the combined use of laser Doppler flowmetry and skin contact thermometry. The obtained results suggest that monitoring of blood microcirculation during the occlusion, performed in conjunction with the skin temperature measurements in the thermally stabilized medium, has a great potential for quantitative assessment of angiospatic dysfunctions of the peripheral blood vessels. The indices of blood flow reserve and temperature response were measured and used as the primarily parameters of the functional diagnostics of the peripheral vessels of skin. Utilizing these parameters, a simple phenomenological model has been suggested to identify patients with angiospastic violations in the vascular system.

Determining the Optimal Number of Core Needle Biopsy Passes for Molecular Diagnostics.

PubMed

Hoang, Nam S; Ge, Benjamin H; Pan, Lorraine Y; Ozawa, Michael G; Kong, Christina S; Louie, John D; Shah, Rajesh P

2018-03-01

The number of core biopsy passes required for adequate next-generation sequencing is impacted by needle cut, needle gauge, and the type of tissue involved. This study evaluates diagnostic adequacy of core needle lung biopsies based on number of passes and provides guidelines for other tissues based on simulated biopsies in ex vivo porcine organ tissues. The rate of diagnostic adequacy for pathology and molecular testing from lung biopsy procedures was measured for eight operators pre-implementation (September 2012-October 2013) and post-implementation (December 2013-April 2014) of a standard protocol using 20-gauge side-cut needles for ten core biopsy passes at a single academic hospital. Biopsy pass volume was then estimated in ex vivo porcine muscle, liver, and kidney using side-cut devices at 16, 18, and 20 gauge and end-cut devices at 16 and 18 gauge to estimate minimum number of passes required for adequate molecular testing. Molecular diagnostic adequacy increased from 69% (pre-implementation period) to 92% (post-implementation period) (p < 0.001) for lung biopsies. In porcine models, both 16-gauge end-cut and side-cut devices require one pass to reach the validated volume threshold to ensure 99% adequacy for molecular characterization, while 18- and 20-gauge devices require 2-5 passes depending on needle cut and tissue type. Use of 20-gauge side-cut core biopsy needles requires a significant number of passes to ensure diagnostic adequacy for molecular testing across all tissue types. To ensure diagnostic adequacy for molecular testing, 16- and 18-gauge needles require markedly fewer passes.

Innovative Assessments That Support Students' STEM Learning

ERIC Educational Resources Information Center

Thummaphan, Phonraphee

2017-01-01

The present study aimed to represent the innovative assessments that support students' learning in STEM education through using the integrative framework for Cognitive Diagnostic Modeling (CDM). This framework is based on three components, cognition, observation, and interpretation (National Research Council, 2001). Specifically, this dissertation…

Diagnostic reliability of MMPI-2 computer-based test interpretations.

PubMed

Pant, Hina; McCabe, Brian J; Deskovitz, Mark A; Weed, Nathan C; Williams, John E

2014-09-01

Reflecting the common use of the MMPI-2 to provide diagnostic considerations, computer-based test interpretations (CBTIs) also typically offer diagnostic suggestions. However, these diagnostic suggestions can sometimes be shown to vary widely across different CBTI programs even for identical MMPI-2 profiles. The present study evaluated the diagnostic reliability of 6 commercially available CBTIs using a 20-item Q-sort task developed for this study. Four raters each sorted diagnostic classifications based on these 6 CBTI reports for 20 MMPI-2 profiles. Two questions were addressed. First, do users of CBTIs understand the diagnostic information contained within the reports similarly? Overall, diagnostic sorts of the CBTIs showed moderate inter-interpreter diagnostic reliability (mean r = .56), with sorts for the 1/2/3 profile showing the highest inter-interpreter diagnostic reliability (mean r = .67). Second, do different CBTIs programs vary with respect to diagnostic suggestions? It was found that diagnostic sorts of the CBTIs had a mean inter-CBTI diagnostic reliability of r = .56, indicating moderate but not strong agreement across CBTIs in terms of diagnostic suggestions. The strongest inter-CBTI diagnostic agreement was found for sorts of the 1/2/3 profile CBTIs (mean r = .71). Limitations and future directions are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Development of a portable non-contact optical diagnostic system for the detection of δ-HMX

NASA Astrophysics Data System (ADS)

Dale, Andrew J.; Wright, Mark W.; Hughes, Christopher T.; Bowden, Mike D.

