Consistency of Cluster Analysis for Cognitive Diagnosis: The Reduced Reparameterized Unified Model and the General Diagnostic Model.

PubMed

Chiu, Chia-Yi; Köhn, Hans-Friedrich

2016-09-01

The asymptotic classification theory of cognitive diagnosis (ACTCD) provided the theoretical foundation for using clustering methods that do not rely on a parametric statistical model for assigning examinees to proficiency classes. Like general diagnostic classification models, clustering methods can be useful in situations where the true diagnostic classification model (DCM) underlying the data is unknown and possibly misspecified, or the items of a test conform to a mix of multiple DCMs. Clustering methods can also be an option when fitting advanced and complex DCMs encounters computational difficulties. These can range from the use of excessive CPU times to plain computational infeasibility. However, the propositions of the ACTCD have only been proven for the Deterministic Input Noisy Output "AND" gate (DINA) model and the Deterministic Input Noisy Output "OR" gate (DINO) model. For other DCMs, there does not exist a theoretical justification to use clustering for assigning examinees to proficiency classes. But if clustering is to be used legitimately, then the ACTCD must cover a larger number of DCMs than just the DINA model and the DINO model. Thus, the purpose of this article is to prove the theoretical propositions of the ACTCD for two other important DCMs, the Reduced Reparameterized Unified Model and the General Diagnostic Model.

[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.

Plate tectonics in the classification of personality disorder: shifting to a dimensional model.

PubMed

Widiger, Thomas A; Trull, Timothy J

2007-01-01

The diagnostic categories of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders were developed in the spirit of a traditional medical model that considers mental disorders to be qualitatively distinct conditions (see, e.g., American Psychiatric Association, 2000). Work is now beginning on the fifth edition of this influential diagnostic manual. It is perhaps time to consider a fundamental shift in how psychopathology is conceptualized and diagnosed. More specifically, it may be time to consider a shift to a dimensional classification of personality disorder that would help address the failures of the existing diagnostic categories as well as contribute to an integration of the psychiatric diagnostic manual with psychology's research on general personality structure. (c) 2007 APA, all rights reserved

Surveillance system and method having an operating mode partitioned fault classification model

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2005-01-01

A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

Diagnostic Classification Models: Which One Should I Use?

ERIC Educational Resources Information Center

Jiao, Hong

2009-01-01

Diagnostic assessment is currently an active research area in educational measurement. Literature related to diagnostic modeling has been in existence for several decades, but a great deal of research has been conducted within the last decade or so, especially within the last five years. The author summarizes the key components in the application…

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.

Latent Partially Ordered Classification Models and Normal Mixtures

ERIC Educational Resources Information Center

Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith

2013-01-01

Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for…

DIAGNOSTIC TOOL DEVELOPMENT AND APPLICATION THROUGH REGIONAL CASE STUDIES

EPA Science Inventory

Case studies are a useful vehicle for developing and testing conceptual models, classification systems, diagnostic tools and models, and stressor-response relationships. Furthermore, case studies focused on specific places or issues of interest to the Agency provide an excellent ...

CLASSIFICATION FRAMEWORK FOR DIAGNOSTICS RESEARCH

EPA Science Inventory

The goal of Diagnostics Research is to provide tools to simplify diagnosis of the causes of biological impairment, in support of State and Tribe 303(d) impaired waters lists. The Diagnostics Workgroup has developed conceptual models for four major aquatic stressors that cause im...

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.

Personality disorders and the DSM-5: Scientific and extra-scientific factors in the maintenance of the status quo.

PubMed

Gøtzsche-Astrup, Oluf; Moskowitz, Andrew

2016-02-01

The aim of this study was to review and discuss the evidence for dimensional classification of personality disorders and the historical and sociological bases of psychiatric nosology and research. Categorical and dimensional conceptualisations of personality disorder are reviewed, with a focus on the Diagnostic and Statistical Manual of Mental Disorders-system's categorisation and the Five-Factor Model of personality. This frames the events leading up to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, personality disorder debacle, where the implementation of a hybrid model was blocked in a last-minute intervention by the American Psychiatric Association Board of Trustees. Explanations for these events are discussed, including the existence of invisible colleges of researchers and the fear of risking a 'scientific revolution' in psychiatry. A failure to recognise extra-scientific factors at work in classification of mental illness can have a profound and long-lasting influence on psychiatric nosology. In the end it was not scientific factors that led to the failure of the hybrid model of personality disorders, but opposing forces within the mental health community in general and the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Task Force in particular. Substantial evidence has accrued over the past decades in support of a dimensional model of personality disorders. The events surrounding the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Personality and Personality Disorders Work Group show the difficulties in reconciling two different worldviews with a hybrid model. They also indicate the future of a psychiatric nosology that will be increasingly concerned with dimensional classification of mental illness. As such, the road is paved for more substantial changes to personality disorder classification in the International Classification of Diseases, 11th Revision, in 2017. © The Royal Australian and New Zealand College of Psychiatrists 2015.

A Practical Illustration of Multidimensional Diagnostic Skills Profiling: Comparing Results from Confirmatory Factor Analysis and Diagnostic Classification Models

ERIC Educational Resources Information Center

Kunina-Habenicht, Olga; Rupp, Andre A.; Wilhelm, Oliver

2009-01-01

In recent years there has been an increasing international interest in fine-grained diagnostic inferences on multiple skills for formative purposes. A successful provision of such inferences that support meaningful instructional decision-making requires (a) careful diagnostic assessment design coupled with (b) empirical support for the structure…

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.

Plate Tectonics in the Classification of Personality Disorder: Shifting to a Dimensional Model

ERIC Educational Resources Information Center

Widiger, Thomas A.; Trull, Timothy J.

2007-01-01

The diagnostic categories of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders were developed in the spirit of a traditional medical model that considers mental disorders to be qualitatively distinct conditions (see, e.g., American Psychiatric Association, 2000). Work is now beginning on the fifth edition…

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.

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

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.

Automatic analysis and classification of surface electromyography.

PubMed

Abou-Chadi, F E; Nashar, A; Saad, M

2001-01-01

In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.

Data sharing as a national quality improvement program: reporting on BRCA1 and BRCA2 variant-interpretation comparisons through the Canadian Open Genetics Repository (COGR).

PubMed

Lebo, Matthew S; Zakoor, Kathleen-Rose; Chun, Kathy; Speevak, Marsha D; Waye, John S; McCready, Elizabeth; Parboosingh, Jillian S; Lamont, Ryan E; Feilotter, Harriet; Bosdet, Ian; Tucker, Tracy; Young, Sean; Karsan, Aly; Charames, George S; Agatep, Ronald; Spriggs, Elizabeth L; Chisholm, Caitlin; Vasli, Nasim; Daoud, Hussein; Jarinova, Olga; Tomaszewski, Robert; Hume, Stacey; Taylor, Sherryl; Akbari, Mohammad R; Lerner-Ellis, Jordan

2018-03-01

PurposeThe purpose of this study was to develop a national program for Canadian diagnostic laboratories to compare DNA-variant interpretations and resolve discordant-variant classifications using the BRCA1 and BRCA2 genes as a case study.MethodsBRCA1 and BRCA2 variant data were uploaded and shared through the Canadian Open Genetics Repository (COGR; http://www.opengenetics.ca). A total of 5,554 variant observations were submitted; classification differences were identified and comparison reports were sent to participating laboratories. Each site had the opportunity to reclassify variants. The data were analyzed before and after the comparison report process to track concordant- or discordant-variant classifications by three different models.ResultsVariant-discordance rates varied by classification model: 38.9% of variants were discordant when using a five-tier model, 26.7% with a three-tier model, and 5.0% with a two-tier model. After the comparison report process, the proportion of discordant variants dropped to 30.7% with the five-tier model, to 14.2% with the three-tier model, and to 0.9% using the two-tier model.ConclusionWe present a Canadian interinstitutional quality improvement program for DNA-variant interpretations. Sharing of variant knowledge by clinical diagnostic laboratories will allow clinicians and patients to make more informed decisions and lead to better patient outcomes.

Optical Elastography of Systemic Sclerosis Skin

DTIC Science & Technology

2017-09-01

1, the animal model of SSc has been successfully re-established. In addition, animals are being scheduled for the proposed treatment and monitoring...study. 15. SUBJECT TERMS Systemic Sclerosis, Imaging, Skin, Diagnostics, Animal Models, OCT, OCE 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...Diagnostics, Animal Models, OCT, OCE 3.ACCOMPLISHMENTS: o What were the major goals of the project? The goals of Aim 1, as outlined in the SOW were

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

Limited-information goodness-of-fit testing of diagnostic classification item response models.

PubMed

Hansen, Mark; Cai, Li; Monroe, Scott; Li, Zhen

2016-11-01

Despite the growing popularity of diagnostic classification models (e.g., Rupp et al., 2010, Diagnostic measurement: theory, methods, and applications, Guilford Press, New York, NY) in educational and psychological measurement, methods for testing their absolute goodness of fit to real data remain relatively underdeveloped. For tests of reasonable length and for realistic sample size, full-information test statistics such as Pearson's X 2 and the likelihood ratio statistic G 2 suffer from sparseness in the underlying contingency table from which they are computed. Recently, limited-information fit statistics such as Maydeu-Olivares and Joe's (2006, Psychometrika, 71, 713) M 2 have been found to be quite useful in testing the overall goodness of fit of item response theory models. In this study, we applied Maydeu-Olivares and Joe's (2006, Psychometrika, 71, 713) M 2 statistic to diagnostic classification models. Through a series of simulation studies, we found that M 2 is well calibrated across a wide range of diagnostic model structures and was sensitive to certain misspecifications of the item model (e.g., fitting disjunctive models to data generated according to a conjunctive model), errors in the Q-matrix (adding or omitting paths, omitting a latent variable), and violations of local item independence due to unmodelled testlet effects. On the other hand, M 2 was largely insensitive to misspecifications in the distribution of higher-order latent dimensions and to the specification of an extraneous attribute. To complement the analyses of the overall model goodness of fit using M 2 , we investigated the utility of the Chen and Thissen (1997, J. Educ. Behav. Stat., 22, 265) local dependence statistic XLD2 for characterizing sources of misfit, an important aspect of model appraisal often overlooked in favour of overall statements. The XLD2 statistic was found to be slightly conservative (with Type I error rates consistently below the nominal level) but still useful in pinpointing the sources of misfit. Patterns of local dependence arising due to specific model misspecifications are illustrated. Finally, we used the M 2 and XLD2 statistics to evaluate a diagnostic model fit to data from the Trends in Mathematics and Science Study, drawing upon analyses previously conducted by Lee et al., (2011, IJT, 11, 144). © 2016 The British Psychological Society.

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.

School Refusal Behavior: Classification, Assessment, and Treatment Issues.

ERIC Educational Resources Information Center

Lee, Marcella I.; Miltenberger, Raymond G.

1996-01-01

Discusses diagnostic and functional classification, assessment, and treatment approaches for school refusal behavior. Diagnostic classification focuses on separation anxiety disorder, specific phobia, social phobia, depression, and truancy. Functional classification focuses on the maintaining consequences of the behavior, such as avoidance of…

76 FR 69034 - Microbiology Devices; Classification of In Vitro Diagnostic Device for Yersinia Species Detection

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-07

... Drug Administration 21 CFR Part 866 Microbiology Devices; Classification of In Vitro Diagnostic Device... CFR Part 866 [Docket No. FDA-2011-N-0729] Microbiology Devices; Classification of In Vitro Diagnostic... of the Microbiology Devices Advisory Panel (the panel). FDA is publishing in this document the...

Estimating Classification Consistency and Accuracy for Cognitive Diagnostic Assessment

ERIC Educational Resources Information Center

Cui, Ying; Gierl, Mark J.; Chang, Hua-Hua

2012-01-01

This article introduces procedures for the computation and asymptotic statistical inference for classification consistency and accuracy indices specifically designed for cognitive diagnostic assessments. The new classification indices can be used as important indicators of the reliability and validity of classification results produced by…

Diagnostic Classification 0-3: Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood.

ERIC Educational Resources Information Center

Zero to Three: National Center for Infants, Toddlers and Families, Washington, DC.

The diagnostic framework presented in this manual seeks to address the need for a systematic, multi-disciplinary, developmentally based approach to the classification of mental health and developmental difficulties in the first 4 years of life. An introduction discusses clinical approaches to assessment and diagnosis, gives an overview of the…

Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood. Diagnostic Classification: 0-3.

ERIC Educational Resources Information Center

Wieder, Serena, Ed.

The diagnostic framework presented in this manual seeks to address the need for a systematic, multidisciplinary, developmentally based approach to the classification of mental health and developmental difficulties in the first 4 years of life. An introduction discusses clinical approaches to assessment and diagnosis, gives an overview of the…

Effects of pressure ulcer classification system education programme on knowledge and visual differential diagnostic ability of pressure ulcer classification and incontinence-associated dermatitis for clinical nurses in Korea.

PubMed

Lee, Yun Jin; Kim, Jung Yoon

2016-03-01

The objective of this study was to evaluate the effect of pressure ulcer classification system education on clinical nurses' knowledge and visual differential diagnostic ability of pressure ulcer (PU) classification and incontinence-associated dermatitis (IAD). One group pre and post-test was used. A convenience sample of 407 nurses, participating in PU classification education programme of continuing education, were enrolled. The education programme was composed of a 50-minute lecture on PU classification and case-studies. The PU Classification system and IAD knowledge test (PUCS-KT) and visual differential diagnostic ability tool (VDDAT), consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 20.0. The overall mean difference of PUCS-KT (t = -11·437, P<0·001) and VDDAT (t = -21·113, P<0·001) was significantly increased after PU classification education. Overall understanding of six PU classification and IAD after education programme was increased, but lacked visual differential diagnostic ability regarding Stage III PU, suspected deep tissue injury (SDTI), and Unstageable. Continuous differentiated education based on clinical practice is needed to improve knowledge and visual differential diagnostic ability for PU classification, and comparison experiment study is required to examine effects of education programmes. © 2016 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

The role of identity in the DSM-5 classification of personality disorders.

PubMed

Schmeck, Klaus; Schlüter-Müller, Susanne; Foelsch, Pamela A; Doering, Stephan

2013-07-31

In the revised Diagnostic and Statistical Manual DSM-5 the definition of personality disorder diagnoses has not been changed from that in the DSM-IV-TR. However, an alternative model for diagnosing personality disorders where the construct "identity" has been integrated as a central diagnostic criterion for personality disorders has been placed in section III of the manual. The alternative model's hybrid nature leads to the simultaneous use of diagnoses and the newly developed "Level of Personality Functioning-Scale" (a dimensional tool to define the severity of the disorder). Pathological personality traits are assessed in five broad domains which are divided into 25 trait facets. With this dimensional approach, the new classification system gives, both clinicians and researchers, the opportunity to describe the patient in much more detail than previously possible. The relevance of identity problems in assessing and understanding personality pathology is illustrated using the new classification system applied in two case examples of adolescents with a severe personality disorder.

Are there financial savings associated with supplementing current diagnostic practice for preeclampsia with a novel test? Learnings from a modeling analysis from a German payer perspective.

PubMed

Hadker, Nandini; Garg, Suchita; Costanzo, Cory; van der Helm, Wim; Creeden, James

2013-05-01

To quantify the financial impact of adding a novel serum test to the current diagnostic toolkit for preeclampsia (PE) detection in Germany. A decision-analytic model was created to quantify the economic impact of adding a recently developed novel diagnostic test for PE (Roche Diagnostics, Rotkreuz, Switzerland) to current diagnostic practice in Germany. The model simulated a cohort of 1000 pregnant patients receiving obstetric care and quantified the budget impact of adding the novel test to current German PE detection and management practices. The model estimates that the costs associated with managing a typical pregnancy in Germany are €941 when the novel test is used versus €1579 with standard practice. This represents savings of €637 per pregnant woman, even when the test is used as a supplementary diagnostic tool. The savings are attributed to the novel test's ability to better classify patients relative to current practice, specifically, its ability to reduce false negatives by 67% and false positives by 71%. The novel PE test has the potential to provide substantial cost savings to German healthcare payers, even when used as an addition to standard practice. Better classification of patients at risk for developing PE and declassification of those that are not compared to current practice leads to economic savings for the healthcare system. Furthermore, by reducing the rates of false-positive and false-negative classification relative to current standard of care, the test helps better target healthcare spending and lowers overall costs associated with PE care.

Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects.

PubMed

Feder, Stephan; Sundermann, Benedikt; Wersching, Heike; Teuber, Anja; Kugel, Harald; Teismann, Henning; Heindel, Walter; Berger, Klaus; Pfleiderer, Bettina

2017-11-01

Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification. We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained. Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level. It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data. Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinical Diagnosis of Dental Caries in the 21st Century: Introductory Paper - ORCA Saturday Afternoon Symposium, 2016.

PubMed

Machiulskiene, Vita; Carvalho, Joana Christina

2018-03-05

Classifications employed to measure dental caries should first of all reflect the dynamics of the disease, in order to provide a solid basis for subsequent treatment decisions and for further monitoring of dental health of individual patients and populations. The contemporary philosophy of dental caries management implies that nonoperative treatment of caries lesions should be implemented whenever possible, limiting operative interventions to the severe and irreversible cases. The ORCA Saturday Afternoon Symposium 2016, held back-to-back to the 63rd ORCA Congress in Athens, Greece, was intended to provide an update on general requirements for clinical caries diagnosis and to overview caries diagnostic classifications including their rationale, validation, advantages, and limitations. Clinical caries diagnostic criteria and caries management outcomes are interrelated, and any diagnostic classification disregarding this concept is outdated, according to the current understanding of oral health care. Choosing clinical caries diagnostic classifications that assess the activity status of detected lesions should be a priority for dental professionals since these classifications favor the best clinical practice directed towards nonoperative interventions. The choice of clinical caries diagnostic classifications in research, in clinical practice, and in public health services should be guided by the best available scientific evidence. The clinical caries diagnostic classifications should be universally applicable in all these fields. Policy making in oral health care and the underlying policy analyses should follow the same standards. Any clinical caries diagnostic classification disregarding the universality of its use is of limited or no interest in the context of the clinical caries diagnosis of today. © 2018 S. Karger AG, Basel.

Applying a Qualitative Modeling Shell to Process Diagnosis: The Caster System. ONR Technical Report #16.

ERIC Educational Resources Information Center

Thompson, Timothy F.; Clancey, William J.

This report describes the application of a shell expert system from the medical diagnostic system, Neomycin, to Caster, a diagnostic system for malfunctions in industrial sandcasting. This system was developed to test the hypothesis that starting with a well-developed classification procedure and a relational language for stating the…

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.

Attribute-Level and Pattern-Level Classification Consistency and Accuracy Indices for Cognitive Diagnostic Assessment

ERIC Educational Resources Information Center

Wang, Wenyi; Song, Lihong; Chen, Ping; Meng, Yaru; Ding, Shuliang

2015-01-01

Classification consistency and accuracy are viewed as important indicators for evaluating the reliability and validity of classification results in cognitive diagnostic assessment (CDA). Pattern-level classification consistency and accuracy indices were introduced by Cui, Gierl, and Chang. However, the indices at the attribute level have not yet…

Depression and Anxiety Symptoms in Mothers of Newborns Hospitalized on the Neonatal Intensive Care Unit

PubMed Central

Segre, Lisa S.; McCabe, Jennifer E.; Chuffo-Siewert, Rebecca; O’Hara, Michael W.

2014-01-01

Background Mothers of infants hospitalized in the neonatal intensive care unit (NICU) are at risk for clinically significant levels of depression and anxiety symptoms; however, the maternal/infant characteristics that predict risk have been difficult to determine. Previous studies have conceptualized depression and anxiety symptoms separately, ignoring their comorbidity. Moreover, risk factors for these symptoms have not been assessed together in one study sample. Objectives The primary aim of this study was to determine whether a diagnostic classification approach or a common-factor model better explained the pattern of symptoms reported by NICU mothers, including depression, generalized anxiety, panic, and trauma. A secondary aim was to assess risk factors of aversive emotional states in NICU mothers based on the supported conceptual model. Method In this cross-sectional study, a nonprobability convenience sample of 200 NICU mothers completed questionnaires assessing maternal demographic and infant health characteristics, as well as maternal depression and anxiety symptoms. Structural equation modeling was used to test a diagnostic classification model, and a common-factor model of aversive emotional states and the risk factors of aversive emotional states in mothers in the NICU. Results Maximum likelihood estimates indicated that examining symptoms of depression and anxiety disorders as separate diagnostic classifications did not fit the data well, whereas examining the common factor of negative emotionality rendered an adequate fit to the data, and identified a history of depression, infant illness, and infant prematurity as significant risk factors. Discussion This study supports a multidimensional view of depression, and should guide both clinical practice and future research with NICU mothers. PMID:25171558

Influence of Texture and Colour in Breast TMA Classification

PubMed Central

Fernández-Carrobles, M. Milagro; Bueno, Gloria; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial; González-López, Lucía

2015-01-01

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors. PMID:26513238

Five systems of psychiatric classification for preschool children: do differences in validity, usefulness and reliability make for competitive or complimentary constellations?

PubMed

Postert, Christian; Averbeck-Holocher, Marlies; Beyer, Thomas; Müller, Jörg; Furniss, Tilman

2009-03-01

DSM-IV and ICD-10 have limitations in the diagnostic classification of psychiatric disorders at preschool age (0-5 years). The publication of the Diagnostic Classification 0-3 (DC:0-3) in 1994, its basically revised second edition (DC:0-3R) in 2005 and the Research Diagnostic Criteria-Preschool Age (RDC-PA) in 2004 have provided several modifications of these manuals. Taking into account the growing empirical evidence highlighting the need for a diagnostic classification system for psychiatric disorders in preschool children, the main categorical classification systems in preschool psychiatry will be presented and discussed. The paper will focus on issues of validity, usefulness and reliability in DSM-IV, ICD-10, RDC-PA, DC:0-3, and DC:0-3R. The reasons for including or excluding postulated psychiatric disorder categories for preschool children with variable degrees of empirical evidence into the different diagnostic systems will be discussed.

Five Systems of Psychiatric Classification for Preschool Children: Do Differences in Validity, Usefulness and Reliability Make for Competitive or Complimentary Constellations?

ERIC Educational Resources Information Center

Postert, Christian; Averbeck-Holocher, Marlies; Beyer, Thomas; Muller, Jorg; Furniss, Tilman

2009-01-01

"DSM-IV" and "ICD-10" have limitations in the diagnostic classification of psychiatric disorders at preschool age (0-5 years). The publication of the "Diagnostic Classification 0-3 (DC:0-3)" in 1994, its basically revised second edition ("DC:0-3R") in 2005 and the "Research Diagnostic Criteria-Preschool Age (RDC-PA)" in 2004 have provided several…

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.

CHANGING OUR DIAGNOSTIC PARADIGM: MOVEMENT SYSTEM DIAGNOSTIC CLASSIFICATION

PubMed Central

Kamonseki, Danilo H.; Staker, Justin L.; Lawrence, Rebekah L.; Braman, Jonathan P.

2017-01-01

Proper diagnosis is a first step in applying best available treatments, and prognosticating outcomes for clients. Currently, the majority of musculoskeletal diagnoses are classified according to pathoanatomy. However, the majority of physical therapy treatments are applied toward movement system impairments or pain. While advocated within the physical therapy profession for over thirty years, diagnostic classification within a movement system framework has not been uniformly developed or adopted. We propose a basic framework and rationale for application of a movement system diagnostic classification for atraumatic shoulder pain conditions, as a case for the broader development of movement system diagnostic labels. Shifting our diagnostic paradigm has potential to enhance communication, improve educational efficiency, facilitate research, directly link to function, improve clinical care, and accelerate preventive interventions. PMID:29158950

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…

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.

The classification of anxiety and hysterical states. Part I. Historical review and empirical delineation.

PubMed

Sheehan, D V; Sheehan, K H

1982-08-01

The history of the classification of anxiety, hysterical, and hypochondriacal disorders is reviewed. Problems in the ability of current classification schemes to predict, control, and describe the relationship between the symptoms and other phenomena are outlined. Existing classification schemes failed the first test of a good classification model--that of providing categories that are mutually exclusive. The independence of these diagnostic categories from each other does not appear to hold up on empirical testing. In the absence of inherently mutually exclusive categories, further empirical investigation of these classes is obstructed since statistically valid analysis of the nominal data and any useful multivariate analysis would be difficult if not impossible. It is concluded that the existing classifications are unsatisfactory and require some fundamental reconceptualization.

Fitting the Reduced RUM with Mplus: A Tutorial

ERIC Educational Resources Information Center

Chiu, Chia-Yi; Köhn, Hans-Friedrich; Wu, Huey-Min

2016-01-01

The Reduced Reparameterized Unified Model (Reduced RUM) is a diagnostic classification model for educational assessment that has received considerable attention among psychometricians. However, the computational options for researchers and practitioners who wish to use the Reduced RUM in their work, but do not feel comfortable writing their own…

Data-Driven Learning of Q-Matrix

ERIC Educational Resources Information Center

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

Invariance Properties for General Diagnostic Classification Models

ERIC Educational Resources Information Center

Bradshaw, Laine P.; Madison, Matthew J.

2016-01-01

In item response theory (IRT), the invariance property states that item parameter estimates are independent of the examinee sample, and examinee ability estimates are independent of the test items. While this property has long been established and understood by the measurement community for IRT models, the same cannot be said for diagnostic…

Interrelationship between Autism Diagnostic Observation Schedule-Generic (ADOS-G), Autism Diagnostic Interview-Revised (ADI-R), and the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) Classification in Children and Adolescents with Mental Retardation

ERIC Educational Resources Information Center

de Bildt, Annelies; Sytema, Sjoerd; Ketelaars, Cees; Kraijer, Dirk; Mulder, Erik; Volkmar, Fred; Minderaa, Ruud

2004-01-01

The interrelationship between the Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule-Generic (ADOS-G) and clinical classification was studied in 184 children and adolescents with Mental Retardation (MR). The agreement between the ADI-R and ADOS-G was fair, with a substantial difference between younger and older…

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

PubMed

Zeng, Ling-Li; Wang, Huaning; Hu, Panpan; Yang, Bo; Pu, Weidan; Shen, Hui; Chen, Xingui; Liu, Zhening; Yin, Hong; Tan, Qingrong; Wang, Kai; Hu, Dewen

2018-04-01

A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Copyright © 2018 German Center for Neurodegenerative Diseases (DZNE). Published by Elsevier B.V. All rights reserved.

Importance of Proper Utilization of International Classification of Diseases 10th Revision and Clinical Documentation in Modern Payment Models.

PubMed

Nichols, Joseph C; Osmani, Feroz A; Sayeed, Yousuf

2016-05-01

Health care payment models are changing rapidly, and the measurement of outcomes and costs is increasing. With the implementation of International Classification of Diseases 10th revision (ICD-10) codes, providers now have the ability to introduce a precise array of diagnoses for their patients. More specific diagnostic codes do not eliminate the potential for vague application, as was seen with the utility of ICD-9. Complete, accurate, and consistent data that reflect the risk, severity, and complexity of care are becoming critically important in this new environment. Orthopedic specialty organizations must be actively involved in influencing the definition of value and risk in the patient population. Now is the time to use the ICD-10 diagnostic codes to improve the management of patient conditions in data. Copyright © 2016 Elsevier Inc. All rights reserved.

Clashing Diagnostic Approaches: DSM-ICD versus RDoC

PubMed Central

Lilienfeld, Scott O.; Treadway, Michael T.

2016-01-01

Since at least the middle of the past century, one overarching model of psychiatric classification, namely, that of the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases (DSM-ICD), has reigned supreme. This DSM-ICD approach embraces an Aristotelian view of mental disorders as largely discrete entities that are characterized by distinctive signs, symptoms, and natural histories. Over the past several years, however, a competing vision, namely, the Research Domain Criteria (RDoC) initiative launched by the National Institute of Mental Health, has emerged in response to accumulating anomalies within the DSM-ICD system. In contrast to DSM-ICD, RDoC embraces a Galilean view of psychopathology as the product of dysfunctions in neural circuitry. RDoC appears to be a valuable endeavor that holds out the long-term promise of an alternative system of mental illness classification. We delineate three sets of pressing challenges – conceptual, methodological, and logistical/pragmatic – that must be addressed for RDoC to realize its scientific potential, and conclude with a call for further research, including investigation of a rapprochement between Aristotelian and Galilean approaches to psychiatric classification. PMID:26845519

The system of technical diagnostics of the industrial safety information network

NASA Astrophysics Data System (ADS)

Repp, P. V.

2017-01-01

This research is devoted to problems of safety of the industrial information network. Basic sub-networks, ensuring reliable operation of the elements of the industrial Automatic Process Control System, were identified. The core tasks of technical diagnostics of industrial information safety were presented. The structure of the technical diagnostics system of the information safety was proposed. It includes two parts: a generator of cyber-attacks and the virtual model of the enterprise information network. The virtual model was obtained by scanning a real enterprise network. A new classification of cyber-attacks was proposed. This classification enables one to design an efficient generator of cyber-attacks sets for testing the virtual modes of the industrial information network. The numerical method of the Monte Carlo (with LPτ - sequences of Sobol), and Markov chain was considered as the design method for the cyber-attacks generation algorithm. The proposed system also includes a diagnostic analyzer, performing expert functions. As an integrative quantitative indicator of the network reliability the stability factor (Kstab) was selected. This factor is determined by the weight of sets of cyber-attacks, identifying the vulnerability of the network. The weight depends on the frequency and complexity of cyber-attacks, the degree of damage, complexity of remediation. The proposed Kstab is an effective integral quantitative measure of the information network reliability.

Diagnostic Classification of Children within the Educational System: Should It Be Eliminated?

ERIC Educational Resources Information Center

Kuther, Tara L.

This paper reviews the literature on positive and negative aspects of diagnostic classification of children, or labeling. It reviews the Education for All Handicapped Children Act, which mandated that special education must be available to all children with disabilities. Definitional issues in diagnostic labeling of students with learning…

[Generalized anxiety disorder, now and the future: a perspective to the DSM-5].

PubMed

Otsubo, Tempei

2012-01-01

Generalized, persistent, and free-floating anxiety was first described by Freud in 1894. The diagnostic term generalized anxiety disorder (GAD) was not in classification systems until the publication of the diagnostic and statistical manual for mental disorders, third edition (DSM-III) in 1980. Initially considered as a residual category to be used when no other diagnosis could be made, it is not accepted that GAD represents a distinct diagnostic category yet. Since 1980, revisions to the diagnostic criteria for GAD in the DSM-III-R, DSM-IV and DSM-5 classifications have slightly redefined this disorder. The classification is fluid. The duration criterion has increased to 6 months in DSM-IV, but decreased to 3 months in DSM-5. This article reviews the development of diagnostic criteria for defining GAD from Freud to DSM-5 and compares the DSM-5 criterion with DSM-IV and the tenth revision of the International Classification of Disease. The impact of the changes in diagnostic criteria on research into GAD, and on diagnosis, differential diagnosis, will be discussed.

ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines

DTIC Science & Technology

2014-05-16

ML-o-scope: a diagnostic visualization system for deep machine learning pipelines Daniel Bruckner Electrical Engineering and Computer Sciences... machine learning pipelines 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f...the system as a support for tuning large scale object-classification pipelines. 1 Introduction A new generation of pipelined machine learning models

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.

On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2011-01-01

Cognitive diagnostic models (CDMs) attempt to uncover latent skills or attributes that examinees must possess in order to answer test items correctly. The DINA (deterministic input, noisy "and") model is a popular CDM that has been widely used. It is shown here that a logistic version of the model can easily be fit with standard software for…

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 of rheumatoid arthritis: multivariate analysis of biomarkers.

PubMed

Wild, Norbert; Karl, Johann; Grunert, Veit P; Schmitt, Raluca I; Garczarek, Ursula; Krause, Friedemann; Hasler, Fritz; van Riel, Piet L C M; Bayer, Peter M; Thun, Matthias; Mattey, Derek L; Sharif, Mohammed; Zolg, Werner

2008-02-01

To test if a combination of biomarkers can increase the classification power of autoantibodies to cyclic citrullinated peptides (anti-CCP) in the diagnosis of rheumatoid arthritis (RA) depending on the diagnostic situation. Biomarkers were subject to three inclusion/exclusion criteria (discrimination between RA patients and healthy blood donors, ability to identify anti-CCP-negative RA patients, specificity in a panel with major non-rheumatological diseases) before univariate ranking and multivariate analysis was carried out using a modelling panel (n = 906). To enable the evaluation of the classification power in different diagnostic settings the disease controls (n = 542) were weighted according to the admission rates in rheumatology clinics modelling a clinic panel or according to the relative prevalences of musculoskeletal disorders in the general population seen by general practitioners modelling a GP panel. Out of 131 biomarkers considered originally, we evaluated 32 biomarkers in this study, of which only seven passed the three inclusion/exclusion criteria and were combined by multivariate analysis using four different mathematical models. In the modelled clinic panel, anti-CCP was the lead marker with a sensitivity of 75.8% and a specificity of 94.0%. Due to the lack in specificity of the markers other than anti-CCP in this diagnostic setting, any gain in sensitivity by any marker combination is off-set by a corresponding loss in specificity. In the modelled GP panel, the best marker combination of anti-CCP and interleukin (IL)-6 resulted in a sensitivity gain of 7.6% (85.9% vs. 78.3%) at a minor loss in specificity of 1.6% (90.3% vs. 91.9%) compared with anti-CCP as the best single marker. Depending on the composition of the sample panel, anti-CCP alone or anti-CCP in combination with IL-6 has the highest classification power for the diagnosis of established RA.

Criteria for solvent-induced chronic toxic encephalopathy: a systematic review.

PubMed

van der Hoek, J A; Verberk, M M; Hageman, G

2000-08-01

In 1985, a WHO Working Group presented diagnostic criteria and a classification for solvent-induced chronic toxic encephalopathy (CTE). In the same year, the "Workshop on neurobehavioral effects of solvents" in Raleigh, N.C., USA introduced a somewhat different classification for CTE. The objective of this review is to study the diagnostic procedures that are used to establish the diagnosis of CTE, and the extent to which the diagnostic criteria and classification of the WHO, and the classification of the Raleigh Working Group, are applied. A systematic search of studies on CTE was performed, and the diagnostic criteria and use of the WHO and Raleigh classifications were listed. We retrieved 30 original articles published in English from 1985 to 1998, in which CTE was diagnosed. Only two articles did not report the duration of solvent exposure. The type of solvent(s) involved was described in detail in four articles, poorly in 17 articles, and not at all in nine articles. Tests of general intelligence were used in 19 articles, and tests of both attention and mental flexibility and of learning and memory were used in 18 articles. Exclusion, by interview, of potentially confounding conditions, such as somatic diseases with central nervous effects and psychiatric diseases, was reported in 21 and 16 articles, respectively. In only six of the articles were both the WHO diagnostic criteria and the WHO or Raleigh classifications used. In the future, parameters of exposure, psychological test results, and use of medication that possibly affects psychological test results should always be described. We list some advantages and disadvantages of the Raleigh and WHO classifications. To aid inter-study comparisons, the diagnosis of CTE should be categorized and reported according to an internationally accepted classification.