2007-09-01

If a HMX-based explosive is subjected to an insult then there is a potential for the insulted β-HMX to undergo a phase change to the more sensitive δ form. AWE has an ongoing programme to develop a science-based model of the response of HMX-based explosives to potential insults. As part of this programme there is a need to identify whether δ-HMX has been formed, as this would subsequently affect the intrinsic safety properties of the formulation. δ-HMX, unlike the more stable β form, exhibits unusual optical properties for an explosive, as it acts as a frequency-doubling material. When illuminated by a high-energy laser pulse areas of the explosive charge that contain δ-HMX emit frequency doubled light. This non-linear optical phenomenon allows for a non-invasive diagnostic to be developed to study creation of the more sensitive δ phase within HMX based formulations. AWE has developed a portable diagnostic system based on this concept to investigate the behaviour of HMX-based explosives after low-speed impacts. The results of the commissioning trials are presented; using both an inert simulant, KDP, to align and prove the system and HMX samples from low-speed impact experiments. The results of these experiments are compared to initial calculations using the Hydrocode EDEN.

Medical applications of model-based dynamic thermography

NASA Astrophysics Data System (ADS)

Nowakowski, Antoni; Kaczmarek, Mariusz; Ruminski, Jacek; Hryciuk, Marcin; Renkielska, Alicja; Grudzinski, Jacek; Siebert, Janusz; Jagielak, Dariusz; Rogowski, Jan; Roszak, Krzysztof; Stojek, Wojciech

2001-03-01

The proposal to use active thermography in medical diagnostics is promising in some applications concerning investigation of directly accessible parts of the human body. The combination of dynamic thermograms with thermal models of investigated structures gives attractive possibility to make internal structure reconstruction basing on different thermal properties of biological tissues. Measurements of temperature distribution synchronized with external light excitation allow registration of dynamic changes of local temperature dependent on heat exchange conditions. Preliminary results of active thermography applications in medicine are discussed. For skin and under- skin tissues an equivalent thermal model may be determined. For the assumed model its effective parameters may be reconstructed basing on the results of transient thermal processes. For known thermal diffusivity and conductivity of specific tissues the local thickness of a two or three layer structure may be calculated. Results of some medical cases as well as reference data of in vivo study on animals are presented. The method was also applied to evaluate the state of the human heart during the open chest cardio-surgical interventions. Reference studies of evoked heart infarct in pigs are referred, too. We see the proposed new in medical applications technique as a promising diagnostic tool. It is a fully non-invasive, clean, handy, fast and affordable method giving not only qualitative view of investigated surfaces but also an objective quantitative measurement result, accurate enough for many applications including fast screening of affected tissues.

A prototype knowledge-based simulation support system

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

Hill, T.R.; Roberts, S.D.

1987-04-01

As a preliminary step toward the goal of an intelligent automated system for simulation modeling support, we explore the feasibility of the overall concept by generating and testing a prototypical framework. A prototype knowledge-based computer system was developed to support a senior level course in industrial engineering so that the overall feasibility of an expert simulation support system could be studied in a controlled and observable setting. The system behavior mimics the diagnostic (intelligent) process performed by the course instructor and teaching assistants, finding logical errors in INSIGHT simulation models and recommending appropriate corrective measures. The system was programmed inmore » a non-procedural language (PROLOG) and designed to run interactively with students working on course homework and projects. The knowledge-based structure supports intelligent behavior, providing its users with access to an evolving accumulation of expert diagnostic knowledge. The non-procedural approach facilitates the maintenance of the system and helps merge the roles of expert and knowledge engineer by allowing new knowledge to be easily incorporated without regard to the existing flow of control. The background, features and design of the system are describe and preliminary results are reported. Initial success is judged to demonstrate the utility of the reported approach and support the ultimate goal of an intelligent modeling system which can support simulation modelers outside the classroom environment. Finally, future extensions are suggested.« less

Advanced Diagnostic System on Earth Observing One

NASA Technical Reports Server (NTRS)