The role of identity in the DSM-5 classification of personality disorders

PubMed Central

2013-01-01

In the revised Diagnostic and Statistical Manual DSM-5 the definition of personality disorder diagnoses has not been changed from that in the DSM-IV-TR. However, an alternative model for diagnosing personality disorders where the construct “identity” has been integrated as a central diagnostic criterion for personality disorders has been placed in section III of the manual. The alternative model’s hybrid nature leads to the simultaneous use of diagnoses and the newly developed “Level of Personality Functioning-Scale” (a dimensional tool to define the severity of the disorder). Pathological personality traits are assessed in five broad domains which are divided into 25 trait facets. With this dimensional approach, the new classification system gives, both clinicians and researchers, the opportunity to describe the patient in much more detail than previously possible. The relevance of identity problems in assessing and understanding personality pathology is illustrated using the new classification system applied in two case examples of adolescents with a severe personality disorder. PMID:23902698

A new classification system for lesbians: the Dyke Diagnostic Manual.

PubMed

Eliason, Michele J

2010-01-01

There has been a long-standing need for a diagnostic manual that documents the unique pathological behaviors of lesbians. The Dyke Diagnostic Manual (DDM) is meant to supplement mainstream classification systems used to identify problematic behaviors in heterosexuals. This article presents thirteen uniquely lesbian conditions that are nowhere to be found in heterosexist diagnostic systems. The DDM may help to reduce the pain and suffering found in many lesbian relationships where one or both partners are afflicted.

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

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Translation and integration of CCC nursing diagnoses into ICNP.

PubMed

Matney, Susan A; DaDamio, Rebecca; Couderc, Carmela; Dlugos, Mary; Evans, Jonathan; Gianonne, Gay; Haskell, Robert; Hardiker, Nicholas; Coenen, Amy; Saba, Virginia K

2008-01-01

The purpose of this study was to translate and integrate nursing diagnosis concepts from the Clinical Care Classification (CCC) System Version 2.0 to DiagnosticPhenomenon or nursing diagnostic statements in the International Classification for Nursing Practice (ICNP) Version 1.0. Source concepts for CCC were mapped by the project team, where possible, to pre-coordinated ICNP terms. The manual decomposition of source concepts according to the ICNP 7-Axis Model served to validate the mappings. A total of 62% of the CCC Nursing Diagnoses were a pre-coordinated match to an ICNP concept, 35% were a post-coordinated match and only 3% had no match. During the mapping process, missing CCC concepts were submitted to the ICNP Programme, with a recommendation for inclusion in future releases.

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.

The Use and Abuse of Diagnostic/Classification Criteria

PubMed Central

June, Rayford R.; Aggarwal, Rohit

2015-01-01

In rheumatic diseases, classification criteria have been developed to identify well-defined homogenous cohorts for clinical research. Although, they are commonly used in clinical practice, their use may not be appropriate for routine diagnostic clinical care. Classification criteria are being revised with improved methodology and further understanding of disease pathophysiology, but still may not encompass all unique clinical situations to be applied for diagnosis of heterogeneous, rare, evolving rheumatic diseases. Diagnostic criteria development is challenging primarily due to difficulty for universal application given significant differences in prevalence of rheumatic diseases based on geographical area and clinic settings. Despite these shortcomings, the clinician can still use classification criteria for understanding the disease as well as a guide for diagnosis with a few caveats. We present the limits of current classification criteria, describe their use and abuse in clinical practice, and how they should be used with caution when applied in clinics. PMID:26096094

Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography.

PubMed

Sugimoto, Katsutoshi; Shiraishi, Junji; Moriyasu, Fuminori; Doi, Kunio

2009-04-01

To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.

Rapid identification and classification of Mycobacterium spp. using whole-cell protein barcodes with matrix assisted laser desorption ionization time of flight mass spectrometry in comparison with multigene phylogenetic analysis.

PubMed

Wang, Jun; Chen, Wen Feng; Li, Qing X

2012-02-24

The need of quick diagnostics and increasing number of bacterial species isolated necessitate development of a rapid and effective phenotypic identification method. Mass spectrometry (MS) profiling of whole cell proteins has potential to satisfy the requirements. The genus Mycobacterium contains more than 154 species that are taxonomically very close and require use of multiple genes including 16S rDNA for phylogenetic identification and classification. Six strains of five Mycobacterium species were selected as model bacteria in the present study because of their 16S rDNA similarity (98.4-99.8%) and the high similarity of the concatenated 16S rDNA, rpoB and hsp65 gene sequences (95.9-99.9%), requiring high identification resolution. The classification of the six strains by MALDI TOF MS protein barcodes was consistent with, but at much higher resolution than, that of the multi-locus sequence analysis of using 16S rDNA, rpoB and hsp65. The species were well differentiated using MALDI TOF MS and MALDI BioTyper™ software after quick preparation of whole-cell proteins. Several proteins were selected as diagnostic markers for species confirmation. An integration of MALDI TOF MS, MALDI BioTyper™ software and diagnostic protein fragments provides a robust phenotypic approach for bacterial identification and classification. Copyright © 2011 Elsevier B.V. All rights reserved.

Minding the body: situating gender identity diagnoses in the ICD-11.

PubMed

Drescher, Jack; Cohen-Kettenis, Peggy; Winter, Sam

2012-12-01

The World Health Organization (WHO) is in the process of revising the International Statistical Classification of Diseases and Related Health Problems (ICD) and ICD-11 has an anticipated publication date of 2015. The Working Group on the Classification of Sexual Disorders and Sexual Health (WGSDSH) is charged with evaluating clinical and research data to inform the revision of diagnostic categories related to sexuality and gender identity that are currently included in the mental and behavioural disorders chapter of ICD-10, and making initial recommendations regarding whether and how these categories should be represented in the ICD-11. The diagnostic classification of disorders related to (trans)gender identity is an area long characterized by lack of knowledge, misconceptions and controversy. The placement of these categories has shifted over time within both the ICD and the American Psychiatric Association's Diagnostic and Statistical Manual (DSM), reflecting developing views about what to call these diagnoses, what they mean and where to place them. This article reviews several controversies generated by gender identity diagnoses in recent years. In both the ICD-11 and DSM-5 development processes, one challenge has been to find a balance between concerns related to the stigmatization of mental disorders and the need for diagnostic categories that facilitate access to healthcare. In this connection, this article discusses several human rights issues related to gender identity diagnoses, and explores the question of whether affected populations are best served by placement of these categories within the mental disorders section of the classification. The combined stigmatization of being transgender and of having a mental disorder diagnosis creates a doubly burdensome situation for this group, which may contribute adversely to health status and to the attainment and enjoyment of human rights. The ICD-11 Working Group on the Classification of Sexual Disorders and Sexual Health believes it is now appropriate to abandon a psychopathological model of transgender people based on 1940s conceptualizations of sexual deviance and to move towards a model that is (1) more reflective of current scientific evidence and best practices; (2) more responsive to the needs, experience, and human rights of this vulnerable population; and (3) more supportive of the provision of accessible and high-quality healthcare services.

Using Response Times to Assess Learning Progress: A Joint Model for Responses and Response Times

ERIC Educational Resources Information Center

Wang, Shiyu; Zhang, Susu; Douglas, Jeff; Culpepper, Steven

2018-01-01

Analyzing students' growth remains an important topic in educational research. Most recently, Diagnostic Classification Models (DCMs) have been used to track skill acquisition in a longitudinal fashion, with the purpose to provide an estimate of students' learning trajectories in terms of the change of fine-grained skills overtime. Response time…

A practicable approach for periodontal classification

PubMed Central

Mittal, Vishnu; Bhullar, Raman Preet K.; Bansal, Rachita; Singh, Karanprakash; Bhalodi, Anand; Khinda, Paramjit K.

2013-01-01

The Diagnosis and classification of periodontal diseases has remained a dilemma since long. Two distinct concepts have been used to define diseases: Essentialism and Nominalism. Essentialistic concept implies the real existence of disease whereas; nominalistic concept states that the names of diseases are the convenient way of stating concisely the endpoint of a diagnostic process. It generally advances from assessment of symptoms and signs toward knowledge of causation and gives a feasible option to name the disease for which etiology is either unknown or it is too complex to access in routine clinical practice. Various classifications have been proposed by the American Academy of Periodontology (AAP) in 1986, 1989 and 1999. The AAP 1999 classification is among the most widely used classification. But this classification also has demerits which provide impediment for its use in day to day practice. Hence a classification and diagnostic system is required which can help the clinician to access the patient's need and provide a suitable treatment which is in harmony with the diagnosis for that particular case. Here is an attempt to propose a practicable classification and diagnostic system of periodontal diseases for better treatment outcome. PMID:24379855

Pathohistological classification systems in gastric cancer: Diagnostic relevance and prognostic value

PubMed Central

Berlth, Felix; Bollschweiler, Elfriede; Drebber, Uta; Hoelscher, Arnulf H; Moenig, Stefan

2014-01-01

Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer. PMID:24914328

A tutorial on the use of ROC analysis for computer-aided diagnostic systems.

PubMed

Scheipers, Ulrich; Perrey, Christian; Siebers, Stefan; Hansen, Christian; Ermert, Helmut

2005-07-01

The application of the receiver operating characteristic (ROC) curve for computer-aided diagnostic systems is reviewed. A statistical framework is presented and different methods of evaluating the classification performance of computer-aided diagnostic systems, and, in particular, systems for ultrasonic tissue characterization, are derived. Most classifiers that are used today are dependent on a separation threshold, which can be chosen freely in many cases. The separation threshold separates the range of output values of the classification system into different target groups, thus conducting the actual classification process. In the first part of this paper, threshold specific performance measures, e.g., sensitivity and specificity, are presented. In the second part, a threshold-independent performance measure, the area under the ROC curve, is reviewed. Only the use of separation threshold-independent performance measures provides classification results that are overall representative for computer-aided diagnostic systems. The following text was motivated by the lack of a complete and definite discussion of the underlying subject in available textbooks, references and publications. Most manuscripts published so far address the theme of performance evaluation using ROC analysis in a manner too general to be practical for everyday use in the development of computer-aided diagnostic systems. Nowadays, the user of computer-aided diagnostic systems typically handles huge amounts of numerical data, not always distributed normally. Many assumptions made in more or less theoretical works on ROC analysis are no longer valid for real-life data. The paper aims at closing the gap between theoretical works and real-life data. The review provides the interested scientist with information needed to conduct ROC analysis and to integrate algorithms performing ROC analysis into classification systems while understanding the basic principles of classification.

What is generalized anxiety disorder?

PubMed

Rickels, K; Rynn, M A

2001-01-01

Generalized, persistent, and free-floating anxiety was first described by Freud in 1894, although the diagnostic term generalized anxiety disorder (GAD) was not included in classification systems until 1980 (Diagnostic and Statistical Manual for Mental Disorders, Third Edition [DSM-III]). Initially considered a residual category to be used when no other diagnosis could be made, it is now widely accepted that GAD represents a distinct diagnostic category. Since 1980, revisions to the diagnostic criteria for GAD in the DSM-III-R and DSM-IV classifications have markedly redefined this disorder, increasing the duration criterion to 6 months and increasing the emphasis on worry and psychic symptoms. This article reviews the development of the diagnostic criteria for defining GAD from Freud to DSM-IV and compares the DSM-IV criteria with the criteria set forth in the tenth revision of the International Classification of Diseases. The impact of the changes in diagnostic criteria on research into GAD, and on diagnosis, differential diagnosis, and treatment of GAD, will be discussed.

An evidence-based diagnostic classification system for low back pain

PubMed Central

Vining, Robert; Potocki, Eric; Seidman, Michael; Morgenthal, A. Paige

2013-01-01

Introduction: While clinicians generally accept that musculoskeletal low back pain (LBP) can arise from specific tissues, it remains difficult to confirm specific sources. Methods: Based on evidence supported by diagnostic utility studies, doctors of chiropractic functioning as members of a research clinic created a diagnostic classification system, corresponding exam and checklist based on strength of evidence, and in-office efficiency. Results: The diagnostic classification system contains one screening category, two pain categories: Nociceptive, Neuropathic, one functional evaluation category, and one category for unknown or poorly defined diagnoses. Nociceptive and neuropathic pain categories are each divided into 4 subcategories. Conclusion: This article describes and discusses the strength of evidence surrounding diagnostic categories for an in-office, clinical exam and checklist tool for LBP diagnosis. The use of a standardized tool for diagnosing low back pain in clinical and research settings is encouraged. PMID:23997245

Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region

NASA Astrophysics Data System (ADS)

Pastor, M. A.; Casado, M. J.

2012-10-01

This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes.

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.

[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.

GENE-07. MOLECULAR NEUROPATHOLOGY 2.0 - INCREASING DIAGNOSTIC ACCURACY IN PEDIATRIC NEUROONCOLOGY

PubMed Central

Sturm, Dominik; Jones, David T.W.; Capper, David; Sahm, Felix; von Deimling, Andreas; Rutkoswki, Stefan; Warmuth-Metz, Monika; Bison, Brigitte; Gessi, Marco; Pietsch, Torsten; Pfister, Stefan M.

2017-01-01

Abstract The classification of central nervous system (CNS) tumors into clinically and biologically distinct entities and subgroups is challenging. Children and adolescents can be affected by >100 histological variants with very variable outcomes, some of which are exceedingly rare. The current WHO classification has introduced a number of novel molecular markers to aid routine neuropathological diagnostics, and DNA methylation profiling is emerging as a powerful tool to distinguish CNS tumor classes. The Molecular Neuropathology 2.0 study aims to integrate genome wide (epi-)genetic diagnostics with reference neuropathological assessment for all newly-diagnosed pediatric brain tumors in Germany. To date, >350 patients have been enrolled. A molecular diagnosis is established by epigenetic tumor classification through DNA methylation profiling and targeted panel sequencing of >130 genes to detect diagnostically and/or therapeutically useful DNA mutations, structural alterations, and fusion events. Results are aligned with the reference neuropathological diagnosis, and discrepant findings are discussed in a multi-disciplinary tumor board including reference neuroradiological evaluation. Ten FFPE sections as input material are sufficient to establish a molecular diagnosis in >95% of tumors. Alignment with reference pathology results in four broad categories: a) concordant classification (~77%), b) discrepant classification resolvable by tumor board discussion and/or additional data (~5%), c) discrepant classification without currently available options to resolve (~8%), and d) cases currently unclassifiable by molecular diagnostics (~10%). Discrepancies are enriched in certain histopathological entities, such as histological high grade gliomas with a molecularly low grade profile. Gene panel sequencing reveals predisposing germline events in ~10% of patients. Genome wide (epi-)genetic analyses add a valuable layer of information to routine neuropathological diagnostics. Our study provides insight into CNS tumors with divergent histopathological and molecular classification, opening new avenues for research discoveries and facilitating optimization of clinical management for affected patients in the future.

Psychologists’ perspectives on the diagnostic classification of mental disorders: Results from the WHO-IUPsyS Global Survey

PubMed Central

Evans, Spencer C.; Reed, Geoffrey M.; Roberts, Michael C.; Esparza, Patricia; Watts, Ann D.; Correia, João Mendonça; Ritchie, Pierre; Maj, Mario; Saxena, Shekhar

2013-01-01

This study examined psychologists’ views and practices regarding diagnostic classification systems for mental and behavioral disorders so as to inform the development of the ICD-11 by the World Health Organization (WHO). WHO and the International Union of Psychological Science (IUPsyS) conducted a multilingual survey of 2155 psychologists from 23 countries, recruited through their national psychological associations. Sixty percent of global psychologists routinely used a formal classification system, with ICD-10 used most frequently by 51% and DSM-IV by 44%. Psychologists viewed informing treatment decisions and facilitating communication as the most important purposes of classification, and preferred flexible diagnostic guidelines to strict criteria. Clinicians favorably evaluated most diagnostic categories, but identified a number of problematic diagnoses. Substantial percentages reported problems with crosscultural applicability and cultural bias, especially among psychologists outside the USA and Europe. Findings underscore the priority of clinical utility and professional and cultural differences in international psychology. Implications for ICD-11 development and dissemination are discussed. PMID:23750927

Meeting the criteria of a nursing diagnosis classification: Evaluation of ICNP, ICF, NANDA and ZEFP.

PubMed

Müller-Staub, Maria; Lavin, Mary Ann; Needham, Ian; van Achterberg, Theo

2007-07-01

Few studies described nursing diagnosis classification criteria and how classifications meet these criteria. The purpose was to identify criteria for nursing diagnosis classifications and to assess how these criteria are met by different classifications. First, a literature review was conducted (N=50) to identify criteria for nursing diagnoses classifications and to evaluate how these criteria are met by the International Classification of Nursing Practice (ICNP), the International Classification of Functioning, Disability and Health (ICF), the International Nursing Diagnoses Classification (NANDA), and the Nursing Diagnostic System of the Centre for Nursing Development and Research (ZEFP). Using literature review based general and specific criteria, the principal investigator evaluated each classification, applying a matrix. Second, a convenience sample of 20 nursing experts from different Swiss care institutions answered standardized interview forms, querying current national and international classification state and use. The first general criterion is that a diagnosis classification should describe the knowledge base and subject matter for which the nursing profession is responsible. ICNP) and NANDA meet this goal. The second general criterion is that each class fits within a central concept. The ICF and NANDA are the only two classifications built on conceptually driven classes. The third general classification criterion is that each diagnosis possesses a description, diagnostic criteria, and related etiologies. Although ICF and ICNP describe diagnostic terms, only NANDA fulfils this criterion. The analysis indicated that NANDA fulfilled most of the specific classification criteria in the matrix. The nursing experts considered NANDA to be the best-researched and most widely implemented classification in Switzerland and internationally. The international literature and the opinion of Swiss expert nurses indicate that-from the perspective of classifying comprehensive nursing diagnoses-NANDA should be recommended for nursing practice and electronic nursing documentation. Study limitations and future research needs are discussed.

Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach.

PubMed

Wu, Mon-Ju; Wu, Hanjing Emily; Mwangi, Benson; Sanches, Marsal; Selvaraj, Sudhakar; Zunta-Soares, Giovana B; Soares, Jair C

2015-03-01

Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment - without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level. We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was 'trained' to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated. The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls. These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Review of current classification and terminology of vulvar disorders].

PubMed

Sláma, J

2012-08-01

To summarize current terminology and classification of vulvar disorders. Review article. Gynecologic oncology center, Department of Gynecology and Obstetrics, General Faculty Hospital and 1st Medical School of Charles University, Prague. Vulvar disorders include wide spectrum of different diagnoses. Multidisciplinary collaboration is frequently needed in diagnostical and therapeutical process. It is essential to use unified terminology using standard dermatological terms, and unified classification for comprehensible communication between different medical professions. Current classification, which is based on Clinical-pathological criteria, was established by International Society for the Study of Vulvovaginal Disease. Recently, there was introduced Clinical classification, which groups disorders according to main morphological finding. Adequate and unified classification and terminology are necessary for effective communication during the diagnostical process.

Verification and classification bias interactions in diagnostic test accuracy studies for fine-needle aspiration biopsy.

PubMed

Schmidt, Robert L; Walker, Brandon S; Cohen, Michael B

2015-03-01

Reliable estimates of accuracy are important for any diagnostic test. Diagnostic accuracy studies are subject to unique sources of bias. Verification bias and classification bias are 2 sources of bias that commonly occur in diagnostic accuracy studies. Statistical methods are available to estimate the impact of these sources of bias when they occur alone. The impact of interactions when these types of bias occur together has not been investigated. We developed mathematical relationships to show the combined effect of verification bias and classification bias. A wide range of case scenarios were generated to assess the impact of bias components and interactions on total bias. Interactions between verification bias and classification bias caused overestimation of sensitivity and underestimation of specificity. Interactions had more effect on sensitivity than specificity. Sensitivity was overestimated by at least 7% in approximately 6% of the tested scenarios. Specificity was underestimated by at least 7% in less than 0.1% of the scenarios. Interactions between verification bias and classification bias create distortions in accuracy estimates that are greater than would be predicted from each source of bias acting independently. © 2014 American Cancer Society.

Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression.

PubMed

Sundermann, Benedikt; Olde Lütke Beverborg, Mona; Pfleiderer, Bettina

2014-01-01

Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD, primarily to serve as feature selection for multivariate pattern analysis techniques (MVPA). Thirty two studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components including the precuneus and neighboring posterior cingulate cortices associated with self-referential processing and the subgenual anterior cingulate and neighboring medial frontal cortices) with lateral prefrontal areas related to externally-directed cognition. Other areas of hyperactivity/hyperconnectivity include the left lateral parietal cortex, right hippocampus and right cerebellum whereas hypoactivity/hypoconnectivity was observed mainly in the left temporal cortex, the insula, precuneus, superior frontal gyrus, lentiform nucleus and thalamus. Results are made available in two different data formats to be used as spatial hypotheses in future studies, particularly for diagnostic classification by MVPA.

A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography.

PubMed

Baltzer, Pascal A T; Dietzel, Matthias; Kaiser, Werner A

2013-08-01

In the face of multiple available diagnostic criteria in MR-mammography (MRM), a practical algorithm for lesion classification is needed. Such an algorithm should be as simple as possible and include only important independent lesion features to differentiate benign from malignant lesions. This investigation aimed to develop a simple classification tree for differential diagnosis in MRM. A total of 1,084 lesions in standardised MRM with subsequent histological verification (648 malignant, 436 benign) were investigated. Seventeen lesion criteria were assessed by 2 readers in consensus. Classification analysis was performed using the chi-squared automatic interaction detection (CHAID) method. Results include the probability for malignancy for every descriptor combination in the classification tree. A classification tree incorporating 5 lesion descriptors with a depth of 3 ramifications (1, root sign; 2, delayed enhancement pattern; 3, border, internal enhancement and oedema) was calculated. Of all 1,084 lesions, 262 (40.4 %) and 106 (24.3 %) could be classified as malignant and benign with an accuracy above 95 %, respectively. Overall diagnostic accuracy was 88.4 %. The classification algorithm reduced the number of categorical descriptors from 17 to 5 (29.4 %), resulting in a high classification accuracy. More than one third of all lesions could be classified with accuracy above 95 %. • A practical algorithm has been developed to classify lesions found in MR-mammography. • A simple decision tree consisting of five criteria reaches high accuracy of 88.4 %. • Unique to this approach, each classification is associated with a diagnostic certainty. • Diagnostic certainty of greater than 95 % is achieved in 34 % of all cases.

Initial interpretation and evaluation of a profile-based classification system for the anxiety and mood disorders: Incremental validity compared to DSM-IV categories.

PubMed

Rosellini, Anthony J; Brown, Timothy A

2014-12-01

Limitations in anxiety and mood disorder diagnostic reliability and validity due to the categorical approach to classification used by the Diagnostic and Statistical Manual of Mental Disorders (DSM) have been long recognized. Although these limitations have led researchers to forward alternative classification schemes, few have been empirically evaluated. In a sample of 1,218 outpatients with anxiety and mood disorders, the present study examined the validity of Brown and Barlow's (2009) proposal to classify the anxiety and mood disorders using an integrated dimensional-categorical approach based on transdiagnostic emotional disorder vulnerabilities and phenotypes. Latent class analyses of 7 transdiagnostic dimensional indicators suggested that a 6-class (i.e., profile) solution provided the best model fit and was the most conceptually interpretable. Interpretation of the classes was further supported when compared with DSM diagnoses (i.e., within-class prevalence of diagnoses, using diagnoses to predict class membership). In addition, hierarchical multiple regression models were used to demonstrate the incremental validity of the profiles; class probabilities consistently accounted for unique variance in anxiety and mood disorder outcomes above and beyond DSM diagnoses. These results provide support for the potential development and utility of a hybrid dimensional-categorical profile approach to anxiety and mood disorder classification. In particular, the availability of dimensional indicators and corresponding profiles may serve as a useful complement to DSM diagnoses for both researchers and clinicians. (c) 2014 APA, all rights reserved.

Initial Interpretation and Evaluation of a Profile-Based Classification System for the Anxiety and Mood Disorders: Incremental Validity Compared to DSM-IV Categories

PubMed Central

Rosellini, Anthony J.; Brown, Timothy A.

2014-01-01

Limitations in anxiety and mood disorder diagnostic reliability and validity due to the categorical approach to classification used by the Diagnostic and Statistical Manual of Mental Disorders (DSM) have been long recognized. Although these limitations have led researchers to forward alternative classification schemes, few have been empirically evaluated. In a sample of 1,218 outpatients with anxiety and mood disorders, the present study examined the validity of Brown and Barlow's (2009) proposal to classify the anxiety and mood disorders using an integrated dimensional-categorical approach based on transdiagnostic emotional disorder vulnerabilities and phenotypes. Latent class analyses of seven transdiagnostic dimensional indicators suggested that a six-class (i.e., profile) solution provided the best model fit and was the most conceptually interpretable. Interpretation of the classes was further supported when compared with DSM-IV diagnoses (i.e., within-class prevalence of diagnoses, using diagnoses to predict class membership). In addition, hierarchical multiple regression models were used to demonstrate the incremental validity of the profiles; class probabilities consistently accounted for unique variance in anxiety and mood disorder outcomes above and beyond DSM diagnoses. These results provide support for the potential development and utility of a hybrid dimensional-categorical profile approach to anxiety and mood disorder classification. In particular, the availability of dimensional indicators and corresponding profiles may serve as a useful complement to DSM diagnoses for both researchers and clinicians. PMID:25265416

Development and content validity testing of a comprehensive classification of diagnoses for pediatric nurse practitioners.

PubMed

Burns, C

1991-01-01

Pediatric nurse practitioners (PNPs) need an integrated, comprehensive classification that includes nursing, disease, and developmental diagnoses to effectively describe their practice. No such classification exists. Further, methodologic studies to help evaluate the content validity of any nursing taxonomy are unavailable. A conceptual framework was derived. Then 178 diagnoses from the North American Nursing Diagnosis Association (NANDA) 1986 list, selected diagnoses from the International Classification of Diseases, the Diagnostic and Statistical Manual, Third Revision, and others were selected. This framework identified and listed, with definitions, three domains of diagnoses: Developmental Problems, Diseases, and Daily Living Problems. The diagnoses were ranked using a 4-point scale (4 = highly related to 1 = not related) and were placed into the three domains. The rating scale was assigned by a panel of eight expert pediatric nurses. Diagnoses that were assigned to the Daily Living Problems domain were then sorted into the 11 Functional Health patterns described by Gordon (1987). Reliability was measured using proportions of agreement and Kappas. Content validity of the groups created was measured using indices of content validity and average congruency percentages. The experts used a new method to sort the diagnoses in a new way that decreased overlaps among the domains. The Developmental and Disease domains were judged reliable and valid. The Daily Living domain of nursing diagnoses showed marginally acceptable validity with acceptable reliability. Six Functional Health Patterns were judged reliable and valid, mixed results were determined for four categories, and the Coping/Stress Tolerance category was judged reliable but not valid using either test. There were considerable differences between the panel's, Gordon's (1987), and NANDA's clustering of NANDA diagnoses. This study defines the diagnostic practice of nurses from a holistic, patient-centered perspective. It is the first study to use quantitative methods to test a diagnostic classification system for nursing. The classification model could also be adapted for other nurse specialties.

Limited-Information Goodness-of-Fit Testing of Diagnostic Classification Item Response Theory Models. CRESST Report 840

ERIC Educational Resources Information Center

Hansen, Mark; Cai, Li; Monroe, Scott; Li, Zhen

2014-01-01

It is a well-known problem in testing the fit of models to multinomial data that the full underlying contingency table will inevitably be sparse for tests of reasonable length and for realistic sample sizes. Under such conditions, full-information test statistics such as Pearson's X[superscript 2]?? and the likelihood ratio statistic…

Vascular Anomalies (Part I): Classification and Diagnostics of Vascular Anomalies.

PubMed

Sadick, Maliha; Müller-Wille, René; Wildgruber, Moritz; Wohlgemuth, Walter A

2018-06-06

 Vascular anomalies are a diagnostic and therapeutic challenge. They require dedicated interdisciplinary management. Optimal patient care relies on integral medical evaluation and a classification system established by experts in the field, to provide a better understanding of these complex vascular entities.  A dedicated classification system according to the International Society for the Study of Vascular Anomalies (ISSVA) and the German Interdisciplinary Society of Vascular Anomalies (DiGGefA) is presented. The vast spectrum of diagnostic modalities, ranging from ultrasound with color Doppler, conventional X-ray, CT with 4 D imaging and MRI as well as catheter angiography for appropriate assessment is discussed.  Congenital vascular anomalies are comprised of vascular tumors, based on endothelial cell proliferation and vascular malformations with underlying mesenchymal and angiogenetic disorder. Vascular tumors tend to regress with patient's age, vascular malformations increase in size and are subdivided into capillary, venous, lymphatic, arterio-venous and combined malformations, depending on their dominant vasculature. According to their appearance, venous malformations are the most common representative of vascular anomalies (70 %), followed by lymphatic malformations (12 %), arterio-venous malformations (8 %), combined malformation syndromes (6 %) and capillary malformations (4 %).  The aim is to provide an overview of the current classification system and diagnostic characterization of vascular anomalies in order to facilitate interdisciplinary management of vascular anomalies.   · Vascular anomalies are comprised of vascular tumors and vascular malformations, both considered to be rare diseases.. · Appropriate treatment depends on correct classification and diagnosis of vascular anomalies, which is based on established national and international classification systems, recommendations and guidelines.. · In the classification, diagnosis and treatment of congenital vascular anomalies, radiology plays an integral part in patient management.. · Sadick M, Müller-Wille R, Wildgruber M et al. Vascular Anomalies (Part I): Classification and Diagnostics of Vascular Anomalies. Fortschr Röntgenstr 2018; DOI: 10.1055/a-0620-8925. © Georg Thieme Verlag KG Stuttgart · New York.

Deep-learning-based classification of FDG-PET data for Alzheimer's disease categories

NASA Astrophysics Data System (ADS)

Singh, Shibani; Srivastava, Anant; Mi, Liang; Caselli, Richard J.; Chen, Kewei; Goradia, Dhruman; Reiman, Eric M.; Wang, Yalin

2017-11-01

Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic Alzheimer's disease (AD) patients. PET scans provide functional information that is unique and unavailable using other types of imaging. However, the computational efficacy of FDG-PET data alone, for the classification of various Alzheimers Diagnostic categories, has not been well studied. This motivates us to correctly discriminate various AD Diagnostic categories using FDG-PET data. Deep learning has improved state-of-the-art classification accuracies in the areas of speech, signal, image, video, text mining and recognition. We propose novel methods that involve probabilistic principal component analysis on max-pooled data and mean-pooled data for dimensionality reduction, and multilayer feed forward neural network which performs binary classification. Our experimental dataset consists of baseline data of subjects including 186 cognitively unimpaired (CU) subjects, 336 mild cognitive impairment (MCI) subjects with 158 Late MCI and 178 Early MCI, and 146 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We measured F1-measure, precision, recall, negative and positive predictive values with a 10-fold cross validation scheme. Our results indicate that our designed classifiers achieve competitive results while max pooling achieves better classification performance compared to mean-pooled features. Our deep model based research may advance FDG-PET analysis by demonstrating their potential as an effective imaging biomarker of AD.

76 FR 28689 - Microbiology Devices; Classification of In Vitro Diagnostic Device for Bacillus Species Detection

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-18

.... FDA-2011-N-0103] Microbiology Devices; Classification of In Vitro Diagnostic Device for Bacillus... of the Microbiology Devices Advisory Panel (the Panel). In addition, the proposed rule would... in the Federal Register. 1. Transcript of the FDA Microbiology Devices Panel meeting, March 7, 2002...

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

PubMed

Kilic, Niyazi; Hosgormez, Erkan

2016-03-01

Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 (five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations.

Novel approach for differentiating Shigella species and Escherichia coli by matrix-assisted laser desorption ionization-time of flight mass spectrometry.

PubMed

Khot, Prasanna D; Fisher, Mark A

2013-11-01

Shigella species are so closely related to Escherichia coli that routine matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) cannot reliably differentiate them. Biochemical and serological methods are typically used to distinguish these species; however, "inactive" isolates of E. coli are biochemically very similar to Shigella species and thus pose a greater diagnostic challenge. We used ClinProTools (Bruker Daltonics) software to discover MALDI-TOF MS biomarker peaks and to generate classification models based on the genetic algorithm to differentiate between Shigella species and E. coli. Sixty-six Shigella spp. and 72 E. coli isolates were used to generate and test classification models, and the optimal models contained 15 biomarker peaks for genus-level classification and 12 peaks for species-level classification. We were able to identify 90% of E. coli and Shigella clinical isolates correctly to the species level. Only 3% of tested isolates were misidentified. This novel MALDI-TOF MS approach allows laboratories to streamline the identification of E. coli and Shigella species.

A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images

PubMed Central

Miri, Maliheh; Amini, Zahra; Rabbani, Hossein; Kafieh, Raheleh

2017-01-01

Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar–venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches. PMID:28553578

Modelling the influence of noise of the image sensor for blood cells recognition in computer microscopy

NASA Astrophysics Data System (ADS)

Nikitaev, V. G.; Nagornov, O. V.; Pronichev, A. N.; Polyakov, E. V.; Dmitrieva, V. V.

2017-12-01

The first stage of diagnostics of blood cancer is the analysis of blood smears. The application of decision-making support systems would reduce the subjectivity of the diagnostic process and avoid errors, resulting in often irreversible changes in the patient's condition. In this regard, the solution of this problem requires the use of modern technology. One of the tools of the program classification of blood cells are texture features, and the task of finding informative among them is promising. The paper investigates the effect of noise of the image sensor to informative texture features with application of methods of mathematical modelling.

Diagnostic criteria, severity classification and guidelines of localized scleroderma.

PubMed

Asano, Yoshihide; Fujimoto, Manabu; Ishikawa, Osamu; Sato, Shinichi; Jinnin, Masatoshi; Takehara, Kazuhiko; Hasegawa, Minoru; Yamamoto, Toshiyuki; Ihn, Hironobu

2018-04-23

We established diagnostic criteria and severity classification of localized scleroderma because there is no established diagnostic criteria or widely accepted severity classification of the disease. Also, there has been no clinical guideline for localized scleroderma, so we established its clinical guideline ahead of all over the world. In particular, the clinical guideline was established by clinical questions based on evidence-based medicine according to the New Minds Clinical Practice Guideline Creation Manual (version 1.0). We aimed to make the guideline easy to use and reliable based on the newest evidence, and to present guidance as specific as possible for various clinical problems in treatment of localized scleroderma. © 2018 Japanese Dermatological Association.

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

PubMed Central

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

Background 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. Methods and Findings 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. Conclusions 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. PMID:27494614

Issues of diagnostic review in brain tumor studies: from the Brain Tumor Epidemiology Consortium.