Hayden, Sandra C.; Sweet, Adam J.; Christa, Scott E.; Tran, Daniel; Shulman, Seth

2004-01-01

In this infusion experiment, the Livingstone 2 (L2) model-based diagnosis engine, developed by the Computational Sciences division at NASA Ames Research Center, has been uploaded to the Earth Observing One (EO-1) satellite. L2 is integrated with the Autonomous Sciencecraft Experiment (ASE) which provides an on-board planning capability and a software bridge to the spacecraft's 1773 data bus. Using a model of the spacecraft subsystems, L2 predicts nominal state transitions initiated by control commands, monitors the spacecraft sensors, and, in the case of failure, isolates the fault based on the discrepant observations. Fault detection and isolation is done by determining a set of component modes, including most likely failures, which satisfy the current observations. All mode transitions and diagnoses are telemetered to the ground for analysis. The initial L2 model is scoped to EO-1's imaging instruments and solid state recorder. Diagnostic scenarios for EO-1's nominal imaging timeline are demonstrated by injecting simulated faults on-board the spacecraft. The solid state recorder stores the science images and also hosts: the experiment software. The main objective of the experiment is to mature the L2 technology to Technology Readiness Level (TRL) 7. Experiment results are presented, as well as a discussion of the challenging technical issues encountered. Future extensions may explore coordination with the planner, and model-based ground operations.

Evolutionary fuzzy modeling human diagnostic decisions.

PubMed

Peña-Reyes, Carlos Andrés

2004-05-01

Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.

Meta-analysis for the comparison of two diagnostic tests to a common gold standard: A generalized linear mixed model approach.

PubMed

Hoyer, Annika; Kuss, Oliver

2018-05-01

Meta-analysis of diagnostic studies is still a rapidly developing area of biostatistical research. Especially, there is an increasing interest in methods to compare different diagnostic tests to a common gold standard. Restricting to the case of two diagnostic tests, in these meta-analyses the parameters of interest are the differences of sensitivities and specificities (with their corresponding confidence intervals) between the two diagnostic tests while accounting for the various associations across single studies and between the two tests. We propose statistical models with a quadrivariate response (where sensitivity of test 1, specificity of test 1, sensitivity of test 2, and specificity of test 2 are the four responses) as a sensible approach to this task. Using a quadrivariate generalized linear mixed model naturally generalizes the common standard bivariate model of meta-analysis for a single diagnostic test. If information on several thresholds of the tests is available, the quadrivariate model can be further generalized to yield a comparison of full receiver operating characteristic (ROC) curves. We illustrate our model by an example where two screening methods for the diagnosis of type 2 diabetes are compared.

Variance Estimation for NAEP Data Using a Resampling-Based Approach: An Application of Cognitive Diagnostic Models. Research Report. ETS RR-10-26

ERIC Educational Resources Information Center

Hsieh, Chueh-an; Xu, Xueli; von Davier, Matthias

2010-01-01

This paper presents an application of a jackknifing approach to variance estimation of ability inferences for groups of students, using a multidimensional discrete model for item response data. The data utilized to demonstrate the approach come from the National Assessment of Educational Progress (NAEP). In contrast to the operational approach…

Using Distractor-Driven Standards-Based Multiple-Choice Assessments and Rasch Modeling to Investigate Hierarchies of Chemistry Misconceptions and Detect Structural Problems with Individual Items

ERIC Educational Resources Information Center

Herrmann-Abell, Cari F.; DeBoer, George E.

2011-01-01

Distractor-driven multiple-choice assessment items and Rasch modeling were used as diagnostic tools to investigate students' understanding of middle school chemistry ideas. Ninety-one items were developed according to a procedure that ensured content alignment to the targeted standards and construct validity. The items were administered to 13360…

Evaluation of the diagnostic potential of ex vivo Raman spectroscopy in gastric cancers: fingerprint versus high wavenumber

NASA Astrophysics Data System (ADS)

Zhou, Xueqian; Dai, Jianhua; Chen, Yao; Duan, Guangjie; Liu, Yulong; Zhang, Hua; Wu, Hongbo; Peng, Guiyong

2016-10-01

The aim of this study was to apply Raman spectroscopy in the high wavenumber (HW) region (2800 to 3000 cm-1) for ex vivo detection of gastric cancer and compare its diagnostic potential with that of the fingerprint (FP) region (800 to 1800 cm-1). Raman spectra were collected in the FP and HW regions to differentiate between normal mucosa (n=38) and gastric cancer (n=37). The distinctive Raman spectral differences between normal and cancer tissues are observed at 853, 879, 1157, 1319, 1338, 1448, and 2932 cm-1 and are primarily related to proteins, lipids, nucleic acids, collagen, and carotenoids in the tissue. In FP and HW Raman spectroscopy for diagnosis of gastric cancer, multivariate diagnostic algorithms based on partial-least-squares discriminant analysis, together with leave-one-sample-out cross validation, yielded diagnostic sensitivities of 94.59% and 81.08%, and specificities of 86.84% and 71.05%, respectively. Receiver operating characteristic analysis further confirmed that the FP region model performance is superior to that of the HW region model. Better differentiation between normal and gastric cancer tissues can be achieved using FP Raman spectroscopy and PLS-DA techniques, but the complementary natures of the FP and HW regions make both of them useful in diagnosis of gastric cancer.

Genetic screening and testing in an episode-based payment model: preserving patient autonomy.

PubMed

Sutherland, Sharon; Farrell, Ruth M; Lockwood, Charles

2014-11-01

The State of Ohio is implementing an episode-based payment model for perinatal care. All costs of care will be tabulated for each live birth and assigned to the delivering provider, creating a three-tiered model for reimbursement for care. Providers will be reimbursed as usual for care that is average in cost and quality, while instituting rewards or penalties for those outside the expected range in either domain. There are few exclusions, and all methods of genetic screening and diagnostic testing are included in the episode cost calculation as proposed. Prenatal ultrasonography, genetic screening, and diagnostic testing are critical components of the delivery of high-quality, evidence-based prenatal care. These tests provide pregnant women with key information about the pregnancy, which, in turn, allows them to work closely with their health care provider to determine optimal prenatal care. The concepts of informed consent and decision-making, cornerstones of the ethical practice of medicine, are founded on the principles of autonomy and respect for persons. These principles recognize that patients' rights to make choices and take actions are based on their personal beliefs and values. Given the personal nature of such decisions, it is critical that patients have unbarred access to prenatal genetic tests if they elect to use them as part of their prenatal care. The proposed restructuring of reimbursement creates a clear conflict between patient autonomy and physician financial incentives.

Safety factor profiles from spectral motional Stark effect for ITER applications

NASA Astrophysics Data System (ADS)

Ko, Jinseok; Chung, Jinil; Wi, Han Min

2017-10-01

Depositions on the first mirror and multiple reflections on the other mirrors in the labyrinth of the optical system in the motional Stark effect (MSE) diagnostic for ITER are regarded as one of the main obstacles to overcome. One of the alternatives to the present-day conventional photoelastic-modulation-based MSE principles is the spectroscopic analyses on the motional Stark emissions where either the ratios among individual Stark multiplets or the amount of the Stark split are measured based on precise and accurate atomic data and models to ultimately provide the critical internal constraints in the magnetic equilibrium reconstruction. Equipped with the PEM-based conventional MSE hardware since 2015, the KSTAR MSE diagnostic system is capable of investigating the feasibility of the spectroscopic MSE approach particularly via comparative studies with the PEM approach. Available atomic data and models are used to analyze the beam emission spectra with a high-spectral-resolution spectrometer with a patent-pending dispersion calibration technology. Experimental validation on the atomic data and models is discussed in association with the effect of the existence of mirrors, the Faraday rotation in the relay optics media, and the background polarized light on the measured spectra. Work supported by the Ministry of Science, ICT and Future Planning, Korea.

The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model.

PubMed

Madison, Matthew J; Bradshaw, Laine P

2015-06-01

Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other multidimensional measurement models. A priori specifications of which latent characteristics or attributes are measured by each item are a core element of the diagnostic assessment design. This item-attribute alignment, expressed in a Q-matrix, precedes and supports any inference resulting from the application of the diagnostic classification model. This study investigates the effects of Q-matrix design on classification accuracy for the log-linear cognitive diagnosis model. Results indicate that classification accuracy, reliability, and convergence rates improve when the Q-matrix contains isolated information from each measured attribute.