PubMed

Davis, Faith G; Malmer, Beatrice S; Aldape, Ken; Barnholtz-Sloan, Jill S; Bondy, Melissa L; Brännström, Thomas; Bruner, Janet M; Burger, Peter C; Collins, V Peter; Inskip, Peter D; Kruchko, Carol; McCarthy, Bridget J; McLendon, Roger E; Sadetzki, Siegal; Tihan, Tarik; Wrensch, Margaret R; Buffler, Patricia A

2008-03-01

Epidemiologists routinely conduct centralized single pathology reviews to minimize interobserver diagnostic variability, but this practice does not facilitate the combination of studies across geographic regions and institutions where diagnostic practices differ. A meeting of neuropathologists and epidemiologists focused on brain tumor classification issues in the context of protocol needs for consortial studies (http://epi.grants.cancer.gov/btec/). It resulted in recommendations relevant to brain tumors and possibly other rare disease studies. Two categories of brain tumors have enough general agreement over time, across regions, and between individual pathologists that one can consider using existing diagnostic data without further review: glioblastomas and meningiomas (as long as uniform guidelines such as those provided by the WHO are used). Prospective studies of these tumors benefit from collection of pathology reports, at a minimum recording the pathology department and classification system used in the diagnosis. Other brain tumors, such as oligodendroglioma, are less distinct and require careful histopathologic review for consistent classification across study centers. Epidemiologic study protocols must consider the study specific aims, diagnostic changes that have taken place over time, and other issues unique to the type(s) of tumor being studied. As diagnostic changes are being made rapidly, there are no readily available answers on disease classification issues. It is essential that epidemiologists and neuropathologists collaborate to develop appropriate study designs and protocols for specific hypothesis and populations.

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics.

PubMed

Catic, Aida; Gurbeta, Lejla; Kurtovic-Kozaric, Amina; Mehmedbasic, Senad; Badnjevic, Almir

2018-02-13

The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology "Mehmedbasic" for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman's) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.

A tri-fold hybrid classification approach for diagnostics with unexampled faulty states

NASA Astrophysics Data System (ADS)

Tamilselvan, Prasanna; Wang, Pingfeng

2015-01-01

System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing system complexity, it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled system faulty states based upon sensory data to avoid sudden catastrophic system failures. This paper presents a trifold hybrid classification (THC) approach for structural health diagnosis with unexampled health states (UHS), which comprises of preliminary UHS identification using a new thresholded Mahalanobis distance (TMD) classifier, UHS diagnostics using a two-class support vector machine (SVM) classifier, and exampled health states diagnostics using a multi-class SVM classifier. The proposed THC approach, which takes the advantages of both TMD and SVM-based classification techniques, is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the exampled health states and forming new ones autonomously. The proposed THC approach is further extended to a generic framework for health diagnostics problems with unexampled faulty states and demonstrated with health diagnostics case studies for power transformers and rolling bearings.

The structure of PTSD symptoms according to DSM-5 and IDC-11 proposal: A multi-sample analysis.

PubMed

Cyniak-Cieciura, M; Staniaszek, K; Popiel, A; Pragłowska, E; Zawadzki, B

2017-07-01

Posttraumatic stress disorder (PTSD) symptoms structure is a subject of ongoing debate since its inclusion in DSM-III classification in 1980. Different research on PTSD symptoms structure proved the better fit of four-factor and five-factor models comparing to the one proposed by DSM-IV. With the publication of DSM-5 classification, which introduced significant changes to PTSD diagnosis, the question arises about the adequacy of the proposed criteria to the real structure of disorder symptoms. Recent analyses suggest that seven-factor hybrid model is the best reflection of symptoms structure proposed to date. At the same time, some researchers and ICD-11 classification postulate a simplification of PTSD diagnosis restricting it to only three core criteria and adding additional diagnostic unit of complex-PTSD. This research aimed at checking symptoms' structure according to well-known and supported four-, five-, six- and seven-factor models based on DSM-5 symptoms and the conceptualization proposed by the ICD-11 as well as examining the relation between PTSD symptoms categories with borderline personality disorder. Four different trauma populations were examined with self-reported Posttraumatic Diagnostic Scale for DSM-5 (PDS-5) measure. The results suggest that six- and seven-factor hybrid model as well as three-factor ICD-11 concept fits the data better than other models. The core PTSD symptoms were less related to borderline personality disorder than other, broader, symptoms categories only in one sample. Combination of ICD-11 simplified PTSD diagnosis with the more complex approach (e.g. basing on a seven-factor model) may be an attractive proposal for both scientists and practitioners, however does not necessarily lower its comorbidity with borderline personality disorder. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

[Implementation of cytology images classification--the Bethesda 2001 System--in a group of screened women from Podlaskie region--effect evaluation].

PubMed

Zbroch, Tomasz; Knapp, Paweł Grzegorz; Knapp, Piotr Andrzej

2007-09-01

Increasing knowledge concerning carcinogenesis within cervical epithelium has forced us to make continues modifications of cytology classification of the cervical smears. Eventually, new descriptions of the submicroscopic cytomorphological abnormalities have enabled the implementation of Bethesda System which was meant to take place of the former Papanicolaou classification although temporarily both are sometimes used simultaneously. The aim of this study was to compare results of these two classification systems in the aspect of diagnostic accuracy verified by further tests of the diagnostic algorithm for the cervical lesion evaluation. The study was conducted in the group of women selected from general population, the criteria being the place of living and cervical cancer age risk group, in the consecutive periods of mass screening in Podlaski region. The performed diagnostic tests have been based on the commonly used algorithm, as well as identical laboratory and methodological conditions. Performed assessment revealed comparable diagnostic accuracy of both analyzing classifications, verified by histological examination, although with marked higher specificity for dysplastic lesions with decreased number of HSIL results and increased diagnosis of LSILs. Higher number of performed colposcopies and biopsies were an additional consequence of TBS classification. Results based on Bethesda System made it possible to find the sources and reasons of abnormalities with much greater precision, which enabled causing agent treatment. Two evaluated cytology classification systems, although not much different, depicted higher potential of TBS and better, more effective communication between cytology laboratory and gynecologist, making reasonable implementation of The Bethesda System in the daily cytology screening work.

Reliability, Validity, and Classification Accuracy of the DSM-5 Diagnostic Criteria for Gambling Disorder and Comparison to DSM-IV.

PubMed

Stinchfield, Randy; McCready, John; Turner, Nigel E; Jimenez-Murcia, Susana; Petry, Nancy M; Grant, Jon; Welte, John; Chapman, Heather; Winters, Ken C

2016-09-01

The DSM-5 was published in 2013 and it included two substantive revisions for gambling disorder (GD). These changes are the reduction in the threshold from five to four criteria and elimination of the illegal activities criterion. The purpose of this study was to twofold. First, to assess the reliability, validity and classification accuracy of the DSM-5 diagnostic criteria for GD. Second, to compare the DSM-5-DSM-IV on reliability, validity, and classification accuracy, including an examination of the effect of the elimination of the illegal acts criterion on diagnostic accuracy. To compare DSM-5 and DSM-IV, eight datasets from three different countries (Canada, USA, and Spain; total N = 3247) were used. All datasets were based on similar research methods. Participants were recruited from outpatient gambling treatment services to represent the group with a GD and from the community to represent the group without a GD. All participants were administered a standardized measure of diagnostic criteria. The DSM-5 yielded satisfactory reliability, validity and classification accuracy. In comparing the DSM-5 to the DSM-IV, most comparisons of reliability, validity and classification accuracy showed more similarities than differences. There was evidence of modest improvements in classification accuracy for DSM-5 over DSM-IV, particularly in reduction of false negative errors. This reduction in false negative errors was largely a function of lowering the cut score from five to four and this revision is an improvement over DSM-IV. From a statistical standpoint, eliminating the illegal acts criterion did not make a significant impact on diagnostic accuracy. From a clinical standpoint, illegal acts can still be addressed in the context of the DSM-5 criterion of lying to others.

New DSM-V neurocognitive disorders criteria and their impact on diagnostic classifications of mild cognitive impairment and dementia in a memory clinic setting.

PubMed

Tay, Laura; Lim, Wee Shiong; Chan, Mark; Ali, Noorhazlina; Mahanum, Shariffah; Chew, Pamela; Lim, June; Chong, Mei Sian

2015-08-01

To examine diagnostic agreement between Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) Neurocognitive Disorders (NCDs) criteria and DSM, Fourth Edition (DSM-IV) criteria for dementia and International Working Group (IWG) criteria for mild cognitive impairment (MCI) and DSM-V's impact on diagnostic classifications of NCDs. The authors further examined clinical factors for discrepancy in diagnostic classifications between the different operational definitions. Using a cross-sectional study in tertiary memory clinic, the authors studied consecutive new patients aged 55 years or older who presented with cognitive symptoms. Dementia severity was scored based on the Clinical Dementia Rating scale (CDR). All patients completed neuropsychological evaluation. Agreement in diagnostic classifications between DSM-IV/IWG and DSM-V was examined using the kappa test and AC1 statistic, with multinomial logistic regression for factors contributing to MCI reclassification as major NCDs as opposed to diagnostically concordant MCI and dementia groups. Of 234 patients studied, 166 patients achieved concordant diagnostic classifications, with overall kappa of 0.41. Eighty-six patients (36.7%) were diagnosed with MCI and 131 (56.0%) with DSM-IV-defined dementia. With DSM-V, 40 patients (17.1%) were classified as mild NCDs and 183 (78.2%) as major NCDs, representing a 39.7% increase in frequency of dementia diagnoses. CDR sum-of-boxes score contributed independently to differentiation of MCI patients reclassified as mild versus major NCDs (OR: 0.01; 95% CI: 0-0.09). CDR sum-of-boxes score (OR: 5.18; 95% CI: 2.04-13.15), performance in amnestic (OR: 0.14; 95% CI: 0.06-0.34) and language (Boston naming: OR: 0.52; 95% CI: 0.29-0.94) tests, were independent determinants of diagnostically concordant dementia diagnosis. The authors observed moderate agreement between the different operational definitions and a 40% increase in dementia diagnoses with operationalization of the DSM-V criteria. Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Performance of the Delirium Rating Scale-Revised-98 Against Different Delirium Diagnostic Criteria in a Population With a High Prevalence of Dementia.

PubMed

Sepulveda, Esteban; Franco, José G; Trzepacz, Paula T; Gaviria, Ana M; Viñuelas, Eva; Palma, José; Ferré, Gisela; Grau, Imma; Vilella, Elisabet

2015-01-01

Delirium diagnosis in elderly is often complicated by underlying dementia. We evaluated performance of the Delirium Rating Scale-Revised-98 (DRS-R98) in patients with high dementia prevalence and also assessed concordance among past and current diagnostic criteria for delirium. Cross-sectional analysis of newly admitted patients to a skilled nursing facility over 6 months, who were rated within 24-48 hours after admission. Interview for Diagnostic and Statistical Manual of Mental Disorders, 3rd edition-R (DSM)-III-R, DSM-IV, DSM-5, and International Classification of Diseases 10th edition delirium ratings, administration of the DRS-R98, and assessment of dementia using the Informant Questionnaire on Cognitive Decline in the Elderly were independently performed by 3 researchers. Discriminant analyses (receiver operating characteristics curves) were used to study DRS-R98 accuracy against different diagnostic criteria. Hanley and McNeil test compared the area under the curve for DRS-R98's discriminant performance for all diagnostic criteria. Dementia was present in 85/125 (68.0%) subjects, and 36/125 (28.8%) met criteria for delirium by at least 1 classification system, whereas only 19/36 (52.8%) did by all. DSM-III-R diagnosed the most as delirious (27.2%), followed by DSM-5 (24.8%), DSM-IV-TR (22.4%), and International Classification of Diseases 10th edition (16%). DRS-R98 had the highest AUC when discriminating DSM-III-R delirium (92.9%), followed by DSM-IV (92.4%), DSM-5 (91%), and International Classification of Diseases 10th edition (90.5%), without statistical differences among them. The best DRS-R98 cutoff score was ≥14.5 for all diagnostic systems except International Classification of Diseases 10th edition (≥15.5). There is a low concordance across diagnostic systems for identification of delirium. The DRS-R98 performs well despite differences across classification systems perhaps because it broadly assesses phenomenology, even in this population with a high prevalence of dementia. Copyright © 2015 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

Molecular Pathology: Predictive, Prognostic, and Diagnostic Markers in Uterine Tumors.

PubMed

Ritterhouse, Lauren L; Howitt, Brooke E

2016-09-01

This article focuses on the diagnostic, prognostic, and predictive molecular biomarkers in uterine malignancies, in the context of morphologic diagnoses. The histologic classification of endometrial carcinomas is reviewed first, followed by the description and molecular classification of endometrial epithelial malignancies in the context of histologic classification. Taken together, the molecular and histologic classifications help clinicians to approach troublesome areas encountered in clinical practice and evaluate the utility of molecular alterations in the diagnosis and subclassification of endometrial carcinomas. Putative prognostic markers are reviewed. The use of molecular alterations and surrogate immunohistochemistry as prognostic and predictive markers is also discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Classification of fracture and non-fracture groups by analysis of coherent X-ray scatter

PubMed Central

Dicken, A. J.; Evans, J. P. O.; Rogers, K. D.; Stone, N.; Greenwood, C.; Godber, S. X.; Clement, J. G.; Lyburn, I. D.; Martin, R. M.; Zioupos, P.

2016-01-01

Osteoporotic fractures present a significant social and economic burden, which is set to rise commensurately with the aging population. Greater understanding of the physicochemical differences between osteoporotic and normal conditions will facilitate the development of diagnostic technologies with increased performance and treatments with increased efficacy. Using coherent X-ray scattering we have evaluated a population of 108 ex vivo human bone samples comprised of non-fracture and fracture groups. Principal component fed linear discriminant analysis was used to develop a classification model to discern each condition resulting in a sensitivity and specificity of 93% and 91%, respectively. Evaluating the coherent X-ray scatter differences from each condition supports the hypothesis that a causal physicochemical change has occurred in the fracture group. This work is a critical step along the path towards developing an in vivo diagnostic tool for fracture risk prediction. PMID:27363947

The impact of endorsing Spitzer's proposed criteria for PTSD in the forthcoming DSM-V on male and female Veterans.

PubMed

Miller, Lyndsey N; Chard, Kathleen M; Schumm, Jeremiah A; O'Brien, Carol

2011-06-01

This study explored differences between Spitzer's proposed model of posttraumatic stress disorder (PTSD) and the current DSM-IV diagnostic classification scheme in 353 Veterans. The majority of Veterans (89%) diagnosed with PTSD as specified in the DSM-IV also met Spitzer's proposed criteria. Veterans who met both DSM-IV and Spitzer's proposed criteria had significantly higher Clinician Administered PTSD Scale severity scores than Veterans only meeting DSM-IV criteria. Logistic regression indicated that being African American and having no comorbid diagnosis of major depressive disorder or history of a substance use disorder were found to predict those Veterans who met current, but not proposed criteria. These findings have important implications regarding proposed changes to the diagnostic classification criteria for PTSD in the forthcoming DSM-V. Copyright © 2011 Elsevier Ltd. All rights reserved.

Between DSM and ICD: Paraphilias and the Transformation of Sexual Norms.

PubMed

Giami, Alain

2015-07-01

The simultaneous revision of the two major international classifications of disease, the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases, serves as an opportunity to observe the dynamic processes through which social norms of sexuality are constructed and are subject to change in relation to social, political, and historical context. This article argues that the classifications of sexual disorders, which define pathological aspects of "sexually arousing fantasies, sexual urges or behaviors" are representations of contemporary sexual norms, gender identifications, and gender relations. It aims to demonstrate how changes in the medical treatment of sexual perversions/paraphilias passed, over the course of the 20th century, from a model of pathologization (and sometimes criminalization) of non-reproductive sexual behaviors to a model that reflects and privileges sexual well-being and responsibility, and pathologizes the absence or the limitation of consent in sexual relations.

Diagnostic Classification of Schizophrenia Patients on the Basis of Regional Reward-Related fMRI Signal Patterns

PubMed Central

Koch, Stefan P.; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

2015-01-01

Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way. PMID:25799236

Diagnostic accuracy of a bayesian latent group analysis for the detection of malingering-related poor effort.

PubMed

Ortega, Alonso; Labrenz, Stephan; Markowitsch, Hans J; Piefke, Martina

2013-01-01

In the last decade, different statistical techniques have been introduced to improve assessment of malingering-related poor effort. In this context, we have recently shown preliminary evidence that a Bayesian latent group model may help to optimize classification accuracy using a simulation research design. In the present study, we conducted two analyses. Firstly, we evaluated how accurately this Bayesian approach can distinguish between participants answering in an honest way (honest response group) and participants feigning cognitive impairment (experimental malingering group). Secondly, we tested the accuracy of our model in the differentiation between patients who had real cognitive deficits (cognitively impaired group) and participants who belonged to the experimental malingering group. All Bayesian analyses were conducted using the raw scores of a visual recognition forced-choice task (2AFC), the Test of Memory Malingering (TOMM, Trial 2), and the Word Memory Test (WMT, primary effort subtests). The first analysis showed 100% accuracy for the Bayesian model in distinguishing participants of both groups with all effort measures. The second analysis showed outstanding overall accuracy of the Bayesian model when estimates were obtained from the 2AFC and the TOMM raw scores. Diagnostic accuracy of the Bayesian model diminished when using the WMT total raw scores. Despite, overall diagnostic accuracy can still be considered excellent. The most plausible explanation for this decrement is the low performance in verbal recognition and fluency tasks of some patients of the cognitively impaired group. Additionally, the Bayesian model provides individual estimates, p(zi |D), of examinees' effort levels. In conclusion, both high classification accuracy levels and Bayesian individual estimates of effort may be very useful for clinicians when assessing for effort in medico-legal settings.

Latent Class Analysis of Early Developmental Trajectory in Baby Siblings of Children with Autism

PubMed Central

Landa, Rebecca J.; Gross, Alden L.; Stuart, Elizabeth A.; Bauman, Margaret

2012-01-01

Background Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Methods Sibs-A (n=204) were assessed with the Mullen Scales of Early Learning from age 6–36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (n=52); non-ASD social/communication delay (broader autism phenotype; BAP) (n=31); and unaffected (n=121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. Results A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Conclusions Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. PMID:22574686

"DC:0-3" to "DC:0-3R" to "DC:0-5": A New Edition

ERIC Educational Resources Information Center

Zeanah, Charles H., Jr.; Carter, Alice; Cohen, Julie; Egger, Helen; Keren, Miri; Gleason, Mary Margaret; Lieberman, Alicia F.; Mulrooney, Kathleen; Oser, Cindy

2015-01-01

Originally published in 1994 by ZERO TO THREE as the "Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood" ("DC:0-3") and revised in 2005 by ZERO TO THREE as the "Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood, Revised…

[Haemolytic uremic syndrome and thrombotic thrombocytopenic purpura: classification based on molecular etiology and review of recent developments in diagnostics].

PubMed

Prohászka, Zoltán

2008-07-06

Haemolytic uremic syndrome and thrombotic thrombocytopenic purpura are overlapping clinical entities based on historical classification. Recent developments in the unfolding of the pathomechanisms of these diseases resulted in the creation of a molecular etiology-based classification. Understanding of some causative relationships yielded detailed diagnostic approaches, novel therapeutic options and thorough prognostic assortment of the patients. Although haemolytic uremic syndrome and thrombotic thrombocytopenic purpura are rare diseases with poor prognosis, the precise molecular etiology-based diagnosis might properly direct the therapy of the affected patients. The current review focuses on the theoretical background and detailed description of the available diagnostic possibilities, and some practical information necessary for the interpretation of their results.

Genetic and Diagnostic Biomarker Development in ASD Toddlers Using Resting State Functional MRI

DTIC Science & Technology

2015-09-01

connectivity networks during natural sleep as a neurologic biomarker for ASD that is suitable for diagnostic use in young children (ages 1-4). Existing... Autism Spectrum Disorders (ASD); functional magnetic resonance imaging (fMRI); connectivity modeling 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Requirements………………………………….. 4 9. Appendices…………………………………………………………….. 4 1. INTRODUCTION Autism spectrum disorders (ASD), characterized by abnormal

Evaluating Intervention Effects in a Diagnostic Classification Model Framework

ERIC Educational Resources Information Center

Madison, Matthew J.; Bradshaw, Laine

2018-01-01

The evaluation of intervention effects is an important objective of educational research. One way to evaluate the effectiveness of an intervention is to conduct an experiment that assigns individuals to control and treatment groups. In the context of pretest/posttest designed studies, this is referred to as a control-group pretest/posttest design.…

Working Models of Attachment in Psychiatrically Hospitalized Adolescents: Relation to Psychopathology and Personality.

ERIC Educational Resources Information Center

Rosenstein, Diana S.; Horowitz, Harvey A.

This study examined the role of attachment in adolescent psychopathology among psychiatrically hospitalized adolescents. Subjects consisted of 60 adolescents and 27 of their mothers. Measures included the Adult Attachment Interview classification for both the adolescents and their mothers, and a battery of diagnostic and personality assessment of…

GDINA and CDM Packages in R

ERIC Educational Resources Information Center

Rupp, André A.; van Rijn, Peter W.

2018-01-01

We review the GIDNA and CDM packages in R for fitting cognitive diagnosis/diagnostic classification models. We first provide a summary of their core capabilities and then use both simulated and real data to compare their functionalities in practice. We found that the most relevant routines in the two packages appear to be more similar than…

The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model.

PubMed

Kaznowska, E; Depciuch, J; Łach, K; Kołodziej, M; Koziorowska, A; Vongsvivut, J; Zawlik, I; Cholewa, M; Cebulski, J

2018-08-15

Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. Copyright © 2018 Elsevier B.V. All rights reserved.

The Classification of Hysteria and Related Disorders: Historical and Phenomenological Considerations

PubMed Central

North, Carol S.

2015-01-01

This article examines the history of the conceptualization of dissociative, conversion, and somatoform syndromes in relation to one another, chronicles efforts to classify these and other phenomenologically-related psychopathology in the American diagnostic system for mental disorders, and traces the subsequent divergence in opinions of dissenting sectors on classification of these disorders. This article then considers the extensive phenomenological overlap across these disorders in empirical research, and from this foundation presents a new model for the conceptualization of these disorders. The classification of disorders formerly known as hysteria and phenomenologically-related syndromes has long been contentious and unsettled. Examination of the long history of the conceptual difficulties, which remain inherent in existing classification schemes for these disorders, can help to address the continuing controversy. This review clarifies the need for a major conceptual revision of the current classification of these disorders. A new phenomenologically-based classification scheme for these disorders is proposed that is more compatible with the agnostic and atheoretical approach to diagnosis of mental disorders used by the current classification system. PMID:26561836

Genetic and environmental contributions to age of onset of alcohol dependence symptoms in male twins.

PubMed

Liu, I-Chao; Blacker, Deborah L; Xu, Ronghui; Fitzmaurice, Garrett; Tsuang, Ming T; Lyons, Michael J

2004-11-01

To investigate genetic and environmental influences on the development of specific alcohol dependence symptoms. A classical twin study of 3372 male-male twin pairs in the Vietnam Era Twin (VET) Registry based on telephone interviews about alcohol use. The nine diagnostic symptoms according to the Diagnostic and Statistical Manual of Mental Disorder, version III (revised) (DSM-III-R) definition of alcohol dependence. Symptoms were grouped into those based on impaired control, biological effects and social consequences (Beresford's classification) or early versus late symptoms (Nelson's classification). Survival models with random effects were used to examine the age of onset of each symptom. Approximately 38% of the variation in age of onset of each symptom group based on Beresford's classification is due to additive genetic factors. The age of onset of late symptoms from Nelson's classification appears to be most affected by genetic factors. Estimates of genetic effects for impaired control symptoms are greatly decreased when twins with comorbid psychiatric disorders are excluded. Our results support the heritability of age of onset of DSM-III-R-defined symptoms for alcohol dependence. However, no symptom group in Beresford's classification could be identified as more heritable than other symptom groups. A strong association between genetic vulnerability and co-occurring diseases for symptoms indicative of impaired control could be found. In addition, our findings show that the late symptom group could be a good candidate for subsequent genetic research.

Integrating nursing diagnostic concepts into the medical entities dictionary using the ISO Reference Terminology Model for Nursing Diagnosis.

PubMed

Hwang, Jee-In; Cimino, James J; Bakken, Suzanne

2003-01-01

The purposes of the study were (1) to evaluate the usefulness of the International Standards Organization (ISO) Reference Terminology Model for Nursing Diagnoses as a terminology model for defining nursing diagnostic concepts in the Medical Entities Dictionary (MED) and (2) to create the additional hierarchical structures required for integration of nursing diagnostic concepts into the MED. The authors dissected nursing diagnostic terms from two source terminologies (Home Health Care Classification and the Omaha System) into the semantic categories of the ISO model. Consistent with the ISO model, they selected Focus and Judgment as required semantic categories for creating intensional definitions of nursing diagnostic concepts in the MED. Because the MED does not include Focus and Judgment hierarchies, the authors developed them to define the nursing diagnostic concepts. The ISO model was sufficient for dissecting the source terminologies into atomic terms. The authors identified 162 unique focus concepts from the 266 nursing diagnosis terms for inclusion in the Focus hierarchy. For the Judgment hierarchy, the authors precoordinated Judgment and Potentiality instead of using Potentiality as a qualifier of Judgment as in the ISO model. Impairment and Alteration were the most frequently occurring judgments. Nursing care represents a large proportion of health care activities; thus, it is vital that terms used by nurses are integrated into concept-oriented terminologies that provide broad coverage for the domain of health care. This study supports the utility of the ISO Reference Terminology Model for Nursing Diagnoses as a facilitator for the integration process.

Integrating Nursing Diagnostic Concepts into the Medical Entities Dictionary Using the ISO Reference Terminology Model for Nursing Diagnosis

PubMed Central

Hwang, Jee-In; Cimino, James J.; Bakken, Suzanne

2003-01-01

Objective: The purposes of the study were (1) to evaluate the usefulness of the International Standards Organization (ISO) Reference Terminology Model for Nursing Diagnoses as a terminology model for defining nursing diagnostic concepts in the Medical Entities Dictionary (MED) and (2) to create the additional hierarchical structures required for integration of nursing diagnostic concepts into the MED. Design and Measurements: The authors dissected nursing diagnostic terms from two source terminologies (Home Health Care Classification and the Omaha System) into the semantic categories of the ISO model. Consistent with the ISO model, they selected Focus and Judgment as required semantic categories for creating intensional definitions of nursing diagnostic concepts in the MED. Because the MED does not include Focus and Judgment hierarchies, the authors developed them to define the nursing diagnostic concepts. Results: The ISO model was sufficient for dissecting the source terminologies into atomic terms. The authors identified 162 unique focus concepts from the 266 nursing diagnosis terms for inclusion in the Focus hierarchy. For the Judgment hierarchy, the authors precoordinated Judgment and Potentiality instead of using Potentiality as a qualifier of Judgment as in the ISO model. Impairment and Alteration were the most frequently occurring judgments. Conclusions: Nursing care represents a large proportion of health care activities; thus, it is vital that terms used by nurses are integrated into concept-oriented terminologies that provide broad coverage for the domain of health care. This study supports the utility of the ISO Reference Terminology Model for Nursing Diagnoses as a facilitator for the integration process. PMID:12668692

The contribution of the vaccine adverse event text mining system to the classification of possible Guillain-Barré syndrome reports.

PubMed

Botsis, T; Woo, E J; Ball, R

2013-01-01

We previously demonstrated that a general purpose text mining system, the Vaccine adverse event Text Mining (VaeTM) system, could be used to automatically classify reports of an-aphylaxis for post-marketing safety surveillance of vaccines. To evaluate the ability of VaeTM to classify reports to the Vaccine Adverse Event Reporting System (VAERS) of possible Guillain-Barré Syndrome (GBS). We used VaeTM to extract the key diagnostic features from the text of reports in VAERS. Then, we applied the Brighton Collaboration (BC) case definition for GBS, and an information retrieval strategy (i.e. the vector space model) to quantify the specific information that is included in the key features extracted by VaeTM and compared it with the encoded information that is already stored in VAERS as Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs). We also evaluated the contribution of the primary (diagnosis and cause of death) and secondary (second level diagnosis and symptoms) diagnostic VaeTM-based features to the total VaeTM-based information. MedDRA captured more information and better supported the classification of reports for GBS than VaeTM (AUC: 0.904 vs. 0.777); the lower performance of VaeTM is likely due to the lack of extraction by VaeTM of specific laboratory results that are included in the BC criteria for GBS. On the other hand, the VaeTM-based classification exhibited greater specificity than the MedDRA-based approach (94.96% vs. 87.65%). Most of the VaeTM-based information was contained in the secondary diagnostic features. For GBS, clinical signs and symptoms alone are not sufficient to match MedDRA coding for purposes of case classification, but are preferred if specificity is the priority.

Reference Standard Test and the Diagnostic Ability of Spectral Domain Optical Coherence Tomography in Glaucoma.

PubMed

Rao, Harsha L; Yadav, Ravi K; Addepalli, Uday K; Begum, Viquar U; Senthil, Sirisha; Choudhari, Nikhil S; Garudadri, Chandra S

2015-08-01

To evaluate the relationship between the reference standard used to diagnose glaucoma and the diagnostic ability of spectral domain optical coherence tomograph (SDOCT). In a cross-sectional study, 280 eyes of 175 consecutive subjects, referred to a tertiary eye care center for glaucoma evaluation, underwent optic disc photography, visual field (VF) examination, and SDOCT examination. The cohort was divided into glaucoma and control groups based on 3 reference standards for glaucoma diagnosis: first based on the optic disc classification (179 glaucoma and 101 control eyes), second on VF classification (glaucoma hemifield test outside normal limits and pattern SD with P-value of <5%, 130 glaucoma and 150 control eyes), and third on the presence of both glaucomatous optic disc and glaucomatous VF (125 glaucoma and 155 control eyes). Relationship between the reference standards and the diagnostic parameters of SDOCT were evaluated using areas under the receiver operating characteristic curve, sensitivity, and specificity. Areas under the receiver operating characteristic curve and sensitivities of most of the SDOCT parameters obtained with the 3 reference standards (ranging from 0.74 to 0.88 and 72% to 88%, respectively) were comparable (P>0.05). However, specificities of SDOCT parameters were significantly greater (P<0.05) with optic disc classification as reference standard (74% to 88%) compared with VF classification as reference standard (57% to 74%). Diagnostic parameters of SDOCT that was significantly affected by reference standard was the specificity, which was greater with optic disc classification as the reference standard. This has to be considered when comparing the diagnostic ability of SDOCT across studies.

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Review of Psychodynamic diagnostics manual (PDM).

PubMed

Moses, Ira

2008-03-01

Reviews the book, Psychodynamic diagnostics manual (PDM) by Alliance of Psychoanalytic Organizations (2006). This volume is divided into three major sections, Part 1--Classification of Adult Mental Heath Disorder, Part 2--Classification of Child and Adolescent Mental Health Disorder, and Part 3--Conceptual and Research Foundations for a Psychodynamically Based Classification System for Mental Health Disorders. Unlike the standard DSM which highlights the patient's presenting symptom (Axis I) with secondary consideration given to an underlying personality disorder (Axis II), the major thesis of classification scheme of this volume is that diagnostic evaluation should provide a more patient centered and a more clinically useful picture of the individual by understanding the symptom(s) through the essential dimensions of the patient's personality and mental functions (interpersonal and cognitive capacities). Part 3, which could stand on its own as a separate volume, is a thorough critique of psychotherapy outcome research in which the authors delineate how major design flaws have derived from "favoring what is measurable over what is meaningful." The authors cogently demonstrate that diagnostic assessment is a continuous effort toward providing individualized and clinically relevant evaluations. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

Benign paroxysmal positional vertigo: Diagnostic criteria Consensus document of the Committee for the Classification of Vestibular Disorders of the Bárány Society.

PubMed

von Brevern, Michael; Bertholon, Pierre; Brandt, Thomas; Fife, Terry; Imai, Takao; Nuti, Daniele; Newman-Toker, David

This article presents operational diagnostic criteria for benign paroxysmal positional vertigo (BPPV), formulated by the Committee for Classification of Vestibular Disorders of the Bárány Society. The classification reflects current knowledge of clinical aspects and pathomechanisms of BPPV and includes both established and emerging syndromes of BPPV. It is anticipated that growing understanding of the disease will lead to further development of this classification. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. All rights reserved.

Similarity-Dissimilarity Competition in Disjunctive Classification Tasks

PubMed Central

Mathy, Fabien; Haladjian, Harry H.; Laurent, Eric; Goldstone, Robert L.

2013-01-01

Typical disjunctive artificial classification tasks require participants to sort stimuli according to rules such as “x likes cars only when black and coupe OR white and SUV.” For categories like this, increasing the salience of the diagnostic dimensions has two simultaneous effects: increasing the distance between members of the same category and increasing the distance between members of opposite categories. Potentially, these two effects respectively hinder and facilitate classification learning, leading to competing predictions for learning. Increasing saliency may lead to members of the same category to be considered less similar, while the members of separate categories might be considered more dissimilar. This implies a similarity-dissimilarity competition between two basic classification processes. When focusing on sub-category similarity, one would expect more difficult classification when members of the same category become less similar (disregarding the increase of between-category dissimilarity); however, the between-category dissimilarity increase predicts a less difficult classification. Our categorization study suggests that participants rely more on using dissimilarities between opposite categories than finding similarities between sub-categories. We connect our results to rule- and exemplar-based classification models. The pattern of influences of within- and between-category similarities are challenging for simple single-process categorization systems based on rules or exemplars. Instead, our results suggest that either these processes should be integrated in a hybrid model, or that category learning operates by forming clusters within each category. PMID:23403979

Human Vision-Motivated Algorithm Allows Consistent Retinal Vessel Classification Based on Local Color Contrast for Advancing General Diagnostic Exams.

PubMed

Ivanov, Iliya V; Leitritz, Martin A; Norrenberg, Lars A; Völker, Michael; Dynowski, Marek; Ueffing, Marius; Dietter, Johannes

2016-02-01

Abnormalities of blood vessel anatomy, morphology, and ratio can serve as important diagnostic markers for retinal diseases such as AMD or diabetic retinopathy. Large cohort studies demand automated and quantitative image analysis of vascular abnormalities. Therefore, we developed an analytical software tool to enable automated standardized classification of blood vessels supporting clinical reading. A dataset of 61 images was collected from a total of 33 women and 8 men with a median age of 38 years. The pupils were not dilated, and images were taken after dark adaption. In contrast to current methods in which classification is based on vessel profile intensity averages, and similar to human vision, local color contrast was chosen as a discriminator to allow artery vein discrimination and arterial-venous ratio (AVR) calculation without vessel tracking. With 83% ± 1 standard error of the mean for our dataset, we achieved best classification for weighted lightness information from a combination of the red, green, and blue channels. Tested on an independent dataset, our method reached 89% correct classification, which, when benchmarked against conventional ophthalmologic classification, shows significantly improved classification scores. Our study demonstrates that vessel classification based on local color contrast can cope with inter- or intraimage lightness variability and allows consistent AVR calculation. We offer an open-source implementation of this method upon request, which can be integrated into existing tool sets and applied to general diagnostic exams.

DIAGNOSTIC CLASSIFICATION OF MENTAL HEALTH AND DEVELOPMENTAL DISORDERS OF INFANCY AND EARLY CHILDHOOD DC:0-5: SELECTIVE REVIEWS FROM A NEW NOSOLOGY FOR EARLY CHILDHOOD PSYCHOPATHOLOGY.

PubMed

Zeanah, Charles H; Carter, Alice S; Cohen, Julie; Egger, Helen; Gleason, Mary Margaret; Keren, Miri; Lieberman, Alicia; Mulrooney, Kathleen; Oser, Cindy

2016-09-01

The Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood: Revised Edition (DC:0-5; ZERO TO THREE) is scheduled to be published in 2016. The articles in this section are selective reviews that have been undertaken as part of the process of refining and updating the nosology. They provide the rationales for new disorders, for disorders that had not been included previously in the Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood: Revised Edition (DC:0-3R; ZERO TO THREE, 2005), and for changes in how certain types of disorders are conceptualized. © 2016 Michigan Association for Infant Mental Health.

Diagnostic Criteria, Classification and Treatment Goals in Multiple Sclerosis: The Chronicles of Time and Space.

PubMed

Ntranos, Achilles; Lublin, Fred

2016-10-01

Multiple sclerosis (MS) is one of the most diverse human diseases. Since its first description by Charcot in the nineteenth century, the diagnostic criteria, clinical course classification, and treatment goals for MS have been constantly revised and updated to improve diagnostic accuracy, physician communication, and clinical trial design. These changes have improved the clinical outcomes and quality of life for patients with the disease. Recent technological and research breakthroughs will almost certainly further change how we diagnose, classify, and treat MS in the future. In this review, we summarize the key events in the history of MS, explain the reasoning behind the current criteria for MS diagnosis, classification, and treatment, and provide suggestions for further improvements that will keep enhancing the clinical practice of MS.

A Three-Dimensional Receiver Operator Characteristic Surface Diagnostic Metric

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2011-01-01

Receiver Operator Characteristic (ROC) curves are commonly applied as metrics for quantifying the performance of binary fault detection systems. An ROC curve provides a visual representation of a detection system s True Positive Rate versus False Positive Rate sensitivity as the detection threshold is varied. The area under the curve provides a measure of fault detection performance independent of the applied detection threshold. While the standard ROC curve is well suited for quantifying binary fault detection performance, it is not suitable for quantifying the classification performance of multi-fault classification problems. Furthermore, it does not provide a measure of diagnostic latency. To address these shortcomings, a novel three-dimensional receiver operator characteristic (3D ROC) surface metric has been developed. This is done by generating and applying two separate curves: the standard ROC curve reflecting fault detection performance, and a second curve reflecting fault classification performance. A third dimension, diagnostic latency, is added giving rise to 3D ROC surfaces. Applying numerical integration techniques, the volumes under and between the surfaces are calculated to produce metrics of the diagnostic system s detection and classification performance. This paper will describe the 3D ROC surface metric in detail, and present an example of its application for quantifying the performance of aircraft engine gas path diagnostic methods. Metric limitations and potential enhancements are also discussed

Evaluation of gene expression classification studies: factors associated with classification performance.

PubMed

Novianti, Putri W; Roes, Kit C B; Eijkemans, Marinus J C

2014-01-01

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Neurodevelopmental Disorders (ASD and ADHD): DSM-5, ICD-10, and ICD-11.

PubMed

Doernberg, Ellen; Hollander, Eric

2016-08-01

Neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have undergone considerable diagnostic evolution in the past decade. In the United States, the current system in place is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), whereas worldwide, the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) serves as a general medical system. This review will examine the differences in neurodevelopmental disorders between these two systems. First, we will review the important revisions made from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) to the DSM-5, with respect to ASD and ADHD. Next, we will cover the similarities and differences between ASD and ADHD classification in the DSM-5 and the ICD-10, and how these differences may have an effect on neurodevelopmental disorder diagnostics and classification. By examining the changes made for the DSM-5 in 2013, and critiquing the current ICD-10 system, we can help to anticipate and advise on the upcoming ICD-11, due to come online in 2017. Overall, this review serves to highlight the importance of progress towards complementary diagnostic classification systems, keeping in mind the difference in tradition and purpose of the DSM and the ICD, and that these systems are dynamic and changing as more is learned about neurodevelopmental disorders and their underlying etiology. Finally this review will discuss alternative diagnostic approaches, such as the Research Domain Criteria (RDoC) initiative, which links symptom domains to underlying biological and neurological mechanisms. The incorporation of new diagnostic directions could have a great effect on treatment development and insurance coverage for neurodevelopmental disorders worldwide.

Analysis of swallowing sounds using hidden Markov models.

PubMed

Aboofazeli, Mohammad; Moussavi, Zahra

2008-04-01

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

Latent Class Analysis of Early Developmental Trajectory in Baby Siblings of Children with Autism

ERIC Educational Resources Information Center

Landa, Rebecca J.; Gross, Alden L.; Stuart, Elizabeth A.; Bauman, Margaret

2012-01-01

Background: Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Methods: Sibs-A (N = 204) were assessed…

The (Un)usual Suspects? A Measurement Community in Search of Its Identity

ERIC Educational Resources Information Center

Rupp, Andre A.; Templin, Jonathan

2009-01-01

In this article, the authors aim to initiate an open discussion about many of the often unspoken assumptions, beliefs, and myths surrounding "diagnostic classification models" (DCMs). Reading the inspired exchange in the commentaries that the authors have received for their article, it is more apparent to them than ever that they are ready to talk…

21 CFR 886.1380 - Diagnostic condensing lens.

Code of Federal Regulations, 2010 CFR

2010-04-01

... light from the fundus of the eye. (b) Classification. Class I (general controls). The device is exempt...) MEDICAL DEVICES OPHTHALMIC DEVICES Diagnostic Devices § 886.1380 Diagnostic condensing lens. (a) Identification. A diagnostic condensing lens is a device used in binocular indirect ophthalmoscopy (a procedure...

Classification of anthropogenic soils by new diagnostic criteria involved in the Slovak Soil Classification System (2014)

NASA Astrophysics Data System (ADS)

Sobocká, Jaroslava; Balkovič, Juraj; Bedrna, Zoltán

2017-04-01

Anthropogenic soils can be found mostly in SUITMA areas. The issue of adequate and correct description and classification of these soils occurs very often and can result in inconsistent even in contradictory opinions. In the new version of the anthropogenic soil classification system in Slovakia some new diagnostics criteria were involved and applied for better understanding the inherent nature of these soils. The group of the former anthropogenic soils was divided following scheme of soil reference groups in the WRB 2014 (Anthrozem and Technozem). According to the new version of the Slovak anthropogenic soils classification (2014) there have been distinguished 2 groups of anthropogenic soils: 1) cultivated soils group including 2 soil types (in Slovak terminology): Kultizem and Hortizem and 2) technogenic soils group having 2 soil types: Antrozem and Technozem. Cultivated soil group represents soils developing or forming "in-situ" with diagnostic horizons characterized by human deeply influenced cultivated processes. Technogenic soil group are soils developing like "ex-situ" soils. The key features recognizing technogenic soil group are human-transported and altered material (HTAM = ex-situ aspect), and artefacts content. Diagnostic horizons (top and subsoil) were described as various material affected by physical-mechanical excavation, transportation and spread, mixing, and containing artefacts (the new diagnostic feature). Kultizems are differentiated by cultivated horizon(s) and Technozems by anthropogenic horizon(s). Cultivated horizons are mostly well-known described horizon in many scientific references. Anthropogenic horizons for Technozem are developed from the human-induced transported and altered material which origin is from the other ecological locality that adjacent area. Materials (or substrates) can consist of various material (natural, technogenic or their mixing) with thickness ≥ 60 cm. Artefacts are the second diagnostic feature which presence authenticates the "artificial origin" of the soil. Natural material contains ≤ 10 % artefacts; natural-technogenic 10-40 % artefacts; and technogenic ≥ 40 %. In the soil survey anthropogenic transported or altered layer is very simply recognizable in soil profile if it is compared with adjacent natural horizons. The classification problem is to define and distinguish not only artefacts in soil profile but recognize the origin of the material. The completed manual for these issues is missing. In the contribution, there graphically individual basic soil types of Antrozems and Technozems with some subtypes will be illustrated. Also the basic schema of classification units in Slovakia will be depicted.

Validity of the Children's Social Behavior Questionnaire (CSBQ) in Children with Intellectual Disability: Comparing the CSBQ with ADI-R, ADOS, and Clinical DSM-IV-TR Classification

ERIC Educational Resources Information Center

de Bildt, Annelies; Mulder, Erik J.; Hoekstra, Pieter J.; van Lang, Natasja D. J.; Minderaa, Ruud B.; Hartman, Catharina A.

2009-01-01

The Children's Social Behavior Questionnaire (CSBQ) was compared with the Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule (ADOS), and clinical classification in children with mild and moderate intellectual disability (ID), to investigate its criterion related validity. The contribution of the CSBQ to a…

A cross-national examination of differences in classification of lifetime alcohol use disorder between DSM-IV and DSM-5: Findings from the World Mental Health Survey

PubMed Central

Slade, Tim; Chiu, Wai-Tat; Glantz, Meyer; Kessler, Ronald C.; Lago, Luise; Sampson, Nancy; Al-Hamzawi, Ali; Florescu, Silvia; Moskalewicz, Jacek; Murphy, Sam; Navarro-Mateu, Fernando; de Galvis, Yolanda Torres; Viana, Maria Carmen; Xavier, Miguel; Degenhardt, Louisa

2016-01-01

Aims To examine the diagnostic overlap in DSM-IV and DSM-5 alcohol use disorder (AUD) and determine the clinical correlates of changing diagnostic status across the two classification systems. Design DSM-IV and DSM-5 definitions of AUD were compared using cross-national community survey data. Setting Nine low-, middle- and high-income countries. Participants/Cases 31,367 respondents to surveys in the World Health Organization World Mental Health Survey Initiative. Measures Composite International Diagnostic Interview, version 3.0 was used to derive DSM-IV and DSM-5 lifetime diagnoses of AUD. Clinical characteristics, also assessed in the surveys, included lifetime DSM-IV anxiety, mood and drug use disorders, lifetime suicidal ideation, plan and attempt, general functional impairment and psychological distress. Findings Compared to DSM-IV AUD (12.3%, SE=0.3%), the DSM-5 definition yielded slightly lower prevalence estimates (10.8%, SE=0.2%). Almost one third (n=802) of all DSM-IV Abuse cases switched to sub-threshold according to DSM-5 and one quarter (n=467) of all DSM-IV diagnostic orphans switched to mild AUD according to DSM-5. New cases of DSM-5 AUD were largely similar to those who maintained their AUD across both classifications. Similarly, new DSM-5 non-cases were similar to those who were sub-threshold across both classifications. The exception to this was with regards to the prevalence of any lifetime drug use disorder. Conclusions In this large cross-national community sample, the prevalence of DSM-5 lifetime AUD was only slightly lower than the prevalence of DSM-IV lifetime AUD. Nonetheless there was considerable diagnostic switching, with a large number of people inconsistently identified across the two DSM classifications. PMID:27426631

White Paper: Movement System Diagnoses in Neurologic Physical Therapy.

PubMed

Hedman, Lois D; Quinn, Lori; Gill-Body, Kathleen; Brown, David A; Quiben, Myla; Riley, Nora; Scheets, Patricia L

2018-04-01

The APTA recently established a vision for physical therapists to transform society by optimizing movement to promote health and wellness, mitigate impairments, and prevent disability. An important element of this vision entails the integration of the movement system into the profession, and necessitates the development of movement system diagnoses by physical therapists. At this point in time, the profession as a whole has not agreed upon diagnostic classifications or guidelines to assist in developing movement system diagnoses that will consistently capture an individual's movement problems. We propose that, going forward, diagnostic classifications of movement system problems need to be developed, tested, and validated. The Academy of Neurologic Physical Therapy's Movement System Task Force was convened to address these issues with respect to management of movement system problems in patients with neurologic conditions. The purpose of this article is to report on the work and recommendations of the Task Force. The Task Force identified 4 essential elements necessary to develop and implement movement system diagnoses for patients with primarily neurologic involvement from existing movement system classifications. The Task Force considered the potential impact of using movement system diagnoses on clinical practice, education and, research. Recommendations were developed and provided recommendations for potential next steps to broaden this discussion and foster the development of movement system diagnostic classifications. The Task Force proposes that diagnostic classifications of movement system problems need to be developed, tested, and validated with the long-range goal to reach consensus on and adoption of a movement system diagnostic framework for clients with neurologic injury or disease states.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A198).

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

On the effect of experimental noise on the classification of biological samples using Raman micro-spectroscopy

NASA Astrophysics Data System (ADS)

Barton, Sinead J.; Kerr, Laura T.; Domijan, Katarina; Hennelly, Bryan M.

2016-04-01

Raman micro-spectroscopy is an optoelectronic technique that can be used to evaluate the chemical composition of biological samples and has been shown to be a powerful diagnostic tool for the investigation of various cancer related diseases including bladder, breast, and cervical cancer. Raman scattering is an inherently weak process with approximately 1 in 107 photons undergoing scattering and for this reason, noise from the recording system can have a significant impact on the quality of the signal, and its suitability for diagnostic classification. The main sources of noise in the recorded signal are shot noise, CCD dark current, and CCD readout noise. Shot noise results from the low signal photon count while dark current results from thermally generated electrons in the semiconductor pixels. Both of these noise sources are time dependent; readout noise is time independent but is inherent in each individual recording and results in the fundamental limit of measurement, arising from the internal electronics of the camera. In this paper, each of the aforementioned noise sources are analysed in isolation, and used to experimentally validate a mathematical model. This model is then used to simulate spectra that might be acquired under various experimental conditions including the use of different cameras, different source wavelength, and power etc. Simulated noisy datasets of T24 and RT112 cell line spectra are generated based on true cell Raman spectrum irradiance values (recorded using very long exposure times) and the addition of simulated noise. These datasets are then input to multivariate classification using Principal Components Analysis and Linear Discriminant Analysis. This method enables an investigation into the effect of noise on the sensitivity and specificity of Raman based classification under various experimental conditions and using different equipment.

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.

PubMed

Thomas, Minta; De Brabanter, Kris; De Moor, Bart

2014-05-10

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques. Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies. We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.

A new epileptic seizure classification based exclusively on ictal semiology.

PubMed

Lüders, H; Acharya, J; Baumgartner, C; Benbadis, S; Bleasel, A; Burgess, R; Dinner, D S; Ebner, A; Foldvary, N; Geller, E; Hamer, H; Holthausen, H; Kotagal, P; Morris, H; Meencke, H J; Noachtar, S; Rosenow, F; Sakamoto, A; Steinhoff, B J; Tuxhorn, I; Wyllie, E

1999-03-01

Historically, seizure semiology was the main feature in the differential diagnosis of epileptic syndromes. With the development of clinical EEG, the definition of electroclinical complexes became an essential tool to define epileptic syndromes, particularly focal epileptic syndromes. Modern advances in diagnostic technology, particularly in neuroimaging and molecular biology, now permit better definitions of epileptic syndromes. At the same time detailed studies showed that there does not necessarily exist a one-to-one relationship between epileptic seizures or electroclinical complexes and epileptic syndromes. These developments call for the reintroduction of an epileptic seizure classification based exclusively on clinical semiology, similar to the seizure classifications which were used by neurologists before the introduction of the modern diagnostic methods. This classification of epileptic seizures should always be complemented by an epileptic syndrome classification based on all the available clinical information (clinical history, neurological exam, ictal semiology, EEG, anatomical and functional neuroimaging, etc.). Such an approach is more consistent with mainstream clinical neurology and would avoid the current confusion between the classification of epileptic seizures (which in the International Seizure Classification is actually a classification of electroclinical complexes) and the classification of epileptic syndromes.

A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics.

PubMed

van den Bent, Martin J; Weller, Michael; Wen, Patrick Y; Kros, Johan M; Aldape, Ken; Chang, Susan

2017-05-01

The 2007 World Health Organization (WHO) classification of brain tumors did not use molecular abnormalities as diagnostic criteria. Studies have shown that genotyping allows a better prognostic classification of diffuse glioma with improved treatment selection. This has resulted in a major revision of the WHO classification, which is now for adult diffuse glioma centered around isocitrate dehydrogenase (IDH) and 1p/19q diagnostics. This revised classification is reviewed with a focus on adult brain tumors, and includes a recommendation of genes of which routine testing is clinically useful. Apart from assessment of IDH mutational status including sequencing of R132H-immunohistochemistry negative cases and testing for 1p/19q, several other markers can be considered for routine testing, including assessment of copy number alterations of chromosome 7 and 10 and of TERT promoter, BRAF, and H3F3A mutations. For "glioblastoma, IDH mutated" the term "astrocytoma grade IV" could be considered. It should be considered to treat IDH wild-type grades II and III diffuse glioma with polysomy of chromosome 7 and loss of 10q as glioblastoma. New developments must be more quickly translated into further revised diagnostic categories. Quality control and rapid integration of molecular findings into the final diagnosis and the communication of the final diagnosis to clinicians require systematic attention. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Diagnostic index of three-dimensional osteoarthritic changes in temporomandibular joint condylar morphology

PubMed Central

Gomes, Liliane R.; Gomes, Marcelo; Jung, Bryan; Paniagua, Beatriz; Ruellas, Antonio C.; Gonçalves, João Roberto; Styner, Martin A.; Wolford, Larry; Cevidanes, Lucia

2015-01-01

Abstract. This study aimed to investigate imaging statistical approaches for classifying three-dimensional (3-D) osteoarthritic morphological variations among 169 temporomandibular joint (TMJ) condyles. Cone-beam computed tomography scans were acquired from 69 subjects with long-term TMJ osteoarthritis (OA), 15 subjects at initial diagnosis of OA, and 7 healthy controls. Three-dimensional surface models of the condyles were constructed and SPHARM-PDM established correspondent points on each model. Multivariate analysis of covariance and direction-projection-permutation (DiProPerm) were used for testing statistical significance of the differences between the groups determined by clinical and radiographic diagnoses. Unsupervised classification using hierarchical agglomerative clustering was then conducted. Compared with healthy controls, OA average condyle was significantly smaller in all dimensions except its anterior surface. Significant flattening of the lateral pole was noticed at initial diagnosis. We observed areas of 3.88-mm bone resorption at the superior surface and 3.10-mm bone apposition at the anterior aspect of the long-term OA average model. DiProPerm supported a significant difference between the healthy control and OA group (p-value=0.001). Clinically meaningful unsupervised classification of TMJ condylar morphology determined a preliminary diagnostic index of 3-D osteoarthritic changes, which may be the first step towards a more targeted diagnosis of this condition. PMID:26158119

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

An integrative dimensional classification of personality disorder.

PubMed

Widiger, Thomas A; Livesley, W John; Clark, Lee Anna

2009-09-01

Psychological assessment research concerns how to describe psychological dysfunction in ways that are both valid and useful. Recent advances in assessment research hold the promise of facilitating significant improvements in description and diagnosis. One such contribution is in the classification of personality disorder symptomatology. The American Psychiatric Association's diagnostic manual considers personality disorders to be categorically distinct entities. However, research assessing personality disorders has consistently supported a dimensional perspective. Recognition of the many limitations of categorical models of personality disorder classification has led to the development of a variety of alternative proposals, which further research has indicated can be integrated within a common hierarchical structure. This article offers an alternative integrated dimensional model of normal and abnormal personality structure, and it illustrates how such a model could be used clinically to describe patients' normal adaptive personality traits as well as their maladaptive personality traits that could provide the basis for future assessments of personality disorder. The empirical support, feasibility, and clinical utility of the proposal are discussed. Points of ambiguity and dispute are highlighted, and suggestions for future research are provided. Copyright 2009 APA, all rights reserved.

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling.

PubMed

Tiss, Ali; Timms, John F; Smith, Celia; Devetyarov, Dmitry; Gentry-Maharaj, Aleksandra; Camuzeaux, Stephane; Burford, Brian; Nouretdinov, Ilia; Ford, Jeremy; Luo, Zhiyuan; Jacobs, Ian; Menon, Usha; Gammerman, Alex; Cramer, Rainer

2010-12-01

Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance. Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples. These were compared with models using CA125 threshold levels from 193 patients with ovarian cancer, 290 with benign neoplasm, and 2236 postmenopausal healthy controls. Using a CA125 cutoff of 30 U/mL, an overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls, whereas a cutoff of 65 U/mL provided a sensitivity of 83.9% (99.6% specificity). High classification accuracies were obtained for early-stage cancers (93.5% sensitivity). Reasons for high accuracies include recruitment bias, restriction to postmenopausal women, and inclusion of only primary invasive epithelial ovarian cancer cases. The combination of MS profiling information with CA125 did not significantly improve the specificity/accuracy compared with classifications on the basis of CA125 alone. We report unexpectedly good performance of serum CA125 using threshold classification in discriminating healthy controls and women with benign masses from those with invasive ovarian cancer. This highlights the dependence of diagnostic tests on the characteristics of the study population and the crucial need for authors to provide sufficient relevant details to allow comparison. Our study also shows that MS profiling information adds little to diagnostic accuracy. This finding is in contrast with other reports and shows the limitations of serum MS profiling for biomarker discovery and as a diagnostic tool.

Congenital left ventricular outpouchings: a systematic review of 839 cases and introduction of a novel classification after two centuries.

PubMed

Malakan Rad, Elaheh; Awad, Sawsan; Hijazi, Ziyad M

2014-01-01

Congenital left ventricular outpouchings (LVOs) are reported under five overlapping and poorly defined terms including left ventricular accessory chamber, left ventricular aneurysm (LVA), left ventricular diverticulum (LVD), double-chambered LV, and accessory left ventricle. Diagnostic criteria are frequently mixed and not mutually exclusive. They convey no information regarding treatment strategy and prognosis. The aim of this systematic review is to provide a clear and inclusive classification, with therapeutic and prognostic implications, for congenital LVOs. We performed three separate sets of search on three subjects including "congenital left ventricular outpouchings," "important and simply measurable markers of left ventricular function," and "relationship of mechanics of intraventricular blood flow and optimal vortex formation in left ventricle and elliptical geometry of LV." We enrolled case series, review articles, and case reports with literature review. All types of acquired LVO's were excluded. We studied the abstracts of all searched articles. We focused on diagnostic criteria and patients' outcome. To examine the validity and reliability of the novel classification, fifteen previous studies were revisited using the novel classification. A total of 20 papers from 11 countries fulfilled our inclusion criteria. The age of patients ranged from prenatal age to geriatric age range. Diagnostic criteria were clearly stated only for two of the above five terms (i.e., congenital LVA and congenital LVD). Cases with mixed diagnostic criteria were frequent.Elliptical geometry of left ventricle was found to have significant impact on effective blood flow mechanics in LV. A simple inclusive classification for congenital LVOs, with therapeutic and prognostic implications, was introduced. The cornerstone of this classification is elliptical LV geometry. Large-type IIc LVO have dismal prognosis, if left untreated. LVO type I and small LVO type IIa have the best prognosis. © 2014 Wiley Periodicals, Inc.

HPV-relatedness definitions for classifying HPV-related oropharyngeal cancer patient do impact on TNM classification and patients' survival.

PubMed

Taberna, Miren; Mena, Marisa; Tous, Sara; Pavón, Miquel Angel; Oliva, Marc; León, Xavier; Garcia, Jacinto; Guix, Marta; Hijano, Rafael; Bonfill, Teresa; Aguilà, Antón; Alemany, Laia; Mesía, Ricard

2018-01-01

Given the different nature and better outcomes of oropharyngeal carcinoma (OPC) associated with human papillomavirus (HPV) infection, a novel clinical stage classification for HPV-related OPC has been accepted for the 8th edition AJCC TNM (ICON-S model). However, it is still unclear the HPV-relatedness definition with best diagnostic accuracy and prognostic value. The aim of this study was to compare different staging system models proposed for HPV-related OPC patients: 7th edition AJCC TNM, RPA stage with non-anatomic factors (Princess Margaret), RPA with N categories for nasopharyngeal cancer (MD-Anderson) and AHR-new (ICON-S), according to different HPV-relatedness definitions: HPV-DNA detection plus an additional positive marker (p16INK4a or HPV-mRNA), p16INK4a positivity alone or the combination of HPV-DNA/p16INK4a positivity as diagnostic tests. A total of 788 consecutive OPC cases diagnosed from 1991 to 2013 were considered eligible for the analysis. Of these samples, 66 (8.4%) were positive for HPV-DNA and (p16INK4a or HPV-mRNA), 83 (10.5%) were p16INK4a positive and 58 (7.4%) were double positive for HPV-DNA/p16INK4a. ICON-S model was the staging system, which performed better in our series when using at least two biomarkers to define HPV-causality. When the same analysis was performed considering only p16INK4a-positivity, RPA stage with non-anatomic factors (Princess Margaret) has the best classification based on AIC criteria. HPV-relatedness definition for classifying HPV-related OPC patient do impact on TNM classification and patients' survival. Further studies assessing HPV-relatedness definitions are warranted to better classify HPV-related OPC patients in the era of de-escalation clinical trials.

A new map of standardized terrestrial ecosystems of Africa

USGS Publications Warehouse

Sayre, Roger G.; Comer, Patrick; Hak, Jon; Josse, Carmen; Bow, Jacquie; Warner, Harumi; Larwanou, Mahamane; Kelbessa, Ensermu; Bekele, Tamrat; Kehl, Harald; Amena, Ruba; Andriamasimanana, Rado; Ba, Taibou; Benson, Laurence; Boucher, Timothy; Brown, Matthew; Cress, Jill J.; Dassering, Oueddo; Friesen, Beverly A.; Gachathi, Francis; Houcine, Sebei; Keita, Mahamadou; Khamala, Erick; Marangu, Dan; Mokua, Fredrick; Morou, Boube; Mucina, Ladislav; Mugisha, Samuel; Mwavu, Edward; Rutherford, Michael; Sanou, Patrice; Syampungani, Stephen; Tomor, Bojoi; Vall, Abdallahi Ould Mohamed; Vande Weghe, Jean Pierre; Wangui, Eunice; Waruingi, Lucy

2013-01-01

Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.

HPV-relatedness definitions for classifying HPV-related oropharyngeal cancer patient do impact on TNM classification and patients’ survival

PubMed Central

Mena, Marisa; Tous, Sara; Pavón, Miquel Angel; Oliva, Marc; León, Xavier; Garcia, Jacinto; Guix, Marta; Hijano, Rafael; Bonfill, Teresa; Aguilà, Antón; Alemany, Laia; Mesía, Ricard

2018-01-01

Background Given the different nature and better outcomes of oropharyngeal carcinoma (OPC) associated with human papillomavirus (HPV) infection, a novel clinical stage classification for HPV-related OPC has been accepted for the 8th edition AJCC TNM (ICON-S model). However, it is still unclear the HPV-relatedness definition with best diagnostic accuracy and prognostic value. Material and methods The aim of this study was to compare different staging system models proposed for HPV-related OPC patients: 7th edition AJCC TNM, RPA stage with non-anatomic factors (Princess Margaret), RPA with N categories for nasopharyngeal cancer (MD-Anderson) and AHR-new (ICON-S), according to different HPV-relatedness definitions: HPV-DNA detection plus an additional positive marker (p16INK4a or HPV-mRNA), p16INK4a positivity alone or the combination of HPV-DNA/p16INK4a positivity as diagnostic tests. Results A total of 788 consecutive OPC cases diagnosed from 1991 to 2013 were considered eligible for the analysis. Of these samples, 66 (8.4%) were positive for HPV-DNA and (p16INK4a or HPV-mRNA), 83 (10.5%) were p16INK4a positive and 58 (7.4%) were double positive for HPV-DNA/p16INK4a. ICON-S model was the staging system, which performed better in our series when using at least two biomarkers to define HPV-causality. When the same analysis was performed considering only p16INK4a-positivity, RPA stage with non-anatomic factors (Princess Margaret) has the best classification based on AIC criteria. Conclusion HPV-relatedness definition for classifying HPV-related OPC patient do impact on TNM classification and patients’ survival. Further studies assessing HPV-relatedness definitions are warranted to better classify HPV-related OPC patients in the era of de-escalation clinical trials. PMID:29664911

Developing a modular architecture for creation of rule-based clinical diagnostic criteria.

PubMed

Hong, Na; Pathak, Jyotishman; Chute, Christopher G; Jiang, Guoqian

2016-01-01

With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified architecture to support the need for diagnostic criteria computerization. In this study, we present a modular architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies. The architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation. Our efforts in developing and prototyping a modular architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization.

Data-driven classification of bipolar I disorder from longitudinal course of mood.

PubMed

Cochran, A L; McInnis, M G; Forger, D B

2016-10-11

The Diagnostic and Statistical Manual of Mental Disorder (DSM) classification of bipolar disorder defines categories to reflect common understanding of mood symptoms rather than scientific evidence. This work aimed to determine whether bipolar I can be objectively classified from longitudinal mood data and whether resulting classes have clinical associations. Bayesian nonparametric hierarchical models with latent classes and patient-specific models of mood are fit to data from Longitudinal Interval Follow-up Evaluations (LIFE) of bipolar I patients (N=209). Classes are tested for clinical associations. No classes are justified using the time course of DSM-IV mood states. Three classes are justified using the course of subsyndromal mood symptoms. Classes differed in attempted suicides (P=0.017), disability status (P=0.012) and chronicity of affective symptoms (P=0.009). Thus, bipolar I disorder can be objectively classified from mood course, and individuals in the resulting classes share clinical features. Data-driven classification from mood course could be used to enrich sample populations for pharmacological and etiological studies.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

PubMed Central

2012-01-01

Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting. PMID:22417403

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

PubMed

Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

2012-03-14

Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries.

PubMed

Peres, Marines Bertolo; Silveira, Landulfo; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu Tavares; Pasqualucci, Carlos Augusto

2011-09-01

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

New Diagnostic and Therapeutic Approaches to Eradicating Recurrent Breast Cancer

DTIC Science & Technology

2015-09-01

of metastatic disease when they are first diagnosed, yet many patients later return to the clinic with cancer that has spread throughout the body. It...treated before they experience disease relapse. 15. SUBJECT TERMS Breast cancer, metastasis, dissemination, recurrence, therapeutic resistance, systemic...instigation, microenvironment, bone marrow cells, canine , mouse models 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF

Rotor Smoothing and Vibration Monitoring Results for the US Army VMEP

DTIC Science & Technology

2009-06-01

individual component CI detection thresholds, and development of models for diagnostics, prognostics , and anomaly detection . Figure 16 VMEP Server...and prognostics are of current interest. Development of those systems requires large amounts of data (collection, monitoring , manipulation) to capture...development of automated systems and for continuous updating of algorithms to improve detection , classification, and prognostic performance. A test

Chronic myelomonocytic leukemia: Forefront of the field in 2015

PubMed Central

Benton, Christopher B; Nazha, Aziz; Pemmaraju, Naveen; Garcia-Manero, Guillermo

2016-01-01

Chronic myelomonocytic leukemia (CMML) includes components of both myelodysplastic syndrome and myeloproliferative neoplasms and is associated with a characteristic peripheral monocytosis. CMML is caused by the proliferation of an abnormal hematopoietic stem cell clone and may be influenced by microenvironmental changes. The disease is rare and has undergone revisions in its classification. We review the recent classification strategies as well as diagnostic criteria, focusing on CMML’s genetic alterations and unique pathophysiology. We also discuss the latest molecular characterization of the disease, including how molecular factors affect current prognostic models. Finally, we focus on available treatment strategies, with a special emphasis on experimental and forthcoming therapies. PMID:25869097

Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

PubMed

Furlanello, Cesare; Serafini, Maria; Merler, Stefano; Jurman, Giuseppe

2003-11-06

We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

A cDNA microarray gene expression data classifier for clinical diagnostics based on graph theory.

PubMed

Benso, Alfredo; Di Carlo, Stefano; Politano, Gianfranco

2011-01-01

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithms.

SYMPTOM PRESENTATIONS AND CLASSIFICATION OF AUTISM SPECTRUM DISORDER IN EARLY CHILDHOOD: APPLICATION TO THE DIAGNOSTIC CLASSIFICATION OF MENTAL HEALTH AND DEVELOPMENTAL DISORDERS OF INFANCY AND EARLY CHILDHOOD (DC:0-5).

PubMed

Soto, Timothy; Giserman Kiss, Ivy; Carter, Alice S

2016-09-01

Over the past 5 years, a great deal of information about the early course of autism spectrum disorder (ASD) has emerged from longitudinal prospective studies of infants at high risk for developing ASD based on a previously diagnosed older sibling. The current article describes early ASD symptom presentations and outlines the rationale for defining a new disorder, Early Atypical Autism Spectrum Disorder (EA-ASD) to accompany ASD in the new revision of the ZERO TO THREE Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC:0-5) (in press) alternative diagnostic classification manual. EA-ASD is designed to identify children who are 9 to 36 months of age presenting with a minimum of (a) two social-communication symptoms and (b) one repetitive and restricted behavior symptom as well as (c) evidence of impairment, with the intention of providing these children with appropriately tailored services and improving the likelihood of optimizing their development. © 2016 Michigan Association for Infant Mental Health.

SYMPTOM PRESENTATIONS AND CLASSIFICATION OF AUTISM SPECTRUM DISORDER IN EARLY CHILDHOOD: APPLICATION TO THE DIAGNOSTIC CLASSIFICATION OF MENTAL HEALTH AND DEVELOPMENTAL DISORDERS OF INFANCY AND EARLY CHILDHOOD (DC:0–5)

PubMed Central

SOTO, TIMOTHY; KISS, IVY GISERMAN; CARTER, ALICE S.

2018-01-01

Over the past 5 years, a great deal of information about the early course of autism spectrum disorder (ASD) has emerged from longitudinal prospective studies of infants at high risk for developing ASD based on a previously diagnosed older sibling. The current article describes early ASD symptom presentations and outlines the rationale for defining a new disorder, Early Atypical Autism Spectrum Disorder (EA-ASD) to accompany ASD in the new revision of the ZERO TO THREE Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC:0–5) (in press) alternative diagnostic classification manual. EA-ASD is designed to identify children who are 9 to 36 months of age presenting with a minimum of (a) two social-communication symptoms and (b) one repetitive and restricted behavior symptom as well as (c) evidence of impairment, with the intention of providing these children with appropriately tailored services and improving the likelihood of optimizing their development. PMID:27556740

Developing a science of clinical utility in diagnostic classification systems field study strategies for ICD-11 mental and behavioral disorders.