Less is more? Assessing the validity of the ICD-11 model of PTSD across multiple trauma samples

PubMed Central

Hansen, Maj; Hyland, Philip; Armour, Cherie; Shevlin, Mark; Elklit, Ask

2015-01-01

Background In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the symptom profile of posttraumatic stress disorder (PTSD) was expanded to include 20 symptoms. An alternative model of PTSD is outlined in the proposed 11th edition of the International Classification of Diseases (ICD-11) that includes just six symptoms. Objectives and method The objectives of the current study are: 1) to independently investigate the fit of the ICD-11 model of PTSD, and three DSM-5-based models of PTSD, across seven different trauma samples (N=3,746) using confirmatory factor analysis; 2) to assess the concurrent validity of the ICD-11 model of PTSD; and 3) to determine if there are significant differences in diagnostic rates between the ICD-11 guidelines and the DSM-5 criteria. Results The ICD-11 model of PTSD was found to provide excellent model fit in six of the seven trauma samples, and tests of factorial invariance showed that the model performs equally well for males and females. DSM-5 models provided poor fit of the data. Concurrent validity was established as the ICD-11 PTSD factors were all moderately to strongly correlated with scores of depression, anxiety, dissociation, and aggression. Levels of association were similar for ICD-11 and DSM-5 suggesting that explanatory power is not affected due to the limited number of items included in the ICD-11 model. Diagnostic rates were significantly lower according to ICD-11 guidelines compared to the DSM-5 criteria. Conclusions The proposed factor structure of the ICD-11 model of PTSD appears valid across multiple trauma types, possesses good concurrent validity, and is more stringent in terms of diagnosis compared to the DSM-5 criteria. PMID:26450830

Less is more? Assessing the validity of the ICD-11 model of PTSD across multiple trauma samples.

PubMed

Hansen, Maj; Hyland, Philip; Armour, Cherie; Shevlin, Mark; Elklit, Ask

2015-01-01

In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the symptom profile of posttraumatic stress disorder (PTSD) was expanded to include 20 symptoms. An alternative model of PTSD is outlined in the proposed 11th edition of the International Classification of Diseases (ICD-11) that includes just six symptoms. The objectives of the current study are: 1) to independently investigate the fit of the ICD-11 model of PTSD, and three DSM-5-based models of PTSD, across seven different trauma samples (N=3,746) using confirmatory factor analysis; 2) to assess the concurrent validity of the ICD-11 model of PTSD; and 3) to determine if there are significant differences in diagnostic rates between the ICD-11 guidelines and the DSM-5 criteria. The ICD-11 model of PTSD was found to provide excellent model fit in six of the seven trauma samples, and tests of factorial invariance showed that the model performs equally well for males and females. DSM-5 models provided poor fit of the data. Concurrent validity was established as the ICD-11 PTSD factors were all moderately to strongly correlated with scores of depression, anxiety, dissociation, and aggression. Levels of association were similar for ICD-11 and DSM-5 suggesting that explanatory power is not affected due to the limited number of items included in the ICD-11 model. Diagnostic rates were significantly lower according to ICD-11 guidelines compared to the DSM-5 criteria. The proposed factor structure of the ICD-11 model of PTSD appears valid across multiple trauma types, possesses good concurrent validity, and is more stringent in terms of diagnosis compared to the DSM-5 criteria.

Perceived risk as a barrier to appropriate diagnosis of irritable bowel syndrome.