PubMed

Keeley, Jared W; Reed, Geoffrey M; Roberts, Michael C; Evans, Spencer C; Medina-Mora, María Elena; Robles, Rebeca; Rebello, Tahilia; Sharan, Pratap; Gureje, Oye; First, Michael B; Andrews, Howard F; Ayuso-Mateos, José Luís; Gaebel, Wolfgang; Zielasek, Juergen; Saxena, Shekhar

2016-01-01

The World Health Organization (WHO) Department of Mental Health and Substance Abuse has developed a systematic program of field studies to evaluate and improve the clinical utility of the proposed diagnostic guidelines for mental and behavioral disorders in the Eleventh Revision of the International Classification of Diseases and Related Health Problems (ICD-11). The clinical utility of a diagnostic classification is critical to its function as the interface between health encounters and health information, and to making the ICD-11 be a more effective tool for helping the WHO's 194 member countries, including the United States, reduce the global disease burden of mental disorders. This article describes the WHO's efforts to develop a science of clinical utility in regard to one of the two major classification systems for mental disorders. We present the rationale and methodologies for an integrated and complementary set of field study strategies, including large international surveys, formative field studies of the structure of clinicians' conceptualization of mental disorders, case-controlled field studies using experimental methodologies to evaluate the impact of proposed changes to the diagnostic guidelines on clinicians' diagnostic decision making, and ecological implementation field studies of clinical utility in the global settings in which the guidelines will ultimately be implemented. The results of these studies have already been used in making decisions about the structure and content of ICD-11. If clinical utility is indeed among the highest aims of diagnostic systems for mental disorders, as their developers routinely claim, future revision efforts should continue to build on these efforts. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

[WHO classification of head and neck tumours 2017: Main novelties and update of diagnostic methods].

PubMed

Sarradin, Victor; Siegfried, Aurore; Uro-Coste, Emmanuelle; Delord, Jean-Pierre

2018-06-01

The publication of the new WHO classification of head and neck tumours in 2017 brought major modifications. Especially, a new chapter is dedicated to the oropharynx, focusing on the description of squamous cell carcinoma induced by the virus Human Papilloma Virus (HPV), and new entities of tumors are described in nasal cavities and sinuses. In this article are presented the novelties and main changes of this new classification, as well as the updates of the diagnostic methods (immunohistochemistry, cytogenetics or molecular biology). Copyright © 2018 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

[Clinical Implications of Changes in Child Psychiatry in the DSM-5. Strengths and Weaknesses of the Changes].

PubMed

Botero-Franco, Diana; Palacio-Ortíz, Juan David; Arroyave-Sierra, Pilar; Piñeros-Ortíz, Sandra

2016-01-01

The Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Statistical Classification of Diseases and related health problems (ICD) integrate the diagnostic criteria commonly used in psychiatric practice, but the DSM-IV-TR was insufficient for current clinical work. The DSM-5 was first made public in May at the Congress of the American Psychiatric Association, and it includes changes to some aspects of Child Psychiatry, as many of the conditions that were at the beginning in chapter of infancy, childhood and adolescence disorders have been transferred to other chapters and there are new diagnostic criteria or new terms are added. It is therefore important to provide it to Psychiatrists who attend children in order to assess the changes they will be facing in the nomenclature and classification in pursuit of a better classification of the childhood psychopathology. Copyright © 2016. Publicado por Elsevier España.

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

PubMed Central

Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.

2016-01-01

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980

Habitat typing versus advanced vegetation classification in western forests

Treesearch

Tony Kusbach; John Shaw; James Long; Helga Van Miegroet

2012-01-01

Major habitat and community types in northern Utah were compared with plant alliances and associations that were derived from fidelity- and diagnostic-species classification concepts. Each of these classification approaches was associated with important environmental factors. Within a 20,000-ha watershed, 103 forest ecosystems were described by physiographic features,...

Psychology Problem Classification for Children and Youth.

ERIC Educational Resources Information Center

Minnesota Systems Research, Inc., Washington, DC.

The development of Psychology Problem Classification is an early step in the direction of providing a uniform nomenclature for classifying the needs and problems of children and youth. There are many potential uses for a diagnostic classification and coding system. The two most important uses for the practitioner are problem identification and…

Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment

PubMed Central

Martínez-Torteya, Antonio; Treviño, Víctor; Tamez-Peña, José G.

2015-01-01

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers. For this, Alzheimer's Disease Neuroimaging Initiative database was mined for case-control candidates. At least 652 baseline features extracted from MRI and PET analyses, biological samples, and clinical data up to February 2014 were used. A feature selection methodology that includes a genetic algorithm search coupled to a logistic regression classifier and forward and backward selection strategies was used to explore combinations of features. This generated diagnostic models with sizes ranging from 3 to 8, including well documented AD biomarkers, as well as unexplored image, biochemical, and clinical features. Accuracies of 0.85, 0.79, and 0.80 were achieved for HC-AD, HC-MCI, and MCI-AD classifications, respectively, when evaluated using a blind test set. In conclusion, a set of features provided additional and independent information to well-established AD biomarkers, aiding in the classification of MCI and AD. PMID:26106620

Proposals for new standardized general diagnostic criteria for the secondary headaches.

PubMed

Olesen, J; Steiner, T; Bousser, M-G; Diener, H-C; Dodick, D; First, M B; Goadsby, P J; Göbel, H; Lainez, M J A; Lipton, R B; Nappi, G; Sakai, F; Schoenen, J; Silberstein, S D

2009-12-01

Headache classification is a dynamic process through clinical testing and re-testing of current and proposed criteria. After publication of the second edition of the International Classification of Headache Disorders (ICHD-II), need arose for revisions in the classification of medication overuse headache and chronic migraine. These changes made apparent a further need for broader revisions to the standard formulation of diagnostic criteria for the secondary headaches. Currently, the fourth criterion makes impossible the definitive diagnosis of a secondary headache until the underlying cause has resolved or been cured or greatly ameliorated by therapy, at which time the headache may no longer be present. Given that the main purpose of diagnostic criteria is to enable a diagnosis at the onset of a disease in order to guide treatment, this is unhelpful in clinical practice. In the present paper we propose maintaining a standard approach to the secondary headaches using a set of four criteria A, B, C and D, but we construct these so that the requirement for resolution or successful treatment is removed. The proposal for general diagnostic criteria for the secondary headaches will be entered into the internet-based version of the appendix of ICHD-II. During 2009 the Classification Committee will apply the general criteria to all the specific types of secondary headaches. These, and other changes, will be included in a revision of the entire classification entitled ICHD-IIR, expected to be published in 2010. ICHD-IIR will be printed and posted on the website and will be the official classification of the International Headache Society. Unfortunately, it will be necessary to translate ICHD-IIR into the many languages of the world, but the good news is that no major changes to the headache classification are then foreseen for the next 10 years. Until the printing of ICHD-IIR, the printed ICHD-II criteria remain in place for all other purposes. We issue a plea to the headache community to use and study these proposed general criteria for the secondary headaches in order to provide more evidence for their utility-before their incorporation in the main body of the classification.

Latent class analysis of early developmental trajectory in baby siblings of children with autism.

PubMed

Landa, Rebecca J; Gross, Alden L; Stuart, Elizabeth A; Bauman, Margaret

2012-09-01

Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Sibs-A (N = 204) were assessed with the Mullen Scales of Early Learning from age 6 to 36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (N = 52); non-ASD social/communication delay (broader autism phenotype; BAP; N = 31); and unaffected (N = 121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. © 2012 The Authors. Journal of Child Psychology and Psychiatry © 2012 Association for Child and Adolescent Mental Health.

Ethical aspects of personality disorders.

PubMed

Bendelow, Gillian

2010-11-01

To review recent literature around the controversial diagnosis of personality disorder, and to assess the ethical aspects of its status as a medical disorder. The diagnostic currency of personality disorder as a psychiatric/medical disorder has a longstanding history of ethical and social challenges through critiques of the medicalization of deviance. More recently controversies by reflexive physicians around the inclusion of the category in the forthcoming revisions of International Classification of Diseases and Diagnostic and Statistical Manual of Mental Disorders classifications reflect the problems of value-laden criteria, with the diagnostic category being severely challenged from within psychiatry as well as from without. The clinical diagnostic criteria for extremely value-laden psychiatric conditions such as personality disorder need to be analyzed through the lens of values-based medicine, as well as through clinical evidence, as the propensity for political and sociolegal appropriation of the categories can render their clinical and diagnostic value meaningless.

Diagnostic classification of macular ganglion cell and retinal nerve fiber layer analysis: differentiation of false-positives from glaucoma.

PubMed

Kim, Ko Eun; Jeoung, Jin Wook; Park, Ki Ho; Kim, Dong Myung; Kim, Seok Hwan

2015-03-01

To investigate the rate and associated factors of false-positive diagnostic classification of ganglion cell analysis (GCA) and retinal nerve fiber layer (RNFL) maps, and characteristic false-positive patterns on optical coherence tomography (OCT) deviation maps. Prospective, cross-sectional study. A total of 104 healthy eyes of 104 normal participants. All participants underwent peripapillary and macular spectral-domain (Cirrus-HD, Carl Zeiss Meditec Inc, Dublin, CA) OCT scans. False-positive diagnostic classification was defined as yellow or red color-coded areas for GCA and RNFL maps. Univariate and multivariate logistic regression analyses were used to determine associated factors. Eyes with abnormal OCT deviation maps were categorized on the basis of the shape and location of abnormal color-coded area. Differences in clinical characteristics among the subgroups were compared. (1) The rate and associated factors of false-positive OCT maps; (2) patterns of false-positive, color-coded areas on the GCA deviation map and associated clinical characteristics. Of the 104 healthy eyes, 42 (40.4%) and 32 (30.8%) showed abnormal diagnostic classifications on any of the GCA and RNFL maps, respectively. Multivariate analysis revealed that false-positive GCA diagnostic classification was associated with longer axial length and larger fovea-disc angle, whereas longer axial length and smaller disc area were associated with abnormal RNFL maps. Eyes with abnormal GCA deviation map were categorized as group A (donut-shaped round area around the inner annulus), group B (island-like isolated area), and group C (diffuse, circular area with an irregular inner margin in either). The axial length showed a significant increasing trend from group A to C (P=0.001), and likewise, the refractive error was more myopic in group C than in groups A (P=0.015) and B (P=0.014). Group C had thinner average ganglion cell-inner plexiform layer thickness compared with other groups (group A=B>C, P=0.004). Abnormal OCT diagnostic classification should be interpreted with caution, especially in eyes with long axial lengths, large fovea-disc angles, and small optic discs. Our findings suggest that the characteristic patterns of OCT deviation map can provide useful clues to distinguish glaucomatous changes from false-positive findings. Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Condition Monitoring for Helicopter Data. Appendix A

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2000-01-01

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Schizoaffective disorder--an ongoing challenge for psychiatric nosology.

PubMed

Jäger, M; Haack, S; Becker, T; Frasch, K

2011-04-01

Schizoaffective disorder is a common diagnosis in mental health services. The present article aims to provide an overview of diagnostic reliability, symptomatology, outcome, neurobiology and treatment of schizoaffective disorder. Literature was identified by searches in "Medline" and "Cochrane Library". The diagnosis of schizoaffective disorder has a low reliability. There are marked differences between the current diagnostic systems. With respect to psychopathological symptoms, no clear boundaries were found between schizophrenia, schizoaffective disorder and affective disorders. Common neurobiological factors were found across the traditional diagnostic categories. Schizoaffective disorder according to ICD-10 criteria, but not to DSM-IV criteria, shows a more favorable outcome than schizophrenia. With regard to treatment, only a small and heterogeneous database exists. Due to the low reliability and questionable validity there is a substantial need for revision and unification of the current diagnostic concepts of schizoaffective disorder. If future diagnostic systems return to Kraepelin's dichotomous classification of non-organic psychosis or adopt a dimensional diagnostic approach, schizoaffective disorder will disappear from the psychiatric nomenclature. A nosological model with multiple diagnostic entities, however, would be compatible with retaining the diagnostic category of schizoaffective disorder. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Can Questionnaire Reports Correctly Classify Relationship Distress and Partner Physical Abuse?

PubMed Central

Heyman, Richard E.; Feldbau-Kohn, Shari R.; Ehrensaft, Miriam K.; Langhinrichsen-Rohling, Jennifer; O’Leary, K. Daniel

2006-01-01

Relationship adjustment (e.g., Dyadic Adjustment Scale; DAS) and physical aggression (e.g., Conflict Tactics Scale) measures are used both as screening tools and as the sole criterion for classification. This study created face valid diagnostic interviews for relationship distress and physical abuse, through which one could compare preliminarily the classification properties of questionnaire reports. The DAS (and a global measure of relationship satisfaction) had modest agreement with a structured diagnostic interview; both questionnaires tended to overdiagnose distress compared with the interview. Results for partner abuse reiterated the need to go beyond occurrence of aggression as the sole diagnostic criterion, because men’s aggression was more likely than women’s to rise to the level of “abuse” when diagnostic criteria (injury or substantial fear) were applied. PMID:11458637

A robust data scaling algorithm to improve classification accuracies in biomedical data.

PubMed

Cao, Xi Hang; Stojkovic, Ivan; Obradovic, Zoran

2016-09-09

Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy. To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms. The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.

[Current treatment options in acute myeloid leukemia].

PubMed

Heuser, M; Schlenk, R F; Ganser, A

2011-12-01

Genetic aberrations form the basis for diagnostic classification of patients with acute myeloid leukemia (AML) according to the World Health Organization (WHO) classification. Moreover, these aberrations predict response to induction chemotherapy, relapse-free survival, and overall survival of patients with AML. Understanding the pathogenetic role of cytogenetic and molecular changes has led to the development of targeted treatment strategies that require rapid diagnostic assessment of the genetic profile of each patient to select the best treatment available.

Taxonomic update on proposed nomenclature and classification changes for bacteria of medical importance, 2015.

PubMed

Janda, J Michael

2016-10-01

A key aspect of medical, public health, and diagnostic microbiology laboratories is the accurate and rapid reporting and communication regarding infectious agents of clinical significance. Microbial taxonomy in the age of molecular diagnostics and phylogenetics creates changes in taxonomy at a rapid rate further complicating this process. This update focuses on the description of new species and classification changes proposed in 2015. Copyright © 2016 Elsevier Inc. All rights reserved.

Active relearning for robust supervised classification of pulmonary emphysema

NASA Astrophysics Data System (ADS)

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

A consensus approach to the classification of pediatric pulmonary hypertensive vascular disease: Report from the PVRI Pediatric Taskforce, Panama 2011

PubMed Central

del Cerro, Maria Jesus; Abman, Steven; Diaz, Gabriel; Freudenthal, Alexandra Heath; Freudenthal, Franz; Harikrishnan, S.; Haworth, Sheila G.; Ivy, Dunbar; Lopes, Antonio A.; Raj, J. Usha; Sandoval, Julio; Stenmark, Kurt; Adatia, Ian

2011-01-01

Current classifications of pulmonary hypertension have contributed a great deal to our understanding of pulmonary vascular disease, facilitated drug trials, and improved our understanding of congenital heart disease in adult survivors. However, these classifications are not applicable readily to pediatric disease. The classification system that we propose is based firmly in clinical practice. The specific aims of this new system are to improve diagnostic strategies, to promote appropriate clinical investigation, to improve our understanding of disease pathogenesis, physiology and epidemiology, and to guide the development of human disease models in laboratory and animal studies. It should be also an educational resource. We emphasize the concepts of perinatal maladaptation, maldevelopment and pulmonary hypoplasia as causative factors in pediatric pulmonary hypertension. We highlight the importance of genetic, chromosomal and multiple congenital malformation syndromes in the presentation of pediatric pulmonary hypertension. We divide pediatric pulmonary hypertensive vascular disease into 10 broad categories. PMID:21874158

Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares.

PubMed

Wang, Pingyue; Chen, Kewei; Yao, Li; Hu, Bin; Wu, Xia; Zhang, Jiacai; Ye, Qing; Guo, Xiaojuan

2016-08-10

In recent years, increasing attention has been given to the identification of the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Brain neuroimaging techniques have been widely used to support the classification or prediction of MCI. The present study combined magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose PET (FDG-PET), and 18F-florbetapir PET (florbetapir-PET) to discriminate MCI converters (MCI-c, individuals with MCI who convert to AD) from MCI non-converters (MCI-nc, individuals with MCI who have not converted to AD in the follow-up period) based on the partial least squares (PLS) method. Two types of PLS models (informed PLS and agnostic PLS) were built based on 64 MCI-c and 65 MCI-nc from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the three-modality informed PLS model achieved better classification accuracy of 81.40%, sensitivity of 79.69%, and specificity of 83.08% compared with the single-modality model, and the three-modality agnostic PLS model also achieved better classification compared with the two-modality model. Moreover, combining the three modalities with clinical test score (ADAS-cog), the agnostic PLS model (independent data: florbetapir-PET; dependent data: FDG-PET and MRI) achieved optimal accuracy of 86.05%, sensitivity of 81.25%, and specificity of 90.77%. In addition, the comparison of PLS, support vector machine (SVM), and random forest (RF) showed greater diagnostic power of PLS. These results suggested that our multimodal PLS model has the potential to discriminate MCI-c from the MCI-nc and may therefore be helpful in the early diagnosis of AD.

The Contribution of the Vaccine Adverse Event Text Mining System to the Classification of Possible Guillain-Barré Syndrome Reports

PubMed Central

Botsis, T.; Woo, E. J.; Ball, R.

2013-01-01

Background We previously demonstrated that a general purpose text mining system, the Vaccine adverse event Text Mining (VaeTM) system, could be used to automatically classify reports of an-aphylaxis for post-marketing safety surveillance of vaccines. Objective To evaluate the ability of VaeTM to classify reports to the Vaccine Adverse Event Reporting System (VAERS) of possible Guillain-Barré Syndrome (GBS). Methods We used VaeTM to extract the key diagnostic features from the text of reports in VAERS. Then, we applied the Brighton Collaboration (BC) case definition for GBS, and an information retrieval strategy (i.e. the vector space model) to quantify the specific information that is included in the key features extracted by VaeTM and compared it with the encoded information that is already stored in VAERS as Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs). We also evaluated the contribution of the primary (diagnosis and cause of death) and secondary (second level diagnosis and symptoms) diagnostic VaeTM-based features to the total VaeTM-based information. Results MedDRA captured more information and better supported the classification of reports for GBS than VaeTM (AUC: 0.904 vs. 0.777); the lower performance of VaeTM is likely due to the lack of extraction by VaeTM of specific laboratory results that are included in the BC criteria for GBS. On the other hand, the VaeTM-based classification exhibited greater specificity than the MedDRA-based approach (94.96% vs. 87.65%). Most of the VaeTM-based information was contained in the secondary diagnostic features. Conclusion For GBS, clinical signs and symptoms alone are not sufficient to match MedDRA coding for purposes of case classification, but are preferred if specificity is the priority. PMID:23650490

Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy

PubMed Central

Acosta-Mesa, Héctor Gabriel; Cruz-Ramírez, Nicandro; Hernández-Jiménez, Rodolfo

2017-01-01

Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4.5), in addition to different data models that take full advantage of this information and improve the diagnostic performance of colposcopy based on acetowhite temporal patterns. Based on the ROC and PRC area scores, the k-Nearest Neighbors and discrete PLA representation performed better than other methods. The values of sensitivity, specificity, and accuracy reached using this method were 60% (95% CI 50–70), 79% (95% CI 71–86), and 70% (95% CI 60–80), respectively. The acetowhitening phenomenon is not exclusive to high-grade lesions, and we have found acetowhite temporal patterns of epithelial changes that are not precancerous lesions but that are similar to positive ones. These findings need to be considered when developing more robust computing systems in the future. PMID:28744318

Regional variations in mortality rates in England and Wales: an analysis using multi-level modelling.

PubMed

Langford, I H; Bentham, G

1996-03-01

Mortality rates in England and Wales display a persistent regional pattern indicating generally poorer health in the North and West. Some of this is simply a reflection of regional differences in the extent of social deprivation which is known to exert a profound influence on health. Part of the pattern may also be the result of regional differences in urbanization which also affect mortality rates. However, there may be important regional differences over and above these compositional effects. This study attempts to establish the magnitude of such independent regional differences in mortality rates by using the techniques of multi-level modelling. Standardized mortality rates (SMRs) for males and females under 65 for 1989-91 in local authority districts are grouped into categories using the ACORN classification scheme. The Townsend Index is included as a measure of social deprivation. Using a cross-classified multi-level model, it is shown that region accounts for approximately four times more variation in SMRs than is explained by the ACORN classification. Analysis of diagnostic residuals show a clear North-South divide in excess mortality when both regional and socio-economic classification of districts are modelled simultaneously, a possibility allowed for by the use of a multi-level model.

Should Social Workers Use "Diagnostic and Statistical Manual of Mental Disorders-5?"

ERIC Educational Resources Information Center

Frances, Allen; Jones, K. Dayle

2014-01-01

Up until now, social workers have depended on the "Diagnostic and Statistical Manual of Mental Disorders" ("DSM") as the primary diagnostic classification for mental disorders. However, the "DSM-5" revision includes scientifically unfounded, inadequately tested, and potentially dangerous diagnoses that may lead them…

voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data.

PubMed

Zararsiz, Gokmen; Goksuluk, Dincer; Klaus, Bernd; Korkmaz, Selcuk; Eldem, Vahap; Karabulut, Erdem; Ozturk, Ahmet

2017-01-01

RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom's precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.

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.

Challenges to the Use of Artificial Neural Networks for Diagnostic Classifications with Student Test Data

ERIC Educational Resources Information Center

Briggs, Derek C.; Circi, Ruhan

2017-01-01

Artificial Neural Networks (ANNs) have been proposed as a promising approach for the classification of students into different levels of a psychological attribute hierarchy. Unfortunately, because such classifications typically rely upon internally produced item response patterns that have not been externally validated, the instability of ANN…

The Costs of Supervised Classification: The Effect of Learning Task on Conceptual Flexibility

ERIC Educational Resources Information Center

Hoffman, Aaron B.; Rehder, Bob

2010-01-01

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of within-category information. Accordingly, we predicted that classification learning would produce a deficit in people's ability to draw "novel…

Social Work Problem Classification for Children and Youth.

ERIC Educational Resources Information Center

Minnesota Systems Research, Inc., Washington, DC.

The development of the Social Work Problem Classification is an early step in the provision of a uniform nomenclature for classifying the needs and problems of children and youth. There are many potential uses for a diagnostic classification and coding system. The two most important for the practitioner are: (1) problem identification and…

A Cross-National Examination of Differences in Classification of Lifetime Alcohol Use Disorder Between DSM-IV and DSM-5: Findings from the World Mental Health Survey.

PubMed

Slade, Tim; Chiu, Wai-Tat; Glantz, Meyer; Kessler, Ronald C; Lago, Luise; Sampson, Nancy; Al-Hamzawi, Ali; Florescu, Silvia; Moskalewicz, Jacek; Murphy, Sam; Navarro-Mateu, Fernando; Torres de Galvis, Yolanda; Viana, Maria Carmen; Xavier, Miguel; Degenhardt, Louisa

2016-08-01

The current study sought to examine the diagnostic overlap in DSM-IV and DSM-5 alcohol use disorder (AUD) and determine the clinical correlates of changing diagnostic status across the 2 classification systems. DSM-IV and DSM-5 definitions of AUD were compared using cross-national community survey data in 9 low-, middle-, and high-income countries. Participants were 31,367 respondents to surveys in the World Health Organization's World Mental Health Survey Initiative. The Composite International Diagnostic Interview, version 3.0, was used to derive DSM-IV and DSM-5 lifetime diagnoses of AUD. Clinical characteristics, also assessed in the surveys, included lifetime DSM-IV anxiety; mood and drug use disorders; lifetime suicidal ideation, plan, and attempt; general functional impairment; and psychological distress. Compared with DSM-IV AUD (12.3%, SE = 0.3%), the DSM-5 definition yielded slightly lower prevalence estimates (10.8%, SE = 0.2%). Almost one-third (n = 802) of all DSM-IV abuse cases switched to subthreshold according to DSM-5 and one-quarter (n = 467) of all DSM-IV diagnostic orphans switched to mild AUD according to DSM-5. New cases of DSM-5 AUD were largely similar to those who maintained their AUD across both classifications. Similarly, new DSM-5 noncases were similar to those who were subthreshold across both classifications. The exception to this was with regard to the prevalence of any lifetime drug use disorder. In this large cross-national community sample, the prevalence of DSM-5 lifetime AUD was only slightly lower than the prevalence of DSM-IV lifetime AUD. Nonetheless, there was considerable diagnostic switching, with a large number of people inconsistently identified across the 2 DSM classifications. Copyright © 2016 by the Research Society on Alcoholism.

21 CFR 868.1840 - Diagnostic spirometer.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Identification. A diagnostic spirometer is a device used in pulmonary function testing to measure the volume of gas moving in or out of a patient's lungs. (b) Classification. Class II (performance standards). ...

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

PubMed

Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

2018-05-16

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation.

PubMed

Zhang, Y N

2017-01-01

Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed.

Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation

PubMed Central

2017-01-01

Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed. PMID:29075547

Dry eye disease: pathophysiology, classification, and diagnosis.

PubMed

Perry, Henry D

2008-04-01

Dry eye disease (DED) is a multifactorial disorder of the tear film and ocular surface that results in eye discomfort, visual disturbance, and often ocular surface damage. Although recent research has made progress in elucidating DED pathophysiology, currently there are no uniform diagnostic criteria. This article discusses the normal anatomy and physiology of the lacrimal functional unit and the tear film; the pathophysiology of DED; DED etiology, classification, and risk factors; and DED diagnosis, including symptom assessment and the roles of selected diagnostic tests.

Brief Report: Concurrent Validity of Autism Symptom Severity Measures

ERIC Educational Resources Information Center

Reszka, Stephanie S.; Boyd, Brian A.; McBee, Matthew; Hume, Kara A.; Odom, Samuel L.

2014-01-01

The autism spectrum disorder (ASD) diagnostic classifications, according to the DSM-5, include a severity rating. Several screening and/or diagnostic measures, such as the autism diagnostic and observation schedule (ADOS), Childhood Autism Rating Scale (CARS) and social responsiveness scale (SRS) (teacher and parent versions), include an…

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.

Alzheimer's Disease Diagnosis in Individual Subjects using Structural MR Images: Validation Studies

PubMed Central

Vemuri, Prashanthi; Gunter, Jeffrey L.; Senjem, Matthew L.; Whitwell, Jennifer L.; Kantarci, Kejal; Knopman, David S.; Boeve, Bradley F.; Petersen, Ronald C.; Jack, Clifford R.

2008-01-01

OBJECTIVE To develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM) based classification of structural MR (sMR) images. BACKGROUND Libraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects. METHODS 190 patients with probable AD were age- and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training was done by four-fold cross validation. The remaining independent sample of 50 AD and 50 CN were used to obtain a minimally biased estimate of the generalization error of the algorithm. RESULTS The CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3% respectively and the developed models generalized well on the independent test datasets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology. CONCLUSIONS This paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy. PMID:18054253

Relating DSM-5 section II and section III personality disorder diagnostic classification systems to treatment planning.

PubMed

Morey, Leslie C; Benson, Kathryn T

2016-07-01

Beginning with DSM-III, the inclusion of a "personality" axis was designed to encourage awareness of personality disorders and the treatment-related implications of individual differences, but since that time there is little accumulated evidence that the personality disorder categories provide substantial treatment-related guidance. The DSM-5 Personality and Personality Disorders Work Group sought to develop an Alternative Model for personality disorder, and this study examined whether this model is more closely related to clinicians' decision-making processes than the traditional categorical personality disorder diagnoses. A national sample of 337 clinicians provided complete personality disorder diagnostic information and several treatment-related clinical judgments about one of their patients. The dimensional concepts of the DSM-5 Alternative Model for personality disorders demonstrated stronger relationships than categorical DSM-IV/DSM-5 Section II diagnoses to 10 of 11 clinical judgments regarding differential treatment planning, optimal treatment intensity, and long-term prognosis. The constructs of the DSM-5 Alternative Model for personality disorders may provide more clinically useful information for treatment planning than the official categorical personality disorder diagnostic system retained in DSM-5 Section II. Copyright © 2016 Elsevier Inc. All rights reserved.

A Descriptive Genetic Classification for Glaciovolcanoes

NASA Astrophysics Data System (ADS)

Edwards, B. R.; Russell, K.; Porritt, L. A.

2014-12-01

We review the recently published descriptive genetic classification for glaciovolcanoes (Russell et al., Quat Sci Rv, 2014). The new classification uses 'tuya' as a root word for all glaciovolcanic edifices, and with modifiers that make the classification descriptive (e.g., andesitic, lava-dominated, flat topped tuya). Although tuyas can range in composition from basaltic to rhyolitic, many of the characteristics diagnostic of glaciovolcanic environments are largely independent of lava composition (e.g., edifice morphology, columnar jointing patterns, glass distributions, pyroclast shapes). Tuya subtypes are first classified on the basis of variations in edifice-scale morphologies (e.g., conical tuya) then, on the proportions of the essential lithofacies (e.g., tephra-dominated conical tuya), and lastly on magma composition (e.g., basaltic, tephra-dominated, conical tuya). The lithofacies associations within tuyas broadly record the interplay between magmatic and glaciohydraulic conditions extent during the active phases of the eruption, including the dominant style of eruption (e.g., explosive vs. effusive). We present nine distinct, endmember models for glaciovolcanic edifices that simultaneously record changes in eruption conditions (explosive, transitional, effusive) for different general glaciohydraulic conditions (closed/sealed, leaky/partly sealed, open/well-drained). To date we have identified potential examples for 7 of the 9 models. Use of a simplified, descriptive classification scheme for glaciovolcanoes will facilitate communications amongst volcanologists and planetary scientists and the use of tuyas for recovering critical paleo-environmental information, particularly the local glaciohydraulics extent during eruptions.

Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis

PubMed Central

Lim, Liang; Nichols, Brandon; Migden, Michael R.; Rajaram, Narasimhan; Reichenberg, Jason S.; Markey, Mia K.; Ross, Merrick I.; Tunnell, James W.

2014-01-01

Abstract. The goal of this study was to determine the diagnostic capability of a multimodal spectral diagnosis (SD) for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers. We acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using custom-built optical fiber-based clinical systems. Biopsies of lesions were classified using standard histopathology as malignant melanoma (MM), nonmelanoma pigmented lesion (PL), basal cell carcinoma (BCC), actinic keratosis (AK), and squamous cell carcinoma (SCC). Spectral data were analyzed using principal component analysis. Using multiple diagnostically relevant principal components, we built leave-one-out logistic regression classifiers. Classification results were compared with histopathology of the lesion. Sensitivity/specificity for classifying MM versus PL (12 versus 17 lesions) was 100%/100%, for SCC and BCC versus AK (57 versus 14 lesions) was 95%/71%, and for AK and SCC and BCC versus normal skin (71 versus 71 lesions) was 90%/85%. The best classification for nonmelanoma skin cancers required multiple modalities; however, the best melanoma classification occurred with Raman spectroscopy alone. The high diagnostic accuracy for classifying both melanoma and nonmelanoma skin cancer lesions demonstrates the potential for SD as a clinical diagnostic device. PMID:25375350

Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis

NASA Astrophysics Data System (ADS)

Lim, Liang; Nichols, Brandon; Migden, Michael R.; Rajaram, Narasimhan; Reichenberg, Jason S.; Markey, Mia K.; Ross, Merrick I.; Tunnell, James W.

2014-11-01

The goal of this study was to determine the diagnostic capability of a multimodal spectral diagnosis (SD) for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers. We acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using custom-built optical fiber-based clinical systems. Biopsies of lesions were classified using standard histopathology as malignant melanoma (MM), nonmelanoma pigmented lesion (PL), basal cell carcinoma (BCC), actinic keratosis (AK), and squamous cell carcinoma (SCC). Spectral data were analyzed using principal component analysis. Using multiple diagnostically relevant principal components, we built leave-one-out logistic regression classifiers. Classification results were compared with histopathology of the lesion. Sensitivity/specificity for classifying MM versus PL (12 versus 17 lesions) was 100%;/100%;, for SCC and BCC versus AK (57 versus 14 lesions) was 95%;/71%, and for AK and SCC and BCC versus normal skin (71 versus 71 lesions) was 90%/85%. The best classification for nonmelanoma skin cancers required multiple modalities; however, the best melanoma classification occurred with Raman spectroscopy alone. The high diagnostic accuracy for classifying both melanoma and nonmelanoma skin cancer lesions demonstrates the potential for SD as a clinical diagnostic device.

Depathologising gender diversity in childhood in the process of ICD revision and reform.

PubMed

Suess Schwend, Amets; Winter, Sam; Chiam, Zhan; Smiley, Adam; Cabral Grinspan, Mauro

2018-01-24

From 2007 on, the World Health Organisation (WHO) has been revising its diagnostic manual, the International Statistical Classification of Diseases and Related Health Problems (ICD), with approval of ICD-11 due in 2018. The ICD revision has prompted debates on diagnostic classifications related to gender diversity and gender development processes, and specifically on the 'Gender incongruence of childhood' (GIC) code. These debates have taken place at a time an emergent trans depathologisation movement is becoming increasingly international, and regional and international human rights bodies are recognising gender identity as a source of discrimination. With reference to the history of diagnostic classification of gender diversity in childhood, this paper conducts a literature review of academic, activist and institutional documents related to the current discussion on the merits of retaining or abandoning the GIC code. Within this broader discussion, the paper reviews in more detail recent publications arguing for the abandonment of this diagnostic code drawing upon clinical, bioethical and human rights perspectives. The review indicates that gender diverse children engaged in exploring their gender identity and expression do not benefit from diagnosis. Instead they benefit from support from their families, their schools and from society more broadly.

Tree Classification Software

NASA Technical Reports Server (NTRS)

Buntine, Wray

1993-01-01

This paper introduces the IND Tree Package to prospective users. IND does supervised learning using classification trees. This learning task is a basic tool used in the development of diagnosis, monitoring and expert systems. The IND Tree Package was developed as part of a NASA project to semi-automate the development of data analysis and modelling algorithms using artificial intelligence techniques. The IND Tree Package integrates features from CART and C4 with newer Bayesian and minimum encoding methods for growing classification trees and graphs. The IND Tree Package also provides an experimental control suite on top. The newer features give improved probability estimates often required in diagnostic and screening tasks. The package comes with a manual, Unix 'man' entries, and a guide to tree methods and research. The IND Tree Package is implemented in C under Unix and was beta-tested at university and commercial research laboratories in the United States.

[Evaluation of new and emerging health technologies. Proposal for classification].

PubMed

Prados-Torres, J D; Vidal-España, F; Barnestein-Fonseca, P; Gallo-García, C; Irastorza-Aldasoro, A; Leiva-Fernández, F

2011-01-01

Review and develop a proposal for the classification of health technologies (HT) evaluated by the Health Technology Assessment Agencies (HTAA). Peer review of AETS of the previous proposed classification of HT. Analysis of their input and suggestions for amendments. Construction of a new classification. Pilot study with physicians. Andalusian Public Health System. Spanish HTAA. Experts from HTAA. Tutors of family medicine residents. HT Update classification previously made by the research team. Peer review by Spanish HTAA. Qualitative and quantitative analysis of responses. Construction of a new and pilot study based on 12 evaluation reports of the HTAA. We obtained 11 thematic categories that are classified into 6 major head groups: 1, prevention technology; 2, diagnostic technology; 3, therapeutic technologies; 4, diagnostic and therapeutic technologies; 5, organizational technology, and 6, knowledge management and quality of care. In the pilot there was a good concordance in the classification of 8 of the 12 reports reviewed by physicians. Experts agree on 11 thematic categories of HT. A new classification of HT with double entry (Nature and purpose of HT) is proposed. APPLICABILITY: According to experts, the classification of the work of the HTAA may represent a useful tool to transfer and manage knowledge. Moreover, an adequate classification of the HTAA reports would help clinicians and other potential users to locate them and this can facilitate their dissemination. Copyright © 2010 SECA. Published by Elsevier Espana. All rights reserved.

[Role of contemporary pathological diagnostics in the personalized treatment of cancer].

PubMed

Tímár, József

2013-03-01

Due to the developments of pathology in the past decades (immunohistochemistry and molecular pathology) classification of cancers changed fundamentally, laying a ground for personalized management of cancer patients. Our picture of cancer is more complex today, identifying the genetic basis of the morphological variants. On the other hand, this picture has a much higher resolution enabling us to subclassify similar histological cancer types based on molecular markers. This redefined classification of cancers helps us to better predict the possible biological behavior of the disease and/or the therapeutic sensitivity, opening the way toward a more personalized treatment of this disease. The redefined molecular classification of cancer may affect the universal application of treatment protocols. To achieve this goal molecular diagnostics must be an integral and reimbursed part of the routine pathological diagnostics. On the other hand, it is time to extend the multidisciplinary team with molecular pathologist to improve the decision making process of the management of cancer patients.

Treatment of congential vascular disorders: classification, step program, and therapeutic procedures

NASA Astrophysics Data System (ADS)

Philipp, Carsten M.; Poetke, Margitta; Engel-Murke, Frank; Waldschmidt, J.; Berlien, Hans-Peter

1994-02-01

Because of the different step programs concerning the preoperative diagnostic and the onset of therapy for the various types of congenital vascular disorders (CVD) a clear classification is important. One has to discern the vascular malformations, including the port wine stain, from the real hemangiomas which are vascular tumors. As former classification, mostly based on histological findings, showed little evidence to a clinical step program, we developed a descriptive classification which allows an early differentiation between the two groups of CVD. In most cases this can be done by a precise medical history of the onset and development of the disorder, a close look to the clinical signs and by Duplex-Ultrasound and MRI-diagnostic. With this protocol and the case adapted use of different lasers and laser techniques we have not seen any severe complications as skin necrosis or nerve lesions.

Cell classification using big data analytics plus time stretch imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Jalali, Bahram; Chen, Claire L.; Mahjoubfar, Ata

2016-09-01

We show that blood cells can be classified with high accuracy and high throughput by combining machine learning with time stretch quantitative phase imaging. Our diagnostic system captures quantitative phase images in a flow microscope at millions of frames per second and extracts multiple biophysical features from individual cells including morphological characteristics, light absorption and scattering parameters, and protein concentration. These parameters form a hyperdimensional feature space in which supervised learning and cell classification is performed. We show binary classification of T-cells against colon cancer cells, as well classification of algae cell strains with high and low lipid content. The label-free screening averts the negative impact of staining reagents on cellular viability or cell signaling. The combination of time stretch machine vision and learning offers unprecedented cell analysis capabilities for cancer diagnostics, drug development and liquid biopsy for personalized genomics.

Classification and correlates of eating disorders among Blacks: findings from the National Survey of American Life.

PubMed

Taylor, Jacquelyn Y; Caldwell, Cleopatra Howard; Baser, Raymond E; Matusko, Niki; Faison, Nakesha; Jackson, James S

2013-02-01

To assess classification adjustments and examine correlates of eating disorders among Blacks. The National Survey of American Life (NSAL) was conducted from 2001-2003 and consisted of adults (n=5,191) and adolescents (n=1,170). The World Mental Health Composite International Diagnostic Interview (WMH-CIDI-World Health Organization 2004-modified) and DSM-IV-TR eating disorder criteria were used. Sixty-six percent of African American and 59% Caribbean Black adults were overweight or obese, while 30% and 29% of adolescents were overweight or obese. Although lifetime rates of anorexia nervosa and bulimia nervosa were low, binge eating disorder was high for both ethnic groups among adults and adolescents. Eliminating certain classification criteria resulted in higher rates of eating disorders for all groups. Culturally sensitive criteria should be incorporated into future versions of Diagnostic Statistical Manual (DSM) classifications for eating disorders that consider within-group ethnic variations.

Recent Changes of Classification for Squamous Intraepithelial Lesions of the Head and Neck.

PubMed

Cho, Kyung-Ja; Song, Joon Seon

2018-05-18

- Interpretation of atypical squamous lesions of the head and neck has always been a nettlesome task for pathologists. Moreover, many different grading systems for squamous intraepithelial lesions have been proposed in past decades. The recent World Health Organization 2017 classification presents 2 types of 2-tier systems for laryngeal and oral precursor lesions. - To review the recent changes in classification and the clinical significance for squamous intraepithelial lesions of the head and neck. - Personal experience and data from the literature. - The 2-tier grading system for laryngeal dysplasia, presented by World Health Organization in 2017, is expected to improve diagnostic reproducibility and clinical implication. However, the diagnostic criteria for low-grade dysplasia do not distinguish it clearly from basal cell hyperplasia. The World Health Organization 2017 classification of oral epithelial dysplasia remains unclear, and complicated and variable grading systems still make head and neck intraepithelial lesions difficult to interpret.

Mixed anxiety depression should not be included in DSM-5.

PubMed

Batelaan, Neeltje M; Spijker, Jan; de Graaf, Ron; Cuijpers, Pim

2012-06-01

Subthreshold anxiety and subthreshold depressive symptoms often co-occur in the general population and in primary care. Based on their associated significant distress and impairment, a psychiatric classification seems justified. To enable classification, mixed anxiety depression (MAD) has been proposed as a new diagnostic category in DSM-5. In this report, we discuss arguments against the classification of MAD. More research is needed before reifying a new category we know so little about. Moreover, we argue that in patients with MAD symptoms and a history of an anxiety or depressive disorder, symptoms should be labeled as part of the course trajectories of these disorders, rather than calling it a different diagnostic entity. In patients with incident co-occurring subthreshold anxiety and subthreshold depression, subthreshold categories of both anxiety and depression could be classified to maintain a consistent classification system at both threshold and subthreshold levels.

Introducing a New Classification of Early Childhood Disorders: DC:0-5™

ERIC Educational Resources Information Center

Zeanah, Charles H.; Carter, Alice S.; Cohen, Julie; Egger, Helen; Gleason, Mary Margaret; Keren, Miri; Lieberman, Alicia; Mulrooney, Kathleen; Oser, Cindy

2017-01-01

This article introduces the revised and updated "DC:0-5™: Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood." The authors describe the past and current efforts to create a developmentally based classification system for very young children. DC:0-3, published in 1994 by ZERO TO THREE,…

Measures of Agreement Between Many Raters for Ordinal Classifications

PubMed Central

Nelson, Kerrie P.; Edwards, Don

2015-01-01

Screening and diagnostic procedures often require a physician's subjective interpretation of a patient's test result using an ordered categorical scale to define the patient's disease severity. Due to wide variability observed between physicians’ ratings, many large-scale studies have been conducted to quantify agreement between multiple experts’ ordinal classifications in common diagnostic procedures such as mammography. However, very few statistical approaches are available to assess agreement in these large-scale settings. Existing summary measures of agreement rely on extensions of Cohen's kappa [1 - 5]. These are prone to prevalence and marginal distribution issues, become increasingly complex for more than three experts or are not easily implemented. Here we propose a model-based approach to assess agreement in large-scale studies based upon a framework of ordinal generalized linear mixed models. A summary measure of agreement is proposed for multiple experts assessing the same sample of patients’ test results according to an ordered categorical scale. This measure avoids some of the key flaws associated with Cohen's kappa and its extensions. Simulation studies are conducted to demonstrate the validity of the approach with comparison to commonly used agreement measures. The proposed methods are easily implemented using the software package R and are applied to two large-scale cancer agreement studies. PMID:26095449

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning with Deep Belief Networks.

PubMed

Ying, Jun; Dutta, Joyita; Guo, Ning; Hu, Chenhui; Zhou, Dan; Sitek, Arkadiusz; Li, Quanzheng

2016-12-21

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A threelayer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. 10,300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a 10-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We thus demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Teaching DSM-III to clinicians. Some problems of the DSM-III system reducing reliability, using the diagnosis and classification of depressive disorders as an example.

PubMed

Malt, U F

1986-01-01

Experiences from teaching DSM-III to more than three hundred Norwegian psychiatrists and clinical psychologists suggest that reliable DSM-III diagnoses can be achieved within a few hours training with reference to the decision trees and the diagnostic criteria only. The diagnoses provided are more reliable than the corresponding ICD diagnoses which the participants were more familiar with. The three main sources of reduced reliability of the DSM-III diagnoses are related to: poor knowledge of the criteria which often is connected with failure of obtaining diagnostic key information during the clinical interview; unfamiliar concepts and vague or ambiguous criteria. The two first issues are related to the quality of the teaching of DSM-III. The third source of reduced reliability reflects unsolved validity issues. By using the classification of five affective case stories as examples, these sources of diagnostic pitfalls, reducing reliability and ways to overcome these problems when teaching the DSM-III system, are discussed. It is concluded that the DSM-III system of classification is easy to teach and that the system is superior to other classification systems available from a reliability point of view. The current version of the DSM-III system, however, partly owes a high degree of reliability to broad and heterogeneous diagnostic categories like the concept major depression, which may have questionable validity. Thus, the future revisions of the DSM-III system should, above all, address the issue of validity.

Diagnostic Principles of Peri-Implantitis: a Systematic Review and Guidelines for Peri-Implantitis Diagnosis Proposal.

PubMed

Ramanauskaite, Ausra; Juodzbalys, Gintaras

2016-01-01

To review and summarize the literature concerning peri-implantitis diagnostic parameters and to propose guidelines for peri-implantitis diagnosis. An electronic literature search was conducted of the MEDLINE (Ovid) and EMBASE databases for articles published between 2011 and 2016. Sequential screening at the title/abstract and full-text levels was performed. Systematic reviews/guidelines of consensus conferences proposing classification or suggesting diagnostic parameters for peri-implantitis in the English language were included. The review was recorded on PROSPERO system with the code CRD42016033287. The search resulted in 10 articles that met the inclusion criteria. Four were papers from consensus conferences, two recommended diagnostic guidelines, three proposed classification of peri-implantitis, and one suggested an index for implant success. The following parameters were suggested to be used for peri-implantitis diagnosis: pain, mobility, bleeding on probing, probing depth, suppuration/exudate, and radiographic bone loss. In all of the papers, different definitions of peri-implantitis or implant success, as well as different thresholds for the above mentioned clinical and radiographical parameters, were used. Current evidence rationale for the diagnosis of peri-implantitis and classification based on consecutive evaluation of soft-tissue conditions and the amount of bone loss were suggested. Currently there is no single uniform definition of peri-implantitis or the parameters that should be used. Rationale for diagnosis and prognosis of peri-implantitis as well as classification of the disease is proposed.

Spatially resolved bimodal spectroscopy for classification/evaluation of mouse skin inflammatory and pre-cancerous stages

NASA Astrophysics Data System (ADS)

Díaz-Ayil, Gilberto; Amouroux, Marine; Clanché, Fabien; Granjon, Yves; Blondel, Walter C. P. M.

2009-07-01

Spatially-resolved bimodal spectroscopy (multiple AutoFluorescence AF excitation and Diffuse Reflectance DR), was used in vivo to discriminate various healthy and precancerous skin stages in a pre-clinical model (UV-irradiated mouse): Compensatory Hyperplasia CH, Atypical Hyperplasia AH and Dysplasia D. A specific data preprocessing scheme was applied to intensity spectra (filtering, spectral correction and intensity normalization), and several sets of spectral characteristics were automatically extracted and selected based on their discrimination power, statistically tested for every pair-wise comparison of histological classes. Data reduction with Principal Components Analysis (PCA) was performed and 3 classification methods were implemented (k-NN, LDA and SVM), in order to compare diagnostic performance of each method. Diagnostic performance was studied and assessed in terms of Sensibility (Se) and Specificity (Sp) as a function of the selected features, of the combinations of 3 different inter-fibres distances and of the numbers of principal components, such that: Se and Sp ~ 100% when discriminating CH vs. others; Sp ~ 100% and Se > 95% when discriminating Healthy vs. AH or D; Sp ~ 74% and Se ~ 63% for AH vs. D.

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

PubMed

Gao, Chao; Sun, Hanbo; Wang, Tuo; Tang, Ming; Bohnen, Nicolaas I; Müller, Martijn L T M; Herman, Talia; Giladi, Nir; Kalinin, Alexandr; Spino, Cathie; Dauer, William; Hausdorff, Jeffrey M; Dinov, Ivo D

2018-05-08

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70-80%.

Raman spectroscopy detection of platelet for Alzheimer’s disease with predictive probabilities

NASA Astrophysics Data System (ADS)

Wang, L. J.; Du, X. Q.; Du, Z. W.; Yang, Y. Y.; Chen, P.; Tian, Q.; Shang, X. L.; Liu, Z. C.; Yao, X. Q.; Wang, J. Z.; Wang, X. H.; Cheng, Y.; Peng, J.; Shen, A. G.; Hu, J. M.

2014-08-01

Alzheimer’s disease (AD) is a common form of dementia. Early and differential diagnosis of AD has always been an arduous task for the medical expert due to the unapparent early symptoms and the currently imperfect imaging examination methods. Therefore, obtaining reliable markers with clinical diagnostic value in easily assembled samples is worthy and significant. Our previous work with laser Raman spectroscopy (LRS), in which we detected platelet samples of different ages of AD transgenic mice and non-transgenic controls, showed great effect in the diagnosis of AD. In addition, a multilayer perception network (MLP) classification method was adopted to discriminate the spectral data. However, there were disturbances, which were induced by noise from the machines and so on, in the data set; thus the MLP method had to be trained with large-scale data. In this paper, we aim to re-establish the classification models of early and advanced AD and the control group with fewer features, and apply some mechanism of noise reduction to improve the accuracy of models. An adaptive classification method based on the Gaussian process (GP) featured, with predictive probabilities, is proposed, which could tell when a data set is related to some kind of disease. Compared with MLP on the same feature set, GP showed much better performance in the experimental results. What is more, since the spectra of platelets are isolated from AD, GP has good expansibility and can be applied in diagnosis of many other similar diseases, such as Parkinson’s disease (PD). Spectral data of 4 month and 12 month AD platelets, as well as control data, were collected. With predictive probabilities, the proposed GP classification method improved the diagnostic sensitivity to nearly 100%. Samples were also collected from PD platelets as classification and comparison to the 12 month AD. The presented approach and our experiments indicate that utilization of GP with predictive probabilities in platelet LRS detection analysis turns out to be more accurate for early and differential diagnosis of AD and has a wide application prospect.

R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification

PubMed Central

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

2015-01-01

PRIMsrc is a novel implementation of a non-parametric bump hunting procedure, based on the Patient Rule Induction Method (PRIM), offering a unified treatment of outcome variables, including censored time-to-event (Survival), continuous (Regression) and discrete (Classification) responses. To fit the model, it uses a recursive peeling procedure with specific peeling criteria and stopping rules depending on the response. To validate the model, it provides an objective function based on prediction-error or other specific statistic, as well as two alternative cross-validation techniques, adapted to the task of decision-rule making and estimation in the three types of settings. PRIMsrc comes as an open source R package, including at this point: (i) a main function for fitting a Survival Bump Hunting model with various options allowing cross-validated model selection to control model size (#covariates) and model complexity (#peeling steps) and generation of cross-validated end-point estimates; (ii) parallel computing; (iii) various S3-generic and specific plotting functions for data visualization, diagnostic, prediction, summary and display of results. It is available on CRAN and GitHub. PMID:26798326

Predictive Validity of DSM-IV and ICD-10 Criteria for ADHD and Hyperkinetic Disorder

ERIC Educational Resources Information Center

Lee, Soyoung I.; Schachar, Russell J.; Chen, Shirley X.; Ornstein, Tisha J.; Charach, Alice; Barr, Cathy; Ickowicz, Abel

2008-01-01

Background: The goal of this study was to compare the predictive validity of the two main diagnostic schemata for childhood hyperactivity--attention-deficit hyperactivity disorder (ADHD; "Diagnostic and Statistical Manual"-IV) and hyperkinetic disorder (HKD; "International Classification of Diseases"-10th Edition). Methods: Diagnostic criteria for…

Nearest Neighbor Classification Using a Density Sensitive Distance Measurement

DTIC Science & Technology

2009-09-01

both the proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and...proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and the MNIST...35 1. The Wisconsin Diagnostic Breast Cancer (WDBC) Dataset..........35 2. The

Diagnoses and Presenting Symptoms in an Infant Psychiatry Clinic: Comparison of Two Diagnostic Systems.

ERIC Educational Resources Information Center

Frankel, Karen A.; Boyum, Lisa A.; Harmon, Robert J.

2004-01-01

Objective: To present data from a general infant psychiatry clinic, including range and frequency of presenting symptoms, relationship between symptoms and diagnoses, and comparison of two diagnostic systems, DSM-IV and Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC: 0-3). Method: A…

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

Coronary artery calcification (CAC) classification with deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Liu, Xiuming; Wang, Shice; Deng, Yufeng; Chen, Kuan

2017-03-01

Coronary artery calcification (CAC) is a typical marker of the coronary artery disease, which is one of the biggest causes of mortality in the U.S. This study evaluates the feasibility of using a deep convolutional neural network (DCNN) to automatically detect CAC in X-ray images. 1768 posteroanterior (PA) view chest X-Ray images from Sichuan Province Peoples Hospital, China were collected retrospectively. Each image is associated with a corresponding diagnostic report written by a trained radiologist (907 normal, 861 diagnosed with CAC). Onequarter of the images were randomly selected as test samples; the rest were used as training samples. DCNN models consisting of 2,4,6 and 8 convolutional layers were designed using blocks of pre-designed CNN layers. Each block was implemented in Theano with Graphics Processing Units (GPU). Human-in-the-loop learning was also performed on a subset of 165 images with framed arteries by trained physicians. The results from the DCNN models were compared to the diagnostic reports. The average diagnostic accuracies for models with 2,4,6,8 layers were 0.85, 0.87, 0.88, and 0.89 respectively. The areas under the curve (AUC) were 0.92, 0.95, 0.95, and 0.96. As the model grows deeper, the AUC or diagnostic accuracies did not have statistically significant changes. The results of this study indicate that DCNN models have promising potential in the field of intelligent medical image diagnosis practice.

Beyond DSM-5: an alternative approach to assessing Social Anxiety Disorder.

PubMed

Skocic, Sonja; Jackson, Henry; Hulbert, Carol

2015-03-01

This article focuses on the Diagnostic and Statistical Manual of Mental Disorders (DSM) classification of Social Anxiety Disorder (SAD). The article details the diagnostic criteria for SAD that have evolved in the various editions and demonstrates that whilst there have been some positive steps taken to more comprehensively define the disorder, further revision is necessary. It will be argued that the DSM-5 (APA, 2013) has made some changes to the diagnostic criteria of SAD that do not seem to be completely in line with theory and research and do not describe SAD effectively in terms of both diversity and presentation. This article concludes with the presentation of a proposed set of diagnostic criteria that address the concerns raised in the article. The proposed criteria reflect a hybrid categorical-dimensional system of classification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Why is the Diagnostic and Statistical Manual of Mental Disorders so hard to revise? Path-dependence and "lock-in" in classification.

PubMed

Cooper, Rachel

2015-06-01

The latest edition of the Diagnostic and Statistical Manual of Mental Disorders, the D.S.M.-5, was published in May 2013. In the lead up to publication, radical changes to the classification were anticipated; there was widespread dissatisfaction with the previous edition and it was accepted that a "paradigm shift" might be required. In the end, however, and despite huge efforts at revision, the published D.S.M.-5 differs far less than originally envisaged from its predecessor. This paper considers why it is that revising the D.S.M. has become so difficult. The D.S.M. is such an important classification that this question is worth asking in its own right. The case of the D.S.M. can also serve as a study for considering stasis in classification more broadly; why and how can classifications become resistant to change? I suggest that classifications like the D.S.M. can be thought of as forming part of the infrastructure of science, and have much in common with material infrastructure. In particular, as with material technologies, it is possible for "path dependent" development to cause a sub-optimal classification to become "locked in" and hard to replace. Copyright © 2015 Elsevier Ltd. All rights reserved.

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

PubMed Central

Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

2015-01-01

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification. PMID:26246834

Do mental health professionals use diagnostic classifications the way we think they do? A global survey

PubMed Central

First, Michael B.; Rebello, Tahilia J.; Keeley, Jared W.; Bhargava, Rachna; Dai, Yunfei; Kulygina, Maya; Matsumoto, Chihiro; Robles, Rebeca; Stona, Anne‐Claire; Reed, Geoffrey M.

2018-01-01

We report on a global survey of diagnosing mental health professionals, primarily psychiatrists, conducted as a part of the development of the ICD‐11 mental and behavioural disorders classification. The survey assessed these professionals' use of various components of the ICD‐10 and the DSM, their attitudes concerning the utility of these systems, and usage of “residual” (i.e., “other” or “unspecified”) categories. In previous surveys, most mental health professionals reported they often use a formal classification system in everyday clinical work, but very little is known about precisely how they are using those systems. For example, it has been suggested that most clinicians employ only the diagnostic labels or codes from the ICD‐10 in order to meet administrative requirements. The present survey was conducted with clinicians who were members of the Global Clinical Practice Network (GCPN), established by the World Health Organization as a tool for global participation in ICD‐11 field studies. A total of 1,764 GCPN members from 92 countries completed the survey, with 1,335 answering the questions with reference to the ICD‐10 and 429 to the DSM (DSM‐IV, DSM‐IV‐TR or DSM‐5). The most frequent reported use of the classification systems was for administrative or billing purposes, with 68.1% reporting often or routinely using them for that purpose. A bit more than half (57.4%) of respondents reported often or routinely going through diagnostic guidelines or criteria systematically to determine whether they apply to individual patients. Although ICD‐10 users were more likely than DSM‐5 users to utilize the classification for administrative purposes, other differences were either slight or not significant. Both classifications were rated to be most useful for assigning a diagnosis, communicating with other health care professionals and teaching, and least useful for treatment selection and determining prognosis. ICD‐10 was rated more useful than DSM‐5 for administrative purposes. A majority of clinicians reported using “residual” categories at least sometimes, with around 12% of ICD‐10 users and 19% of DSM users employing them often or routinely, most commonly for clinical presentations that do not conform to a specific diagnostic category or when there is insufficient information to make a more specific diagnosis. These results provide the most comprehensive available information about the use of diagnostic classifications of mental disorders in ordinary clinical practice. PMID:29856559

Do mental health professionals use diagnostic classifications the way we think they do? A global survey.

PubMed

First, Michael B; Rebello, Tahilia J; Keeley, Jared W; Bhargava, Rachna; Dai, Yunfei; Kulygina, Maya; Matsumoto, Chihiro; Robles, Rebeca; Stona, Anne-Claire; Reed, Geoffrey M

2018-06-01

We report on a global survey of diagnosing mental health professionals, primarily psychiatrists, conducted as a part of the development of the ICD-11 mental and behavioural disorders classification. The survey assessed these professionals' use of various components of the ICD-10 and the DSM, their attitudes concerning the utility of these systems, and usage of "residual" (i.e., "other" or "unspecified") categories. In previous surveys, most mental health professionals reported they often use a formal classification system in everyday clinical work, but very little is known about precisely how they are using those systems. For example, it has been suggested that most clinicians employ only the diagnostic labels or codes from the ICD-10 in order to meet administrative requirements. The present survey was conducted with clinicians who were members of the Global Clinical Practice Network (GCPN), established by the World Health Organization as a tool for global participation in ICD-11 field studies. A total of 1,764 GCPN members from 92 countries completed the survey, with 1,335 answering the questions with reference to the ICD-10 and 429 to the DSM (DSM-IV, DSM-IV-TR or DSM-5). The most frequent reported use of the classification systems was for administrative or billing purposes, with 68.1% reporting often or routinely using them for that purpose. A bit more than half (57.4%) of respondents reported often or routinely going through diagnostic guidelines or criteria systematically to determine whether they apply to individual patients. Although ICD-10 users were more likely than DSM-5 users to utilize the classification for administrative purposes, other differences were either slight or not significant. Both classifications were rated to be most useful for assigning a diagnosis, communicating with other health care professionals and teaching, and least useful for treatment selection and determining prognosis. ICD-10 was rated more useful than DSM-5 for administrative purposes. A majority of clinicians reported using "residual" categories at least sometimes, with around 12% of ICD-10 users and 19% of DSM users employing them often or routinely, most commonly for clinical presentations that do not conform to a specific diagnostic category or when there is insufficient information to make a more specific diagnosis. These results provide the most comprehensive available information about the use of diagnostic classifications of mental disorders in ordinary clinical practice. © 2018 World Psychiatric Association.

Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

PubMed

Igual, Laura; Soliva, Joan Carles; Escalera, Sergio; Gimeno, Roger; Vilarroya, Oscar; Radeva, Petia

2012-12-01

We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. Copyright © 2012 Elsevier Ltd. All rights reserved.

Investigating the cross-cultural validity of DSM-5 autism spectrum disorder: evidence from Finnish and UK samples.

PubMed

Mandy, William; Charman, Tony; Puura, Kaija; Skuse, David

2014-01-01

The recent Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) reformulation of autism spectrum disorder has received empirical support from North American and UK samples. Autism spectrum disorder is an increasingly global diagnosis, and research is needed to discover how well it generalises beyond North America and the United Kingdom. We tested the applicability of the DSM-5 model to a sample of Finnish young people with autism spectrum disorder (n = 130) or the broader autism phenotype (n = 110). Confirmatory factor analysis tested the DSM-5 model in Finland and compared the fit of this model between Finnish and UK participants (autism spectrum disorder, n = 488; broader autism phenotype, n = 220). In both countries, autistic symptoms were measured using the Developmental, Diagnostic and Dimensional Interview. Replicating findings from English-speaking samples, the DSM-5 model fitted well in Finnish autism spectrum disorder participants, outperforming a Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV) model. The DSM-5 model fitted equally well in Finnish and UK autism spectrum disorder samples. Among broader autism phenotype participants, this model fitted well in the United Kingdom but poorly in Finland, suggesting that cross-cultural variability may be greatest for milder autistic characteristics. We encourage researchers with data from other cultures to emulate our methodological approach, to map any cultural variability in the manifestation of autism spectrum disorder and the broader autism phenotype. This would be especially valuable given the ongoing revision of the International Classification of Diseases-11th Edition, the most global of the diagnostic manuals.

Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model

NASA Astrophysics Data System (ADS)

Liu, Sijia; Sa, Ruhan; Maguire, Orla; Minderman, Hans; Chaudhary, Vipin

2015-03-01

Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.

A comparison of DSM-IV-TR and DSM-5 definitions for sexual dysfunctions: critiques and challenges.

PubMed

Sungur, Mehmet Z; Gündüz, Anil

2014-02-01

The diagnostic criteria of sexual dysfunctions (SDs) are paramount for the development of sexual medicine as reliable diagnoses are essential to guide treatment plans. Prior Diagnostic and Statistical Manual of Mental Disorders (DSM) classifications based definitions of SD mostly on expert opinions and included imprecise terms. The validity of diagnoses of SD has only recently been challenged, and efforts are made to make more operational definitions. This paper aims to compare and contrast the recently released Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) diagnostic criteria of SD with that of Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition Text Revision (DSM-IV-TR) and explains the rationale for making changes in the new DSM-5. It also aims to address some issues to be considered further for the future. Online proposed American Psychiatric Association website DSM-5, the new released DSM-5, and DSM-IV-TR diagnostic criteria for SD were thoroughly inspected, and an extensive literature search was performed for comparative reasons. Changes in diagnostic criteria of DSM-5 were detected, and DSM-IV-TR and DSM-5 diagnostic criteria for SD were compared and contrasted. Diagnostic criteria were more operationalized, and explicit duration and frequency criteria were set up in DSM-5 for purposes of good clinical research. Classifications based on simple linear sexual response were abandoned, and diagnostic classifications were separately made for males and females. Desire and arousal disorders in women were merged. Drifting apart from linear sexual response cycle may be an advancement in establishing specific diagnostic criteria for different genders. However, it is still a question of debate whether there is enough evidence to lump sexual interest and arousal disorders in females. Making more precise definitions is important to differentiate disorders from other transient conditions. However, there is still room to improve our definitions and find a way to include gay and lesbian individuals. Further discussions and debates are expected to be continued in the future. © 2013 International Society for Sexual Medicine.

The road to 11th edition of the International Classification of Diseases: trajectories of scientific consensus and contested science in the classification of intellectual disability/intellectual developmental disorders.

PubMed

Salvador-Carulla, Luis; Bertelli, Marco; Martinez-Leal, Rafael

2018-03-01

To increase the expert knowledge-base on intellectual developmental disorders (IDDs) by investigating the typology trajectories of consensus formation in the classification systems up to the 11th edition of the International Classification of Diseases (ICD-11). This expert review combines an analysis of key recent literature and the revision of the consensus formation and contestation in the expert committees contributing to the classification systems since the 1950s. Historically two main approaches have contributed to the development of this knowledge-base: a neurodevelopmental-clinical approach and a psychoeducational-social approach. These approaches show a complex interaction throughout the history of IDD and have had a diverse influence on its classification. Although in theory Diagnostic and Statistical Manual (DSM)-5 and ICD adhere to the neurodevelopmental-clinical model, the new definition in the ICD-11 follows a restrictive normality approach to intellectual quotient and to the measurement of adaptive behaviour. On the contrary DSM-5 is closer to the recommendations made by the WHO 'Working Group on Mental Retardation' for ICD-11 for an integrative approach. A cyclical pattern of consensus formation has been identified in IDD. The revision of the three major classification systems in the last decade has increased the terminological and conceptual variability and the overall scientific contestation on IDD.

Counselor-Client Diagnostic Agreement and Perceived Outcomes of Counseling: A Progress Report.

ERIC Educational Resources Information Center

Hurst, James C.; And Others

This study was designed to investigate the effect of congruity of counselor and client diagnoses upon client-perceived success in counseling. The Missouri Diagnostic Classification Plan (MDCP) was used as the basic diagnostic method. Agreement in the 15 categories was related to client-perceived success of counseling. Subjects, all clients at the…

Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

NASA Astrophysics Data System (ADS)

Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

2013-03-01

The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

Nanomechanics of carbon nanotubes

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Kumar, Prashanth S.; Varadan, Vijay K.

2017-04-01

This review focusses on introducing the mechanics in carbon nanotubes (CNT), and the major applications of CNT and its composites in biomedicine. It emphasizes the nanomechanics of these materials by reviewing the widely followed experimental methods, theoretical models, simulations, classification, segregation and applications the aforementioned materials. First, several mechanical properties contributing to the classification of the CNT, for various biomedicine applications, are discussed in detail to provide a cursory glance at the uses of CNT. The mechanics of CNT discussed in this paper include: elasticity, stress, tension, compression, nano-scale mechanics. In addition to these basic properties, a brief introduction about nanoscale composites is given. Second, a brief review on some of the major applications of CNT in biomedicine including drug delivery, therapeutics, diagnostics and regenerative medicine is given.

When do latent class models overstate accuracy for diagnostic and other classifiers in the absence of a gold standard?

PubMed

Spencer, Bruce D

2012-06-01

Latent class models are increasingly used to assess the accuracy of medical diagnostic tests and other classifications when no gold standard is available and the true state is unknown. When the latent class is treated as the true class, the latent class models provide measures of components of accuracy including specificity and sensitivity and their complements, type I and type II error rates. The error rates according to the latent class model differ from the true error rates, however, and empirical comparisons with a gold standard suggest the true error rates often are larger. We investigate conditions under which the true type I and type II error rates are larger than those provided by the latent class models. Results from Uebersax (1988, Psychological Bulletin 104, 405-416) are extended to accommodate random effects and covariates affecting the responses. The results are important for interpreting the results of latent class analyses. An error decomposition is presented that incorporates an error component from invalidity of the latent class model. © 2011, The International Biometric Society.

A Diagnostic Marker to Discriminate Childhood Apraxia of Speech From Speech Delay: I. Development and Description of the Pause Marker

PubMed Central

Strand, Edythe A.; Fourakis, Marios; Jakielski, Kathy J.; Hall, Sheryl D.; Karlsson, Heather B.; Mabie, Heather L.; McSweeny, Jane L.; Tilkens, Christie M.; Wilson, David L.

2017-01-01

Purpose The goal of this article (PM I) is to describe the rationale for and development of the Pause Marker (PM), a single-sign diagnostic marker proposed to discriminate early or persistent childhood apraxia of speech from speech delay. Method The authors describe and prioritize 7 criteria with which to evaluate the research and clinical utility of a diagnostic marker for childhood apraxia of speech, including evaluation of the present proposal. An overview is given of the Speech Disorders Classification System, including extensions completed in the same approximately 3-year period in which the PM was developed. Results The finalized Speech Disorders Classification System includes a nosology and cross-classification procedures for childhood and persistent speech disorders and motor speech disorders (Shriberg, Strand, & Mabie, 2017). A PM is developed that provides procedural and scoring information, and citations to papers and technical reports that include audio exemplars of the PM and reference data used to standardize PM scores are provided. Conclusions The PM described here is an acoustic-aided perceptual sign that quantifies one aspect of speech precision in the linguistic domain of phrasing. This diagnostic marker can be used to discriminate early or persistent childhood apraxia of speech from speech delay. PMID:28384779

Clinical Data as an Adjunct to Ultrasound Reduces the False-Negative Malignancy Rate in BI-RADS 3 Breast Lesions

PubMed Central

Ackermann, S.; Schoenenberger, C.-A.; Zanetti-Dällenbach, R.