PubMed

Ahn, Eunmi; Son, Ki Young; Shin, Dong Wook; Han, Min Kyu; Lee, Hyejin; An, Ah Reum; Kim, Eun Ho; Cho, BeLong

2014-12-28

To evaluate perceived risk, diagnostic testing, and acceptance of a diagnosis of irritable bowel syndrome (IBS) among the Korean laypersons. We designed a conceptual framework to evaluate the health-seeking behavior of subjects based on a knowledge, attitude, and practice model. We developed a vignette-based questionnaire about IBS based on a literature review and focused group interviews. The vignette described a 40-year-old woman who meets the Rome III criteria for IBS without red-flag signs. It was followed by questions about demographic characteristics, health behaviors, IBS symptoms, risk perception, perceived need for diagnostic tests, and acceptance of a positive diagnosis of IBS. We planned a nationwide survey targeting laypersons without IBS and between the ages of 20 and 69 years. Survey participants were selected by quota sampling stratified by gender, age, and nationwide location. A multivariate logistic model was constructed based on literature reviews, univariate analysis, and a stepwise selection method to investigate correlations between the perceived risk, need for diagnostic tests, and acceptance of a positive diagnosis. Of 2354 eligible households, 1000 subjects completed the survey and 983 subjects were analyzed, excluding those who met symptom criteria for IBS. After reading the IBS vignette, the majority of subjects (86.8%) responded that the patient was at increased risk of severe disease. The most frequent concern was colon cancer (59.8%), followed by surgical condition (51.5%). Most subjects responded the patient needs diagnostic tests (97.2%). Colonoscopy was the most commonly required test (79.5%). Less than half of the respondents requested a stool examination (45.0%), blood test (40.7%), abdominal ultrasound (36.0%), or computed tomography (20.2%). The subjects who felt increased risk were more likely to see a need for colonoscopy [adjusted odds ratio (aOR) = 2.10, 95%CI: 1.38-3.18]. When asked about the positive diagnosis, the most frequent response was that "the patient would not be reassured" (65.7%). The increased risk perception group was less likely to be reassured by a positive diagnosis of IBS, compared to the other respondents (aOR = 0.52, 95%CI: 0.34-0.78). For IBS diagnosis, increased risk perception is a possible barrier to the appropriate use of diagnostic tests and to the patient's acceptance of a positive diagnosis.

Proposal of diagnostic process model for computer based diagnosis.

PubMed

Matsumura, Yasushi; Takeda, Toshihiro; Manabe, Shiro; Saito, Hirokazu; Teramoto, Kei; Kuwata, Shigeki; Mihara, Naoki

2012-01-01

We aim at making a diagnosis support system that can be put to practical use. We proposed a diagnostic process model based on simple knowledge which can be gleaned from textbooks. We defined clinical finding (CF) as a general concept for patient's symptom or findings etc., whose value is expressed by Boolean. We call the combination of several CFs a "CF pattern", and a set of CF patterns with concomitant diseases "case base". We consider diagnosis as a process of searching an instance from the case base whose CF pattern is concomitant with that of a patient. The diseases which have the same CF pattern are candidates for diagnosis. Then we select a CF which is present in part of the candidates and check whether it is present or absent in the patient in order to narrow down the candidates. Because the case base does not exist in reality, the probability of CF pattern is calculated by the product of CF occurrence rate assuming that occurrence of CF is independent. Therefore the knowledge required for diagnosis is frequency of disease under sex and age group and CF-disease relation (CF and its occurrence rate in the disease). By processing these two types of knowledge, diagnosis can be made.

How Preclinical Models Evolved to Resemble the Diagnostic Criteria of Drug Addiction.

PubMed

Belin-Rauscent, Aude; Fouyssac, Maxime; Bonci, Antonello; Belin, David

2016-01-01

Drug addiction is a complex neuropsychiatric disorder that affects a subset of the individuals who take drugs. It is characterized by maladaptive drug-seeking habits that are maintained despite adverse consequences and intense drug craving. The pathophysiology and etiology of addiction is only partially understood despite extensive research because of the gap between current preclinical models of addiction and the clinical criteria of the disorder. This review presents a brief overview, based on selected methodologies, of how behavioral models have evolved over the last 50 years to the development of recent preclinical models of addiction that more closely mimic diagnostic criteria of addiction. It is hoped that these new models will increase our understanding of the complex neurobiological mechanisms whereby some individuals switch from controlled drug use to compulsive drug-seeking habits and relapse to these maladaptive habits. Additionally, by paving the way to bridge the gap that exists between biobehavioral research on addiction and the human situation, these models may provide new perspectives for the development of novel and effective therapeutic strategies for drug addiction. Published by Elsevier Inc.