2016-01-01

Purpose: Ultrasound (US) is a well-established diagnostic procedure for breast examination. We investigated the malignancy rate in solid breast lesions according to their BI-RADS classification with a particular focus on false-negative BI-RADS 3 lesions. We examined whether patient history and clinical findings could provide additional information that would help determine further diagnostic steps in breast lesions. Materials and Methods: We conducted a retrospective study by exploring US BI-RADS in 1469 breast lesions of 1201 patients who underwent minimally invasive breast biopsy (MIBB) from January 2002 to December 2011. Results: The overall sensitivity and specificity of BI-RADS classification was 97.4% and 66.4%, respectively, with a positive (PPV) and negative predictive value (NPV) of 65% and 98%, respectively. In 506 BI-RADS 3 lesions, histology revealed 15 malignancies (2.4% malignancy rate), which corresponds to a false-negative rate (FNR) of 2.6%. Clinical evaluation and patient requests critically influenced the further diagnostic procedure, thereby prevailing over the recommendation given by the BI-RADS 3 classification. Conclusion: Clinical criteria including age, family and personal history, clinical examination, mammography and patient choice ensure adequate diagnostic procedures such as short-term follow-up or MIBB in patients with lesions classified as US-BI-RADS 3. PMID:27689181

An evaluation of ICD-11 posttraumatic stress disorder criteria in two samples of adolescents and young adults exposed to mass shootings: factor analysis and comparisons to ICD-10 and DSM-IV.

PubMed

Haravuori, Henna; Kiviruusu, Olli; Suomalainen, Laura; Marttunen, Mauri

2016-05-12

The proposed posttraumatic stress disorder (PTSD) criteria for the International Classification of Diseases (ICD) 11th revision are simpler than the criteria in ICD-10, DSM-IV or DSM-5. The aim of this study was to evaluate the ICD-11 PTSD factor structure in samples of young people, and to compare PTSD prevalence rates and diagnostic agreement between the different diagnostic systems. Possible differences in clinical characteristics of the PTSD cases identified by ICD-11, ICD-10 and DSM-IV are explored. Two samples of adolescents and young adults were followed after exposure to similar mass shooting incidents in their schools. Semi-structured diagnostic interviews were performed to assess psychiatric diagnoses and PTSD symptom scores (N = 228, mean age 17.6 years). PTSD symptom item scores were used to compose diagnoses according to the different classification systems. Confirmatory factor analyses indicated that the proposed ICD-11 PTSD symptoms represented two rather than three factors; re-experiencing and avoidance symptoms comprised one factor and hyperarousal symptoms the other factor. In the studied samples, the three-factor ICD-11 criteria identified 51 (22.4%) PTSD cases, the two-factor ICD-11 identified 56 (24.6%) cases and the DSM-IV identified 43 (18.9%) cases, while the number of cases identified by ICD-10 was larger, being 85 (37.3%) cases. Diagnostic agreement of the ICD-11 PTSD criteria with ICD-10 and DSM-IV was moderate, yet the diagnostic agreement turned to be good when an impairment criterion was imposed on ICD-10. Compared to ICD-11, ICD-10 identified cases with less severe trauma exposure and posttraumatic symptoms and DSM-IV identified cases with less severe trauma exposure. The findings suggest that the two-factor model of ICD-11 PTSD is preferable to the three-factor model. The proposed ICD-11 criteria are more restrictive compared to the ICD-10 criteria. There were some differences in the clinical characteristics of the PTSD cases identified by ICD-11, when compared to ICD-10 and DSM-IV.

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.

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

PubMed

Park, Seong Ho; Han, Kyunghwa

2018-03-01

The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics. First, statistical methods for assessing the discrimination and calibration performances of a diagnostic or predictive model are summarized. Next, the effects of disease manifestation spectrum and disease prevalence on the performance results are explained, followed by a discussion of the difference between evaluating the performance with use of internal and external datasets, the importance of using an adequate external dataset obtained from a well-defined clinical cohort to avoid overestimating the clinical performance as a result of overfitting in high-dimensional or overparameterized classification model and spectrum bias, and the essentials for achieving a more robust clinical evaluation. Finally, the authors review the role of clinical trials and observational outcome studies for ultimate clinical verification of diagnostic or predictive artificial intelligence tools through patient outcomes, beyond performance metrics, and how to design such studies. © RSNA, 2018.

Spectral areas and ratios classifier algorithm for pancreatic tissue classification using optical spectroscopy

NASA Astrophysics Data System (ADS)

Chandra, Malavika; Scheiman, James; Simeone, Diane; McKenna, Barbara; Purdy, Julianne; Mycek, Mary-Ann

2010-01-01

Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.

Plus disease in retinopathy of prematurity: a continuous spectrum of vascular abnormality as basis of diagnostic variability

PubMed Central

Campbell, J. Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D.; Hutcheson, Kelly; Shapiro, Michael J.; Repka, Michael X.; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E.; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify patterns of inter-expert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). Design We developed two datasets of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP study, and determined a consensus reference standard diagnosis (RSD) for each image, based on 3 independent image graders and the clinical exam. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Subjects, Participants, and/or Controls Images obtained during routine ROP screening in neonatal intensive care units. 8 participating experts with >10 years of clinical ROP experience and >5 peer-reviewed ROP publications. Methods, Intervention, or Testing Expert classification of images of plus disease in ROP. Main Outcome Measures Inter-expert agreement (weighted kappa statistic), and agreement and bias on ordinal classification between experts (ANOVA) and the RSD (percent agreement). Results There was variable inter-expert agreement on diagnostic classifications between the 8 experts and the RSD (weighted kappa 0 – 0.75, mean 0.30). RSD agreement ranged from 80 – 94% agreement for the dataset of 100 images, and 29 – 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and pre-plus disease. The two-way ANOVA model suggested a highly significant effect of both image and user on the average score (P<0.05, adjusted R2=0.82 for dataset A, and P< 0.05 and adjusted R2 =0.6615 for dataset B). Conclusions and Relevance There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different “cut-points” for the amounts of vascular abnormality required for presence of plus and pre-plus disease. This has important implications for research, teaching and patient care for ROP, and suggests that a continuous ROP plus disease severity score may more accurately reflect the behavior of expert ROP clinicians, and may better standardize classification in the future. PMID:27591053

Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability.

PubMed

Campbell, J Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D; Hutcheson, Kelly; Shapiro, Michael J; Repka, Michael X; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E; Chan, R V Paul; Chiang, Michael F

2016-11-01

To identify patterns of interexpert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). We developed 2 datasets of clinical images as part of the Imaging and Informatics in ROP study and determined a consensus reference standard diagnosis (RSD) for each image based on 3 independent image graders and the clinical examination results. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Eight participating experts with more than 10 years of clinical ROP experience and more than 5 peer-reviewed ROP publications who analyzed images obtained during routine ROP screening in neonatal intensive care units. Expert classification of images of plus disease in ROP. Interexpert agreement (weighted κ statistic) and agreement and bias on ordinal classification between experts (analysis of variance [ANOVA]) and the RSD (percent agreement). There was variable interexpert agreement on diagnostic classifications between the 8 experts and the RSD (weighted κ, 0-0.75; mean, 0.30). The RSD agreement ranged from 80% to 94% for the dataset of 100 images and from 29% to 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and preplus disease. The 2-way ANOVA model suggested a highly significant effect of both image and user on the average score (dataset A: P < 0.05 and adjusted R 2  = 0.82; and dataset B: P < 0.05 and adjusted R 2  = 0.6615). There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different cut points for the amounts of vascular abnormality required for presence of plus and preplus disease. This has important implications for research, teaching, and patient care for ROP and suggests that a continuous ROP plus disease severity score may reflect more accurately the behavior of expert ROP clinicians and may better standardize classification in the future. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Utility of the 3Di Short Version for the Diagnostic Assessment of Autism Spectrum Disorder and Compatibility with DSM-5.

PubMed

Slappendel, Geerte; Mandy, William; van der Ende, Jan; Verhulst, Frank C; van der Sijde, Ad; Duvekot, Jorieke; Skuse, David; Greaves-Lord, Kirstin

2016-05-01

The Developmental Diagnostic Dimensional Interview-short version (3Di-sv) provides a brief standardized parental interview for diagnosing autism spectrum disorder (ASD). This study explored its validity, and compatibility with DSM-5 ASD. 3Di-sv classifications showed good sensitivity but low specificity when compared to ADOS-2-confirmed clinical diagnosis. Confirmatory factor analyses found a better fit against a DSM-5 model than a DSM-IV-TR model of ASD. Exploration of the content validity of the 3Di-sv for the DSM-5 revealed some construct underrepresentation, therefore we obtained data from a panel of 3Di-trained clinicians from ASD-specialized centers to recommend items to fill these gaps. Taken together, the 3Di-sv provides a solid basis to create a similar instrument suitable for DSM-5. Concrete recommendations are provided to improve DSM-5 compatibility.

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, kai

2007-01-01

Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

Selective Immunophenotyping for Diagnosis of B-cell Neoplasms: Immunohistochemistry and Flow Cytometry Strategies and Results

PubMed Central

Boyd, Scott D.; Natkunam, Yasodha; Allen, John R.; Warnke, Roger A.

2012-01-01

Determining the immunophenotype of hematologic malignancies is now an indispensible part of diagnostic classification, and can help to guide therapy, or to predict clinical outcome. Diagnostic workup should be guided by morphologic findings and evaluate clinically important markers, but ideally should avoid the use of overly-broad panels of immunostains that can reveal incidental findings of uncertain significance and give rise to increased costs. Here, we outline our approach to diagnosis of B cell neoplasms, combining histologic and clinical data with tailored panels of immunophenotyping reagents, in the context of the 2008 World Health Organization classification. We present data from cases seen at our institution from 2004-8 using this approach, to provide a practical reference for findings seen in daily diagnostic practice. PMID:22820658

Evaluation of diagnostic criteria for panic attack using item response theory: findings from the National Comorbidity Survey in USA.

PubMed

Ietsugu, Tetsuji; Sukigara, Masune; Furukawa, Toshiaki A

2007-12-01

The dichotomous diagnostic systems such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) lose much important information concerning what each symptom can offer. This study explored the characteristics and performances of DSM-IV and ICD-10 diagnostic criteria items for panic attack using modern item response theory (IRT). The National Comorbidity Survey used the Composite International Diagnostic Interview to assess 14 DSM-IV and ICD-10 panic attack diagnostic criteria items in the general population in the USA. The dimensionality and measurement properties of these items were evaluated using dichotomous factor analysis and the two-parameter IRT model. A total of 1213 respondents reported at least one subsyndromal or syndromal panic attack in their lifetime. Factor analysis indicated that all items constitute a unidimensional construct. The two-parameter IRT model produced meaningful and interpretable results. Among items with high discrimination parameters, the difficulty parameter for "palpitation" was relatively low, while those for "choking," "fear of dying" and "paresthesia" were relatively high. Several items including "dry mouth" and "fear of losing control" had low discrimination parameters. The item characteristics of diagnostic criteria among help-seeking clinical populations may be different from those that we observed in the general population and deserve further examination. "Paresthesia," "choking" and "fear of dying" can be thought to be good indicators of severe panic attacks, while "palpitation" can discriminate well between cases and non-cases at low level of panic attack severity. Items such as "dry mouth" would contribute less to the discrimination.

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

NASA Astrophysics Data System (ADS)

Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

2017-11-01

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

Multivariate Analysis As a Support for Diagnostic Flowcharts in Allergic Bronchopulmonary Aspergillosis: A Proof-of-Concept Study.

PubMed

Vitte, Joana; Ranque, Stéphane; Carsin, Ania; Gomez, Carine; Romain, Thomas; Cassagne, Carole; Gouitaa, Marion; Baravalle-Einaudi, Mélisande; Bel, Nathalie Stremler-Le; Reynaud-Gaubert, Martine; Dubus, Jean-Christophe; Mège, Jean-Louis; Gaudart, Jean

2017-01-01

Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti- Aspergillus fumigatus ( Af ) IgE, anti- Af "precipitins," and anti- Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af -sensitized patients at risk for ABPA.

3D model-based documentation with the Tumor Therapy Manager (TTM) improves TNM staging of head and neck tumor patients.

PubMed

Pankau, Thomas; Wichmann, Gunnar; Neumuth, Thomas; Preim, Bernhard; Dietz, Andreas; Stumpp, Patrick; Boehm, Andreas

2015-10-01

Many treatment approaches are available for head and neck cancer (HNC), leading to challenges for a multidisciplinary medical team in matching each patient with an appropriate regimen. In this effort, primary diagnostics and its reliable documentation are indispensable. A three-dimensional (3D) documentation system was developed and tested to determine its influence on interpretation of these data, especially for TNM classification. A total of 42 HNC patient data sets were available, including primary diagnostics such as panendoscopy, performed and evaluated by an experienced head and neck surgeon. In addition to the conventional panendoscopy form and report, a 3D representation was generated with the "Tumor Therapy Manager" (TTM) software. These cases were randomly re-evaluated by 11 experienced otolaryngologists from five hospitals, half with and half without the TTM data. The accuracy of tumor staging was assessed by pre-post comparison of the TNM classification. TNM staging showed no significant differences in tumor classification (T) with and without 3D from TTM. However, there was a significant decrease in standard deviation from 0.86 to 0.63 via TTM ([Formula: see text]). In nodal staging without TTM, the lymph nodes (N) were significantly underestimated with [Formula: see text] classes compared with [Formula: see text] with TTM ([Formula: see text]). Likewise, the standard deviation was reduced from 0.79 to 0.69 ([Formula: see text]). There was no influence of TTM results on the evaluation of distant metastases (M). TNM staging was more reproducible and nodal staging more accurate when 3D documentation of HNC primary data was available to experienced otolaryngologists. The more precise assessment of the tumor classification with TTM should provide improved decision-making concerning therapy, especially within the interdisciplinary tumor board.

Making psychiatric semiology great again: A semiologic, not nosologic challenge.

PubMed

Micoulaud-Franchi, J-A; Quiles, C; Batail, J-M; Lancon, C; Masson, M; Dumas, G; Cermolacce, M

2018-06-06

This article analyzes whether psychiatric disorders can be considered different from non-psychiatric disorders on a nosologic or semiologic point of view. The supposed difference between psychiatric and non-psychiatric disorders relates to the fact that the individuation of psychiatric disorders seems more complex than for non-psychiatric disorders. This individuation process can be related to nosologic and semiologic considerations. The first part of the article analyzes whether the ways of constructing classifications of psychiatric disorders are different than for non-psychiatric disorders. The ways of establishing the boundaries between the normal and the pathologic, and of classifying the signs and symptoms in different categories of disorder, are analyzed. Rather than highlighting the specificity of psychiatric disorders, nosologic investigation reveals conceptual notions that apply to the entire field of medicine when we seek to establish the boundaries between the normal and the pathologic and between different disorders. Psychiatry is thus very important in medicine because it exemplifies the inherent problem of the construction of cognitive schemes imposed on clinical and scientific medical information to delineate a classification of disorders and increase its comprehensibility and utility. The second part of this article assesses whether the clinical manifestations of psychiatric disorders (semiology) are specific to the point that they are entities that are different from non-psychiatric disorders. The attribution of clinical manifestations in the different classifications (Research Diagnostic Criteria, Diagnostic Statistic Manual, Research Domain Criteria) is analyzed. Then the two principal models on signs and symptoms, i.e. the latent variable model and the causal network model, are assessed. Unlike nosologic investigation, semiologic analysis is able to reveal specific psychiatric features in a patient. The challenge, therefore, is to better define and classify signs and symptoms in psychiatry based on a dual and mutually interactive biological and psychological perspective, and to incorporate semiologic psychiatry into an integrative, multilevel and multisystem brain and cognitive approach. Copyright © 2018 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

[New International Classification of Chronic Pancreatitis (M-ANNHEIM multifactor classification system, 2007): principles, merits, and demerits].

PubMed

Tsimmerman, Ia S

2008-01-01

The new International Classification of Chronic Pancreatitis (designated as M-ANNHEIM) proposed by a group of German specialists in late 2007 is reviewed. All its sections are subjected to analysis (risk group categories, clinical stages and phases, variants of clinical course, diagnostic criteria for "established" and "suspected" pancreatitis, instrumental methods and functional tests used in the diagnosis, evaluation of the severity of the disease using a scoring system, stages of elimination of pain syndrome). The new classification is compared with the earlier classification proposed by the author. Its merits and demerits are discussed.

Diagnostic criteria, severity classification and guidelines of systemic sclerosis.

PubMed

Asano, Yoshihide; Jinnin, Masatoshi; Kawaguchi, Yasushi; Kuwana, Masataka; Goto, Daisuke; Sato, Shinichi; Takehara, Kazuhiko; Hatano, Masaru; Fujimoto, Manabu; Mugii, Naoki; Ihn, Hironobu

2018-06-01

Several effective drugs have been identified for the treatment of systemic sclerosis (SSc). However, in advanced cases, not only their effectiveness is reduced but they may be also harmful due to their side-effects. Therefore, early diagnosis and early treatment is most important for the treatment of SSc. We established diagnostic criteria for SSc in 2003 and early diagnostic criteria for SSc in 2011, for the purpose of developing evaluation of each organ in SSc. Moreover, in November 2013, the American College of Rheumatology and the European Rheumatology Association jointly developed new diagnostic criteria for increasing their sensitivity and specificity, so we revised our diagnostic criteria and severity classification of SSc. Furthermore, we have revised the clinical guideline based on the newest evidence. In particular, the clinical guideline was established by clinical questions based on evidence-based medicine according to the New Minds Clinical Practice Guideline Creation Manual (version 1.0). We aimed to make the guideline easy to use and reliable based on the newest evidence, and to present guidance as specific as possible for various clinical problems in treatment of SSc. © 2018 Japanese Dermatological Association.

Classification of stillbirths is an ongoing dilemma.

PubMed

Nappi, Luigi; Trezza, Federica; Bufo, Pantaleo; Riezzo, Irene; Turillazzi, Emanuela; Borghi, Chiara; Bonaccorsi, Gloria; Scutiero, Gennaro; Fineschi, Vittorio; Greco, Pantaleo

2016-10-01

To compare different classification systems in a cohort of stillbirths undergoing a comprehensive workup; to establish whether a particular classification system is most suitable and useful in determining cause of death, purporting the lowest percentage of unexplained death. Cases of stillbirth at gestational age 22-41 weeks occurring at the Department of Gynecology and Obstetrics of Foggia University during a 4 year period were collected. The World Health Organization (WHO) diagnosis of stillbirth was used. All the data collection was based on the recommendations of an Italian diagnostic workup for stillbirth. Two expert obstetricians reviewed all cases and classified causes according to five classification systems. Relevant Condition at Death (ReCoDe) and Causes Of Death and Associated Conditions (CODAC) classification systems performed best in retaining information. The ReCoDe system provided the lowest rate of unexplained stillbirth (14%) compared to de Galan-Roosen (16%), CODAC (16%), Tulip (18%), Wigglesworth (62%). Classification of stillbirth is influenced by the multiplicity of possible causes and factors related to fetal death. Fetal autopsy, placental histology and cytogenetic analysis are strongly recommended to have a complete diagnostic evaluation. Commonly employed classification systems performed differently in our experience, the most satisfactory being the ReCoDe. Given the rate of "unexplained" cases, none can be considered optimal and further efforts are necessary to work out a clinically useful system.

21 CFR 868.1900 - Diagnostic pulmonary-function interpretation calculator.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Diagnostic pulmonary-function interpretation calculator. 868.1900 Section 868.1900 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND... pulmonary-function values. (b) Classification. Class II (performance standards). ...

21 CFR 868.1900 - Diagnostic pulmonary-function interpretation calculator.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Diagnostic pulmonary-function interpretation calculator. 868.1900 Section 868.1900 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND... pulmonary-function values. (b) Classification. Class II (performance standards). ...

DSM-III as a research tool.

PubMed

Treece, C

1982-05-01

The author describes the use of the DSM-III's diagnostic criteria and classification system as a research instrument and discusses some of the advantages and drawbacks of DMS-III for a specific type of study. A rearrangement of the hierarchical order of the DSM-III diagnostic classes is suggested. This rearrangement provides for levels of certainty in analyzing interrater reliability and offers a simplified framework for summarizing group data. When this approach is combined with a structured interview and response format, it provides a flexible way of managing a large classification system for a smaller study without sacrificing standardization.

A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features

NASA Astrophysics Data System (ADS)

Srinivasan, Yeshwanth; Hernes, Dana; Tulpule, Bhakti; Yang, Shuyu; Guo, Jiangling; Mitra, Sunanda; Yagneswaran, Sriraja; Nutter, Brian; Jeronimo, Jose; Phillips, Benny; Long, Rodney; Ferris, Daron

2005-04-01

Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic markers is extremely image-specific. The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating an archive of 60,000 digitized color images of the uterine cervix. NLM is developing tools for the analysis and dissemination of these images over the Web for the study of visual features correlated with precancerous neoplasia and cancer. To enable indexing of images of the cervix, it is essential to develop algorithms for the segmentation of regions of interest, such as acetowhitened regions, and automatic identification and classification of regions exhibiting mosaicism and punctation. Success of such algorithms depends, primarily, on the selection of relevant features representing the region of interest. We present color and geometric features based statistical classification and segmentation algorithms yielding excellent identification of the regions of interest. The distinct classification of the mosaic regions from the non-mosaic ones has been obtained by clustering multiple geometric and color features of the segmented sections using various morphological and statistical approaches. Such automated classification methodologies will facilitate content-based image retrieval from the digital archive of uterine cervix and have the potential of developing an image based screening tool for cervical cancer.

Exploiting ensemble learning for automatic cataract detection and grading.

PubMed

Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing

2016-02-01

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Exercise-Associated Collapse in Endurance Events: A Classification System.

ERIC Educational Resources Information Center

Roberts, William O.

1989-01-01

Describes a classification system devised for exercise-associated collapse in endurance events based on casualties observed at six Twin Cities Marathons. Major diagnostic criteria are body temperature and mental status. Management protocol includes fluid and fuel replacement, temperature correction, and leg cramp treatment. (Author/SM)

Diagnostic Concordance between DSM-5 and ICD-10 Cannabis Use Disorders.

PubMed

Proctor, Steven L; Williams, Daniel C; Kopak, Albert M; Voluse, Andrew C; Connolly, Kevin M; Hoffmann, Norman G

2016-07-01

With the recent federal mandate that all U.S. health care settings transition to ICD-10 billing codes, empirical evidence is necessary to determine if the DSM-5 designations map to their respective ICD-10 diagnostic categories/billing codes. The present study examined the concordance between DSM-5 and ICD-10 cannabis use disorder diagnoses. Data were derived from routine clinical assessments of 6871 male and 801 female inmates recently admitted to a state prison system from 2000 to 2003. DSM-5 and ICD-10 diagnostic determinations were made from algorithms corresponding to the respective diagnostic formulations. Past 12-month prevalence rates of cannabis use disorders were comparable across classification systems. The vast majority of inmates with no DSM-5 diagnosis continued to have no diagnosis per the ICD-10, and a similar proportion with a DSM-5 severe diagnosis received an ICD-10 dependence diagnosis. Most of the variation in diagnostic classifications was accounted for by those with a DSM-5 moderate diagnosis in that approximately half of these cases received an ICD-10 dependence diagnosis while the remaining cases received a harmful use diagnosis. Although there appears to be a generally high level of agreement between diagnostic classification systems for those with no diagnosis or those evincing symptoms of a more severe condition, concordance between DSM-5 moderate and ICD-10 dependence diagnoses was poor. Additional research is warranted to determine the appropriateness and implications of the current DSM-5 coding guidelines regarding the assignment of an ICD-10 dependence code for those with a DSM-5 moderate diagnosis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reference intervals and diagnostic ranges for serum free κ and free λ immunoglobulin light chains vary by instrument platform: Implications for classification of patient results in a multi-center study.

PubMed

Cotten, Steven W; Shajani-Yi, Zahra; Cervinski, Mark A; Voorhees, Timothy; Tuchman, Sascha A; Korpi-Steiner, Nichole

2018-06-06

Serum free light chain (FLC) immunoglobulins are key biomarkers that aid in the diagnosis, prognosis and assessment of treatment response in patients with plasma cell disorders (PCD). Here we investigated the transference of manufacturer's reported κFLC, λFLC and κ to λ FLC reference intervals (RI) and established de novo FLC RI and diagnostic ranges on four instruments at three academic medical centers. In addition, we also compared the classification of patient FLC results using manufacturer's versus established RIs and diagnostic ranges. CLSI EP28-A3C protocol was applied to investigate transference and establishment of FLC reference intervals on the cobas (Roche), Immage (Beckman), Optilite and SPA Plus (Binding Site). Serum κ FLC and λ FLC were measured in reference sera (N = 126) with estimation of central 95% RIs and FLC ratio diagnostic range (total range). Frequencies (%) in patient FLC results (N > 380 per institution) classified above, below or within manufacturer's versus established FLC RI were compared. Three of four instrument platforms did not exhibit acceptable transference of manufacturer's reported κFLC RI. The manufacturer's reported FLC total diagnostic range did not encompass all values observed in reference sera for any of the four platforms evaluated. Established FLC ratio diagnostic ranges reduced the frequency of patient results classified above range for three of four platforms evaluated. Transference of manufacturer's reported FLC RIs may be inappropriate for select instrument platforms. De novo establishment of FLC RIs specific to instrument platform is highly recommended in order to assure correct patient result classification. Copyright © 2017. Published by Elsevier Inc.

The Screening Accuracy of the Parent and Teacher-Reported Social Responsiveness Scale (SRS): Comparison with the 3Di and ADOS.

PubMed

Duvekot, Jorieke; van der Ende, Jan; Verhulst, Frank C; Greaves-Lord, Kirstin

2015-06-01

The screening accuracy of the parent and teacher-reported Social Responsiveness Scale (SRS) was compared with an autism spectrum disorder (ASD) classification according to (1) the Developmental, Dimensional, and Diagnostic Interview (3 Di), (2) the Autism Diagnostic Observation Schedule (ADOS), (3) both the 3 Di and ADOS, in 186 children referred to six mental health centers. The parent report showed excellent correspondence to an ASD classification according to the 3 Di and both the 3 Di and ADOS. The teacher report added significantly to the screening accuracy over and above the parent report when compared with the ADOS classification. Findings support the screening utility of the parent-reported SRS among clinically referred children and indicate that different informants may provide unique information relevant for ASD assessment.

Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

PubMed

Khanmohammadi, Mohammadreza; Bagheri Garmarudi, Amir; Samani, Simin; Ghasemi, Keyvan; Ashuri, Ahmad

2011-06-01

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

PubMed

Zhou, Yongxia; Yu, Fang; Duong, Timothy

2014-01-01

This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.

An approach to the diagnosis of flat intraepithelial lesions of the urinary bladder using the World Health Organization/ International Society of Urological Pathology consensus classification system.

PubMed

Amin, Mahul B; McKenney, Jesse K

2002-07-01

The classification of flat urothelial (transitional cell) lesions with atypia has historically varied in its application from institution to institution with no fewer than six major nomenclature systems proposed in the past 25 years. In 1998, the World Health Organization/ International Society of Urological Pathology (WHO/ISUP) published a consensus classification that included the following categories for flat urinary bladder lesions: reactive atypia, atypia of unknown significance, dysplasia (low-grade intraepithelial neoplasia), and carcinoma in situ (high-grade intraepithelial neoplasia). This classification expands the definition traditionally used for urothelial carcinoma in situ, basing its diagnosis primarily on the severity of cytologic changes. In proposing the classification system for flat lesions of the bladder with atypia, it was hoped that consistent use of uniform diagnostic terminology would ultimately aid in a better understanding of the biology of these lesions. In this review, the authors discuss the history of the concept of flat urothelial neoplasia, the rationale and histologic criteria for the WHO/ISUP diagnostic categories, an approach to the diagnosis of flat lesions, and problems and pitfalls associated with their recognition in routine surgical pathology specimens.

Change in Autism Classification with Early Intervention: Predictors and Outcomes

ERIC Educational Resources Information Center

Ben Itzchak, Esther; Zachor, Ditza A.

2009-01-01

The current study characterized stability and changes of autism diagnostic classification with intervention in very young children and examined pre-treatment predictors and post-intervention outcome. Sixty-eight children diagnosed with autism, aged 18-35 months (M = 25.4, SD = 4.0) participated in the study. Children underwent comprehensive…

Use of Classification Agreement Analyses to Evaluate RTI Implementation

ERIC Educational Resources Information Center

VanDerHeyden, Amanda

2010-01-01

RTI as a framework for decision making has implications for the diagnosis of specific learning disabilities. Any diagnostic tool must meet certain standards to demonstrate that its use leads to predictable decisions with minimal risk. Classification agreement analyses are described as optimal for demonstrating the technical adequacy of RTI…

Diagnosis and Classification in Autism.

ERIC Educational Resources Information Center

Waterhouse, Lynn; And Others

1996-01-01

This study compared four systems for diagnosis of autism (Diagnostic and Statistical Manuals of Mental Disorders III, III-R, and IV, and the International Classification of Disabilities-10) with 2 empirically derived taxa and 3 social subgroups (aloof, passive, and active but odd) in 194 preschool children with social impairment. Findings support…

Eating Disorder Diagnoses: Empirical Approaches to Classification

ERIC Educational Resources Information Center

Wonderlich, Stephen A.; Joiner, Thomas E., Jr.; Keel, Pamela K.; Williamson, Donald A.; Crosby, Ross D.

2007-01-01

Decisions about the classification of eating disorders have significant scientific and clinical implications. The eating disorder diagnoses in the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) reflect the collective wisdom of experts in the field but are frequently not supported in…

Asymmetry of Peak Thicknesses between the Superior and Inferior Retinal Nerve Fiber Layers for Early Glaucoma Detection: A Simple Screening Method.

PubMed

Bae, Hyoung Won; Lee, Sang Yeop; Kim, Sangah; Park, Chan Keum; Lee, Kwanghyun; Kim, Chan Yun; Seong, Gong Je

2018-01-01

To assess whether the asymmetry in the peripapillary retinal nerve fiber layer (pRNFL) thickness between superior and inferior hemispheres on optical coherence tomography (OCT) is useful for early detection of glaucoma. The patient population consisted of Training set (a total of 60 subjects with early glaucoma and 59 normal subjects) and Validation set (30 subjects with early glaucoma and 30 normal subjects). Two kinds of ratios were employed to measure the asymmetry between the superior and inferior pRNFL thickness using OCT. One was the ratio of the superior to inferior peak thicknesses (peak pRNFL thickness ratio; PTR), and the other was the ratio of the superior to inferior average thickness (average pRNFL thickness ratio; ATR). The diagnostic abilities of the PTR and ATR were compared to the color code classification in OCT. Using the optimal cut-off values of the PTR and ATR obtained from the Training set, the two ratios were independently validated for diagnostic capability. For the Training set, the sensitivities/specificities of the PTR, ATR, quadrants color code classification, and clock-hour color code classification were 81.7%/93.2%, 71.7%/74.6%, 75.0%/93.2%, and 75.0%/79.7%, respectively. The PTR showed a better diagnostic performance for early glaucoma detection than the ATR and the clock-hour color code classification in terms of areas under the receiver operating characteristic curves (AUCs) (0.898, 0.765, and 0.773, respectively). For the Validation set, the PTR also showed the best sensitivity and AUC. The PTR is a simple method with considerable diagnostic ability for early glaucoma detection. It can, therefore, be widely used as a new screening method for early glaucoma. © Copyright: Yonsei University College of Medicine 2018

Molecular Diagnostics of Gliomas Using Next Generation Sequencing of a Glioma-Tailored Gene Panel.

PubMed

Zacher, Angela; Kaulich, Kerstin; Stepanow, Stefanie; Wolter, Marietta; Köhrer, Karl; Felsberg, Jörg; Malzkorn, Bastian; Reifenberger, Guido

2017-03-01

Current classification of gliomas is based on histological criteria according to the World Health Organization (WHO) classification of tumors of the central nervous system. Over the past years, characteristic genetic profiles have been identified in various glioma types. These can refine tumor diagnostics and provide important prognostic and predictive information. We report on the establishment and validation of gene panel next generation sequencing (NGS) for the molecular diagnostics of gliomas. We designed a glioma-tailored gene panel covering 660 amplicons derived from 20 genes frequently aberrant in different glioma types. Sensitivity and specificity of glioma gene panel NGS for detection of DNA sequence variants and copy number changes were validated by single gene analyses. NGS-based mutation detection was optimized for application on formalin-fixed paraffin-embedded tissue specimens including small stereotactic biopsy samples. NGS data obtained in a retrospective analysis of 121 gliomas allowed for their molecular classification into distinct biological groups, including (i) isocitrate dehydrogenase gene (IDH) 1 or 2 mutant astrocytic gliomas with frequent α-thalassemia/mental retardation syndrome X-linked (ATRX) and tumor protein p53 (TP53) gene mutations, (ii) IDH mutant oligodendroglial tumors with 1p/19q codeletion, telomerase reverse transcriptase (TERT) promoter mutation and frequent Drosophila homolog of capicua (CIC) gene mutation, as well as (iii) IDH wildtype glioblastomas with frequent TERT promoter mutation, phosphatase and tensin homolog (PTEN) mutation and/or epidermal growth factor receptor (EGFR) amplification. Oligoastrocytic gliomas were genetically assigned to either of these groups. Our findings implicate gene panel NGS as a promising diagnostic technique that may facilitate integrated histological and molecular glioma classification. © 2016 International Society of Neuropathology.

Assessment of the diagnostic performance and interobserver variability of endocytoscopy in Barrett’s esophagus: A pilot ex-vivo study

PubMed Central

Tomizawa, Yutaka; Iyer, Prasad G; Wongkeesong, Louis M; Buttar, Navtej S; Lutzke, Lori S; Wu, Tsung-Teh; Wang, Kenneth K

2013-01-01

AIM: To investigate a classification of endocytoscopy (ECS) images in Barrett’s esophagus (BE) and evaluate its diagnostic performance and interobserver variability. METHODS: ECS was applied to surveillance endoscopic mucosal resection (EMR) specimens of BE ex-vivo. The mucosal surface of specimen was stained with 1% methylene blue and surveyed with a catheter-type endocytoscope. We selected still images that were most representative of the endoscopically suspect lesion and matched with the final histopathological diagnosis to accomplish accurate correlation. The diagnostic performance and inter-observer variability of the new classification scheme were assessed in a blinded fashion by physicians with expertise in both BE and ECS and inexperienced physicians with no prior exposure to ECS. RESULTS: Three staff physicians and 22 gastroenterology fellows classified eight randomly assigned unknown still ECS pictures (two images per each classification) into one of four histopathologic categories as follows: (1) BEC1-squamous epithelium; (2) BEC2-BE without dysplasia; (3) BEC3-BE with dysplasia; and (4) BEC4-esophageal adenocarcinoma (EAC) in BE. Accuracy of diagnosis in staff physicians and clinical fellows were, respectively, 100% and 99.4% for BEC1, 95.8% and 83.0% for BEC2, 91.7% and 83.0% for BEC3, and 95.8% and 98.3% for BEC4. Interobserver agreement of the faculty physicians and fellows in classifying each category were 0.932 and 0.897, respectively. CONCLUSION: This is the first study to investigate classification system of ECS in BE. This ex-vivo pilot study demonstrated acceptable diagnostic accuracy and excellent interobserver agreement. PMID:24379583

A theory of fine structure image models with an application to detection and classification of dementia.

PubMed

O'Neill, William; Penn, Richard; Werner, Michael; Thomas, Justin

2015-06-01

Estimation of stochastic process models from data is a common application of time series analysis methods. Such system identification processes are often cast as hypothesis testing exercises whose intent is to estimate model parameters and test them for statistical significance. Ordinary least squares (OLS) regression and the Levenberg-Marquardt algorithm (LMA) have proven invaluable computational tools for models being described by non-homogeneous, linear, stationary, ordinary differential equations. In this paper we extend stochastic model identification to linear, stationary, partial differential equations in two independent variables (2D) and show that OLS and LMA apply equally well to these systems. The method employs an original nonparametric statistic as a test for the significance of estimated parameters. We show gray scale and color images are special cases of 2D systems satisfying a particular autoregressive partial difference equation which estimates an analogous partial differential equation. Several applications to medical image modeling and classification illustrate the method by correctly classifying demented and normal OLS models of axial magnetic resonance brain scans according to subject Mini Mental State Exam (MMSE) scores. Comparison with 13 image classifiers from the literature indicates our classifier is at least 14 times faster than any of them and has a classification accuracy better than all but one. Our modeling method applies to any linear, stationary, partial differential equation and the method is readily extended to 3D whole-organ systems. Further, in addition to being a robust image classifier, estimated image models offer insights into which parameters carry the most diagnostic image information and thereby suggest finer divisions could be made within a class. Image models can be estimated in milliseconds which translate to whole-organ models in seconds; such runtimes could make real-time medicine and surgery modeling possible.

Classification of mood disorders in DSM-V and DSM-VI.

PubMed

Joyce, Peter R

2008-10-01

For any diagnostic system to be clinically useful, and go beyond description, it must provide an understanding that informs about aetiology and/or outcome. DSM-III and DSM-IV have provided reliability; the challenge for DSM-V and DSM-VI will be to provide validity. For DSM-V this will not be achieved. Believers in DSM-III and DSM-IV have impeded progress towards a valid classification system, so DSM-V needs to retain continuity with its predecessors to retain reliability and enhance research, but position itself to inform a valid diagnostic system by DSM-VI. This review examines the features of a diagnostic system and summarizes what is really known about mood disorders. The review also questions whether what are called mood disorders are primarily disorders of mood. Finally, it provides suggestions for DSM-VI.

Diagnostic Performance of SRU and ATA Thyroid Nodule Classification Algorithms as Tested With a 1 Million Virtual Thyroid Nodule Model.

PubMed

Boehnke, Mitchell; Patel, Nayana; McKinney, Kristin; Clark, Toshimasa

The Society of Radiologists in Ultrasound (SRU 2005) and American Thyroid Association (ATA 2009 and ATA 2015) have published algorithms regarding thyroid nodule management. Kwak et al. and other groups have described models that estimate thyroid nodules' malignancy risk. The aim of our study is to use Kwak's model to evaluate the tradeoffs of both sensitivity and specificity of SRU 2005, ATA 2009 and ATA 2015 management algorithms. 1,000,000 thyroid nodules were modeled in MATLAB. Ultrasound characteristics were modeled after published data. Malignancy risk was estimated per Kwak's model and assigned as a binary variable. All nodules were then assessed using the published management algorithms. With the malignancy variable as condition positivity and algorithms' recommendation for FNA as test positivity, diagnostic performance was calculated. Modeled nodule characteristics mimic those of Kwak et al. 12.8% nodules were assigned as malignant (malignancy risk range of 2.0-98%). FNA was recommended for 41% of nodules by SRU 2005, 66% by ATA 2009, and 82% by ATA 2015. Sensitivity and specificity is significantly different (< 0.0001): 49% and 60% for SRU; 81% and 36% for ATA 2009; and 95% and 20% for ATA 2015. SRU 2005, ATA 2009 and ATA 2015 algorithms are used routinely in clinical practice to determine whether thyroid nodule biopsy is indicated. We demonstrate significant differences in these algorithms' diagnostic performance, which result in a compromise between sensitivity and specificity. Copyright © 2017 Elsevier Inc. All rights reserved.

The clinical application of fMRI data in a single-patient diagnostic conundrum: Classifying brain response to experimental pain to distinguish between gastrointestinal, depressive and eating disorder symptoms.

PubMed

Strigo, Irina A; Murray, Stuart B; Simmons, Alan N; Bernard, Rebecca S; Huang, Jeannie S; Kaye, Walter H

2017-11-01

Patients with eating disorders (EDs) often present with psychiatric comorbidity, and functional and/or organic gastrointestinal (GI) symptomatology. Such multidiagnostic presentations can complicate diagnostic practice and treatment delivery. Here we describe an adolescent patient who presented with mixed ED, depressive, and GI symptomatology, who had received multiple contrasting diagnoses throughout treatment. We used a novel machine learning approach to classify (i) the patient's functional brain imaging during an experimental pain paradigm, and (ii) patient self-report psychological measures, to categorize the diagnostic phenotype most closely approximated by the patient. Specifically, we found that the patient's response to pain anticipation and experience within the insula and anterior cingulate cortices, and patient self-report data, were most consistent with patients with GI pain. This work is the first to demonstrate the possibility of using imaging data, alongside supervised learning models, for purposes of single patient classification in those with ED symptomatology, where diagnostic comorbidity is common. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic

PubMed Central

de Groot, Maartje H.; van Campen, Jos P.; Beijnen, Jos H.; Hortobágyi, Tibor; Vuillerme, Nicolas; Lamoth, Claudine C. J.

2017-01-01

Fall prediction in geriatric patients remains challenging because the increased fall risk involves multiple, interrelated factors caused by natural aging and/or pathology. Therefore, we used a multi-factorial statistical approach to model categories of modifiable fall risk factors among geriatric patients to identify fallers with highest sensitivity and specificity with a focus on gait performance. Patients (n = 61, age = 79; 41% fallers) underwent extensive screening in three categories: (1) patient characteristics (e.g., handgrip strength, medication use, osteoporosis-related factors) (2) cognitive function (global cognition, memory, executive function), and (3) gait performance (speed-related and dynamic outcomes assessed by tri-axial trunk accelerometry). Falls were registered prospectively (mean follow-up 8.6 months) and one year retrospectively. Principal Component Analysis (PCA) on 11 gait variables was performed to determine underlying gait properties. Three fall-classification models were then built using Partial Least Squares–Discriminant Analysis (PLS-DA), with separate and combined analyses of the fall risk factors. PCA identified ‘pace’, ‘variability’, and ‘coordination’ as key properties of gait. The best PLS-DA model produced a fall classification accuracy of AUC = 0.93. The specificity of the model using patient characteristics was 60% but reached 80% when cognitive and gait outcomes were added. The inclusion of cognition and gait dynamics in fall classification models reduced misclassification. We therefore recommend assessing geriatric patients’ fall risk using a multi-factorial approach that incorporates patient characteristics, cognition, and gait dynamics. PMID:28575126

Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic.

PubMed

Kikkert, Lisette H J; de Groot, Maartje H; van Campen, Jos P; Beijnen, Jos H; Hortobágyi, Tibor; Vuillerme, Nicolas; Lamoth, Claudine C J

2017-01-01

Fall prediction in geriatric patients remains challenging because the increased fall risk involves multiple, interrelated factors caused by natural aging and/or pathology. Therefore, we used a multi-factorial statistical approach to model categories of modifiable fall risk factors among geriatric patients to identify fallers with highest sensitivity and specificity with a focus on gait performance. Patients (n = 61, age = 79; 41% fallers) underwent extensive screening in three categories: (1) patient characteristics (e.g., handgrip strength, medication use, osteoporosis-related factors) (2) cognitive function (global cognition, memory, executive function), and (3) gait performance (speed-related and dynamic outcomes assessed by tri-axial trunk accelerometry). Falls were registered prospectively (mean follow-up 8.6 months) and one year retrospectively. Principal Component Analysis (PCA) on 11 gait variables was performed to determine underlying gait properties. Three fall-classification models were then built using Partial Least Squares-Discriminant Analysis (PLS-DA), with separate and combined analyses of the fall risk factors. PCA identified 'pace', 'variability', and 'coordination' as key properties of gait. The best PLS-DA model produced a fall classification accuracy of AUC = 0.93. The specificity of the model using patient characteristics was 60% but reached 80% when cognitive and gait outcomes were added. The inclusion of cognition and gait dynamics in fall classification models reduced misclassification. We therefore recommend assessing geriatric patients' fall risk using a multi-factorial approach that incorporates patient characteristics, cognition, and gait dynamics.

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.

Developmentally Sensitive Diagnostic Criteria for Mental Health Disorders in Early Childhood: The Diagnostic and Statistical Manual of Mental Disorders-IV, the Research Diagnostic Criteria-Preschool Age, and the Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood-Revised

ERIC Educational Resources Information Center

Egger, Helen L.; Emde, Robert N.

2011-01-01

As the infant mental health field has turned its focus to the presentation, course, and treatment of clinically significant mental health disorders, the need for reliable and valid criteria for identifying and assessing mental health symptoms and disorders in early childhood has become urgent. In this article we offer a critical perspective on…

Updating Allergy and/or Hypersensitivity Diagnostic Procedures in the WHO ICD-11 Revision.

PubMed

Tanno, Luciana Kase; Calderon, Moises A; Li, James; Casale, Thomas; Demoly, Pascal

2016-01-01

The classification of allergy and/or hypersensitivity conditions for the World Health Organization (WHO) International Classification of Diseases (ICD)-11 provides the appropriate corresponding codes for allergic diseases, assuming that the final diagnosis is correct. This classification should be linked to in vitro and in vivo diagnostic procedures. Considering the impact for our specialty, we decided to review the codification of these procedures into the ICD aiming to have a baseline and to suggest changes and/or submit new proposals. For that, we prepared a list of the relevant allergy and/or hypersensitivity diagnostic procedures that health care professionals are dealing with on a daily basis. This was based on the main current guidelines and selected all possible and relevant corresponding terms from the ICD-10 (2015 version) and the ICD-11 β phase foundation (June 2015 version). More than 90% of very specific and important diagnostic procedures currently used by the allergists' community on a daily basis are missing. We observed that some concepts usually used by the allergist community on a daily basis are not fully recognized by other specialties. The whole scheme and the correspondence in the ICD-10 (2015 version) and ICD-11 foundation (June 2015 version) provided us a big picture of the missing or imprecise terms and how they are scattered in the current ICD-11 framework, allowing us to submit new proposals to increase the visibility of the allergy and/or hypersensitivity conditions and diagnostic procedures. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. All rights reserved.

Expanding the Taxonomy of the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)

PubMed Central

Peck, Christopher C.; Goulet, Jean-Paul; Lobbezoo, Frank; Schiffman, Eric L.; Alstergren, Per; Anderson, Gary C.; de Leeuw, Reny; Jensen, Rigmor; Michelotti, Ambra; Ohrbach, Richard; Petersson, Arne; List, Thomas

2014-01-01

Background There is a need to expand the current temporomandibular disorder (TMD) classification to include less common, but clinically important disorders. The immediate aim was to develop a consensus-based classification system and associated diagnostic criteria that have clinical and research utility for less common TMDs. The long-term aim was to establish a foundation, vis-à-vis this classification system, that will stimulate data collection, validity testing, and further criteria refinement. Methods A working group [members of the International RDC/TMD Consortium Network of the International Association for Dental Research (IADR), members of the Orofacial Pain Special Interest Group (SIG) of the International Association for the Study of Pain (IASP), and members from other professional societies] reviewed disorders for inclusion based on clinical significance, the availability of plausible diagnostic criteria, and the ability to operationalize and study the criteria. The disorders were derived from the literature when possible and based on expert opinion as necessary. The expanded TMD taxonomy was presented for feedback at international meetings. Results Of 56 disorders considered, 37 were included in the expanded taxonomy and were placed into the following four categories: temporomandibular joint disorders, masticatory muscle disorders, headache disorders, and disorders affecting associated structures. Those excluded were extremely uncommon, lacking operationalized diagnostic criteria, not clearly related to TMDs, or not sufficiently distinct from disorders already included within the taxonomy. Conclusions The expanded TMD taxonomy offers an integrated approach to clinical diagnosis and provides a framework for further research to operationalize and test the proposed taxonomy and diagnostic criteria. PMID:24443898

Expanding the taxonomy of the diagnostic criteria for temporomandibular disorders.

PubMed

Peck, C C; Goulet, J-P; Lobbezoo, F; Schiffman, E L; Alstergren, P; Anderson, G C; de Leeuw, R; Jensen, R; Michelotti, A; Ohrbach, R; Petersson, A; List, T

2014-01-01

There is a need to expand the current temporomandibular disorders' (TMDs) classification to include less common but clinically important disorders. The immediate aim was to develop a consensus-based classification system and associated diagnostic criteria that have clinical and research utility for less common TMDs. The long-term aim was to establish a foundation, vis-à-vis this classification system, that will stimulate data collection, validity testing and further criteria refinement. A working group [members of the International RDC/TMD Consortium Network of the International Association for Dental Research (IADR), members of the Orofacial Pain Special Interest Group (SIG) of the International Association for the Study of Pain (IASP), and members from other professional societies] reviewed disorders for inclusion based on clinical significance, the availability of plausible diagnostic criteria and the ability to operationalise and study the criteria. The disorders were derived from the literature when possible and based on expert opinion as necessary. The expanded TMDs taxonomy was presented for feedback at international meetings. Of 56 disorders considered, 37 were included in the expanded taxonomy and were placed into the following four categories: temporomandibular joint disorders, masticatory muscle disorders, headache disorders and disorders affecting associated structures. Those excluded were extremely uncommon, lacking operationalised diagnostic criteria, not clearly related to TMDs, or not sufficiently distinct from disorders already included within the taxonomy. The expanded TMDs taxonomy offers an integrated approach to clinical diagnosis and provides a framework for further research to operationalise and test the proposed taxonomy and diagnostic criteria. © 2014 John Wiley & Sons Ltd.

Eating Disorders in the Adolescent Population: An Overview.

ERIC Educational Resources Information Center

Reijonen, Jori H.; Pratt, Helen D.; Patel, Dilip R.; Greydanus, Donald E.

2003-01-01

Selectively reviews the literature on the diagnostic criteria for eating disorders (anorexia nervosa, bulimia nervosa, and binge-eating disorder) as described in "Diagnostic and Statistical Manual of Mental Disorders" (4th ed.) and "International Classification of Diseases" (10th ed.). Discusses the prevalence and course of…

Toward a Model-Based Approach to the Clinical Assessment of Personality Psychopathology

PubMed Central

Eaton, Nicholas R.; Krueger, Robert F.; Docherty, Anna R.; Sponheim, Scott R.

2015-01-01

Recent years have witnessed tremendous growth in the scope and sophistication of statistical methods available to explore the latent structure of psychopathology, involving continuous, discrete, and hybrid latent variables. The availability of such methods has fostered optimism that they can facilitate movement from classification primarily crafted through expert consensus to classification derived from empirically-based models of psychopathological variation. The explication of diagnostic constructs with empirically supported structures can then facilitate the development of assessment tools that appropriately characterize these constructs. Our goal in this paper is to illustrate how new statistical methods can inform conceptualization of personality psychopathology and therefore its assessment. We use magical thinking as example, because both theory and earlier empirical work suggested the possibility of discrete aspects to the latent structure of personality psychopathology, particularly forms of psychopathology involving distortions of reality testing, yet other data suggest that personality psychopathology is generally continuous in nature. We directly compared the fit of a variety of latent variable models to magical thinking data from a sample enriched with clinically significant variation in psychotic symptomatology for explanatory purposes. Findings generally suggested a continuous latent variable model best represented magical thinking, but results varied somewhat depending on different indices of model fit. We discuss the implications of the findings for classification and applied personality assessment. We also highlight some limitations of this type of approach that are illustrated by these data, including the importance of substantive interpretation, in addition to use of model fit indices, when evaluating competing structural models. PMID:24007309

[Aggressive B‑cell lymphomas : Recommendations from the German Panel of Reference Pathologists in the Competence Network on Malignant Lymphomas on diagnostic procedures according to the current WHO classification, update 2017].

PubMed

Klapper, W; Fend, F; Feller, A; Hansmann, M L; Möller, P; Stein, H; Rosenwald, A; Ott, G

2018-04-17

The update of the 4th edition of the WHO classification for hematopoietic neoplasms introduces changes in the field of mature aggressive B‑cell lymphomas that are relevant to diagnostic pathologists. In daily practice, the question arises of which analysis should be performed when diagnosing the most common lymphoma entity, diffuse large B‑cell lymphoma. We discuss the importance of the cell of origin, the analysis of MYC translocations, and the delineation of the new WHO entities of high-grade B‑cell lymphomas.

Parametric estimates for the receiver operating characteristic curve generalization for non-monotone relationships.

PubMed

Martínez-Camblor, Pablo; Pardo-Fernández, Juan C

2017-01-01

Diagnostic procedures are based on establishing certain conditions and then checking if those conditions are satisfied by a given individual. When the diagnostic procedure is based on a continuous marker, this is equivalent to fix a region or classification subset and then check if the observed value of the marker belongs to that region. Receiver operating characteristic curve is a valuable and popular tool to study and compare the diagnostic ability of a given marker. Besides, the area under the receiver operating characteristic curve is frequently used as an index of the global discrimination ability. This paper revises and widens the scope of the receiver operating characteristic curve definition by setting the classification subsets in which the final decision is based in the spotlight of the analysis. We revise the definition of the receiver operating characteristic curve in terms of particular classes of classification subsets and then focus on a receiver operating characteristic curve generalization for situations in which both low and high values of the marker are associated with more probability of having the studied characteristic. Parametric and non-parametric estimators of the receiver operating characteristic curve generalization are investigated. Monte Carlo studies and real data examples illustrate their practical performance.

Acute kidney injury—an overview of diagnostic methods and clinical management

PubMed Central

Hertzberg, Daniel; Rydén, Linda; Pickering, John W.; Sartipy, Ulrik

2017-01-01

Abstract Acute kidney injury (AKI) is a common condition in multiple clinical settings. Patients with AKI are at an increased risk of death, over both the short and long term, and of accelerated renal impairment. As the condition has become more recognized and definitions more unified, there has been a rapid increase in studies examining AKI across many different clinical settings. This review focuses on the classification, diagnostic methods and clinical management that are available, or promising, for patients with AKI. Furthermore, preventive measures with fluids, acetylcysteine, statins and remote ischemic preconditioning, as well as when dialysis should be initiated in AKI patients are discussed. The classification of AKI includes both changes in serum creatinine concentrations and urine output. Currently, no kidney injury biomarkers are included in the classification of AKI, but proposals have been made to include them as independent diagnostic markers. Treatment of AKI is aimed at addressing the underlying causes of AKI, and at limiting damage and preventing progression. The key principles are: to treat the underlying disease, to optimize fluid balance and optimize hemodynamics, to treat electrolyte disturbances, to discontinue or dose-adjust nephrotoxic drugs and to dose-adjust drugs with renal elimination. PMID:28616210

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

PubMed

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

RBANS memory percentage retention: No evidence of incremental validity beyond RBANS scores for diagnostic classification of mild cognitive impairment and dementia and for prediction of daily function.

PubMed

Jodouin, Kara A; O'Connell, Megan E; Morgan, Debra G

2017-01-01

RBANS percentage retention scores may be useful for diagnosis, but their incremental validity is unclear. Percentage retention versus RBANS immediate and delayed memory subtests and delayed index scores were compared for diagnostic classification and for prediction of function. Data from 173 memory clinic patients with an interdisciplinary diagnosis (no cognitive impairment, amnestic mild cognitive impairment [aMCI], and dementia due to Alzheimer's disease [AD]) and complete RBANS data were analyzed. Across diagnostic contrasts, list percentage retention classification accuracy was similar to List Learning delayed recall, but below the Delayed Memory Index (DMI). Similarly, for classifying no cognitive impairment versus aMCI or dementia due to AD, story percentage retention was similar to Story Memory subtests and below the DMI. For classifying aMCI versus AD; however, Story Memory exceeded the DMI, but was similar to Story Memory subtest scores. Similarly, for prediction of function percentage retention measures did not predict variance beyond that predicted by the RBANS subtest or index scores. In sum, there is no evidence that calculation of percentage retention for RBANS adds clinical utility beyond those provided by the standard RBANS scores.

[Acquired brain injury: a proposal for its definition, diagnostic criteria and classification].

PubMed

Castellanos-Pinedo, Fernando; Cid-Gala, Manuel; Duque, Pablo; Ramirez-Moreno, José M; Zurdo-Hernández, José M

2012-03-16

Acquired brain injury is a heterogeneous clinical concept that goes beyond the limits of the classical medical view, which tends to define processes and diseases on the grounds of a single causation. Although in the medical literature it appears fundamentally associated to traumatic brain injury, there are many other causes and management is similar in all of them, during the post-acute and chronic phases, as regards the measures to be taken concerning rehabilitation and attention to dependence. Yet, despite being an important health issue, today we do not have a set of diagnostic criteria or a classification for this condition. This is a serious handicap when it comes to carrying out epidemiological studies, designing specific care programmes and comparing results among different programmes and centres. Accordingly, the Extremadura Acquired Brain Injury Health Care Plan working group has drawn up these proposed diagnostic criteria, definition and classification. The proposal is intended to be essentially practical, its main purpose being to allow correct identification of the cases that must be attended to and to optimise the use of neurorehabilitation and attention to dependence resources, thereby ensuring attention is provided on a fair basis.

Diagnostic index of 3D osteoarthritic changes in TMJ condylar morphology

NASA Astrophysics Data System (ADS)

Gomes, Liliane R.; Gomes, Marcelo; Jung, Bryan; Paniagua, Beatriz; Ruellas, Antonio C.; Gonçalves, João. Roberto; Styner, Martin A.; Wolford, Larry; Cevidanes, Lucia

2015-03-01

The aim of this study was to investigate imaging statistical approaches for classifying 3D osteoarthritic morphological variations among 169 Temporomandibular Joint (TMJ) condyles. Cone beam Computed Tomography (CBCT) scans were acquired from 69 patients with long-term TMJ Osteoarthritis (OA) (39.1 ± 15.7 years), 15 patients at initial diagnosis of OA (44.9 ± 14.8 years) and 7 healthy controls (43 ± 12.4 years). 3D surface models of the condyles were constructed and Shape Correspondence was used to establish correspondent points on each model. The statistical framework included a multivariate analysis of covariance (MANCOVA) and Direction-Projection- Permutation (DiProPerm) for testing statistical significance of the differences between healthy control and the OA group determined by clinical and radiographic diagnoses. Unsupervised classification using hierarchical agglomerative clustering (HAC) was then conducted. Condylar morphology in OA and healthy subjects varied widely. Compared with healthy controls, OA average condyle was statistically significantly smaller in all dimensions except its anterior surface. Significant flattening of the lateral pole was noticed at initial diagnosis (p < 0.05). It was observed areas of 3.88 mm bone resorption at the superior surface and 3.10 mm bone apposition at the anterior aspect of the long-term OA average model. 1000 permutation statistics of DiProPerm supported a significant difference between the healthy control group and OA group (t = 6.7, empirical p-value = 0.001). Clinically meaningful unsupervised classification of TMJ condylar morphology determined a preliminary diagnostic index of 3D osteoarthritic changes, which may be the first step towards a more targeted diagnosis of this condition.

Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

NASA Astrophysics Data System (ADS)

Liu, J.; Lan, T.; Qin, H.

2017-10-01

Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class-imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class-imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class-balanced effect of Time-Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry-INTerferometry (POINT) system.

Evaluation of a Diagnostic Encyclopedia Workstation for ovarian pathology.

PubMed

van Ginneken, A M; Baak, J P; Jansen, W; Smeulders, A W

1990-10-01

The Diagnostic Encyclopedia Workstation (DEW) is a computer system that provides completely integrated pictorial and textual information as reference knowledge in the field of ovarian pathology. The textual component comprises information per diagnosis such as descriptions of macroscopic and microscopic images, clinical signs, and prognosis. In addition, the system offers lists of differential diagnoses and criteria to differentiate among lists of differential diagnoses and criteria to differentiate among them. The present study evaluates to what extent the system influences the diagnostic process in efficiency and outcome. Therefore, two groups of six pathologists each, covering a wide spectrum of experience in ovarian pathology, participated in the evaluation of the DEW. The quality of the resulting diagnoses was statistically analyzed with the Wilcoxon rank sum test with respect to five different viewpoints: classification, morphology, clinical consequences, duration of diagnostic process, and consensus among the participants. The results are discussed and it is concluded that classification and morphology showed better results when books were used. The evaluation experiment was, however, very rigid and negatively biased with respect to the DEW system. Positive aspects of the encyclopedia are the easy access to diagnostic and differential diagnostic information and the large set of illustrations. Insight is acquired with respect to existing bottlenecks and how they may be overcome.

Tissue classification and diagnostics using a fiber probe for combined Raman and fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Cicchi, Riccardo; Anand, Suresh; Rossari, Susanna; Sturiale, Alessandro; Giordano, Flavio; De Giorgi, Vincenzo; Maio, Vincenza; Massi, Daniela; Nesi, Gabriella; Buccoliero, Anna Maria; Tonelli, Francesco; Guerrini, Renzo; Pimpinelli, Nicola; Pavone, Francesco S.

2015-03-01

Two different optical fiber probes for combined Raman and fluorescence spectroscopic measurements were designed, developed and used for tissue diagnostics. Two visible laser diodes were used for fluorescence spectroscopy, whereas a laser diode emitting in the NIR was used for Raman spectroscopy. The two probes were based on fiber bundles with a central multimode optical fiber, used for delivering light to the tissue, and 24 surrounding optical fibers for signal collection. Both fluorescence and Raman spectra were acquired using the same detection unit, based on a cooled CCD camera, connected to a spectrograph. The two probes were successfully employed for diagnostic purposes on various tissues in a good agreement with common routine histology. This study included skin, brain and bladder tissues and in particular the classification of: malignant melanoma against melanocytic lesions and healthy skin; urothelial carcinoma against healthy bladder mucosa; brain tumor against dysplastic brain tissue. The diagnostic capabilities were determined using a cross-validation method with a leave-one-out approach, finding very high sensitivity and specificity for all the examined tissues. The obtained results demonstrated that the multimodal approach is crucial for improving diagnostic capabilities. The system presented here can improve diagnostic capabilities on a broad range of tissues and has the potential of being used for endoscopic inspections in the near future.

Tissue classification and diagnostics using a fiber probe for combined Raman and fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Cicchi, Riccardo; Anand, Suresh; Crisci, Alfonso; Giordano, Flavio; Rossari, Susanna; De Giorgi, Vincenzo; Maio, Vincenza; Massi, Daniela; Nesi, Gabriella; Buccoliero, Anna Maria; Guerrini, Renzo; Pimpinelli, Nicola; Pavone, Francesco S.

2015-07-01

Two different optical fiber probes for combined Raman and fluorescence spectroscopic measurements were designed, developed and used for tissue diagnostics. Two visible laser diodes were used for fluorescence spectroscopy, whereas a laser diode emitting in the NIR was used for Raman spectroscopy. The two probes were based on fiber bundles with a central multimode optical fiber, used for delivering light to the tissue, and 24 surrounding optical fibers for signal collection. Both fluorescence and Raman spectra were acquired using the same detection unit, based on a cooled CCD camera, connected to a spectrograph. The two probes were successfully employed for diagnostic purposes on various tissues in a good agreement with common routine histology. This study included skin, brain and bladder tissues and in particular the classification of: malignant melanoma against melanocytic lesions and healthy skin; urothelial carcinoma against healthy bladder mucosa; brain tumor against dysplastic brain tissue. The diagnostic capabilities were determined using a cross-validation method with a leave-one-out approach, finding very high sensitivity and specificity for all the examined tissues. The obtained results demonstrated that the multimodal approach is crucial for improving diagnostic capabilities. The system presented here can improve diagnostic capabilities on a broad range of tissues and has the potential of being used for endoscopic inspections in the near future.

Diagnosis and classification of mastocytosis in non-specialized versus reference centres: a Spanish Network on Mastocytosis (REMA) study on 122 patients.

PubMed

Sánchez-Muñoz, Laura; Morgado, Jose M; Álvarez-Twose, Ivan; Matito, Almudena; Garcia-Montero, Andrés C; Teodosio, Cristina; Jara-Acevedo, Maria; Mayado, Andrea; Mollejo, Manuela; Caldas, Carolina; González de Olano, David; Escribano, Luis; Orfao, Alberto

2016-01-01

The diagnosis of 'rare diseases', such as mastocytosis, remains a challenge. Despite this, the precise benefits of referral of mastocytosis patients to highly specialized reference centres are poorly defined and whether patients should be managed at non-specialized versus reference centres remains a matter of debate. To evaluate the quality and efficiency of diagnostic procedures performed at the reference centres for mastocytosis in Spain (REMA) versus other non-reference centres, we retrospectively analysed a series of 122 patients, for the overall degree of agreement obtained for the World Health Organization (WHO) diagnostic and classification criteria betwen the referring and REMA centres. Our results showed that not all WHO diagnostic criteria were frequently investigated at the referring centres. Among the five WHO diagnostic criteria, the highest degree of agreement was obtained for serum tryptase levels [median 90% (95% confidence interval 84-96%)]; in turn, the overall agreement was significantly lower for the major histopathological criterion [80% (72-89%)], and the other three minor criteria: cytomorphology [68% (56-80%)] immunophenotyping of BM mast cells [75% (62-87%)] and detection of the KIT mutation [34% (8-60%)]. Referral of patients with diagnostic suspicion of mastocytosis to a multidisciplinary reference centre improves diagnostic efficiency and quality. © 2015 John Wiley & Sons Ltd.

21 CFR 864.9600 - Potentiating media for in vitro diagnostic use.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Manufacture Blood and Blood Products § 864.9600 Potentiating media for in vitro diagnostic use. (a... to suspend red cells and to enhance cell reactions for antigen-antibody testing. (b) Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart...

21 CFR 864.9600 - Potentiating media for in vitro diagnostic use.

Code of Federal Regulations, 2013 CFR

2013-04-01

... Manufacture Blood and Blood Products § 864.9600 Potentiating media for in vitro diagnostic use. (a... to suspend red cells and to enhance cell reactions for antigen-antibody testing. (b) Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart...

21 CFR 864.9600 - Potentiating media for in vitro diagnostic use.

Code of Federal Regulations, 2012 CFR

2012-04-01

... Manufacture Blood and Blood Products § 864.9600 Potentiating media for in vitro diagnostic use. (a... to suspend red cells and to enhance cell reactions for antigen-antibody testing. (b) Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart...

21 CFR 864.9600 - Potentiating media for in vitro diagnostic use.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Manufacture Blood and Blood Products § 864.9600 Potentiating media for in vitro diagnostic use. (a... to suspend red cells and to enhance cell reactions for antigen-antibody testing. (b) Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart...

21 CFR 864.9600 - Potentiating media for in vitro diagnostic use.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Manufacture Blood and Blood Products § 864.9600 Potentiating media for in vitro diagnostic use. (a... to suspend red cells and to enhance cell reactions for antigen-antibody testing. (b) Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart...

Grassland and shrubland habitat types of western Montana

Treesearch

W. F. Mueggler; W. L. Stewart

1978-01-01

A classification system based upon potential natural vegetation is presented for the grasslands and shrublands of the mountainous western third of Montana. The classification was developed by analyzing data from 580 stands. Twenty-nine habitat types in 13 climax series are defined and a diagnostic key provided for field identification. Environment, vegetative...

Utility of Intelligence Tests for Treatment Planning, Classification, and Placement Decisions: Recent Empirical Findings and Future Directions.

ERIC Educational Resources Information Center

Gresham, Frank M.; Witt, Joseph C.

1997-01-01

Maintains that intelligence tests contribute little to the planning, implementation, and evaluation of instructional interventions for children. Suggests that intelligence tests are not useful in making differential diagnostic and classification determinations for children with mild learning problems and that such testing is not a cost-beneficial…

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.

[Nosological classification and assessment of muscle dysmorphia].

PubMed

Babusa, Bernadett; Túry, Ferenc

2011-01-01

Muscle dysmorphia is a recently described psychiatric disorder, characterized by a pathological preoccupation with muscle size. In spite of their huge muscles, muscle dysmorphia sufferers believe that they are insufficiently large and muscular therefore would like to be bigger and more muscular. Male bodybuilders are at high-risk for the disorder. The nosological classification of muscle dysmorphia has been changed over the years. However, consensus has not emerged so far. Most of the ongoing debate has conceptualized muscle dysmorphia as an eating disorder, obsessive-compulsive disorder and body dysmorphic disorder. There are a number of arguments for and againts. In the present study the authors do not take a position on the diagnostic classification of muscle dysmorphia. The purpose of the study is to review the present approaches relating to the diagnostic classification of muscle dysmporphia. Many different questionnaires were developed for the assessment of muscle dysmorphia. Currently, there is a lack of assessment methods measuring muscle dysmorphia symptoms in Hungary. As a secondary purpose the study also presents the Hungarian version of the Muscle Appearance Satisfaction Scale (Mayville et al., 2002).

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

PubMed

Kebschull, Moritz; Papapanou, Panos N

2017-01-01

Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of -omics data.

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.

Disorders without borders: current and future directions in the meta-structure of mental disorders.

PubMed

Carragher, Natacha; Krueger, Robert F; Eaton, Nicholas R; Slade, Tim

2015-03-01

Classification is the cornerstone of clinical diagnostic practice and research. However, the extant psychiatric classification systems are not well supported by research evidence. In particular, extensive comorbidity among putatively distinct disorders flags an urgent need for fundamental changes in how we conceptualize psychopathology. Over the past decade, research has coalesced on an empirically based model that suggests many common mental disorders are structured according to two correlated latent dimensions: internalizing and externalizing. We review and discuss the development of a dimensional-spectrum model which organizes mental disorders in an empirically based manner. We also touch upon changes in the DSM-5 and put forward recommendations for future research endeavors. Our review highlights substantial empirical support for the empirically based internalizing-externalizing model of psychopathology, which provides a parsimonious means of addressing comorbidity. As future research goals, we suggest that the field would benefit from: expanding the meta-structure of psychopathology to include additional disorders, development of empirically based thresholds, inclusion of a developmental perspective, and intertwining genomic and neuroscience dimensions with the empirical structure of psychopathology.

[Surgical treatment of chronic pancreatitis based on classification of M. Buchler and coworkers].

PubMed

Krivoruchko, I A; Boĭko, V V; Goncharova, N N; Andreeshchev, S A

2011-08-01

The results of surgical treatment of 452 patients, suffering chronic pancreatitis (CHP), were analyzed. The CHP classification, elaborated by M. Buchler and coworkers (2009), based on clinical signs, morphological peculiarities and pancreatic function analysis, contains scientifically substantiated recommendations for choice of diagnostic methods and complex treatment of the disease. The classification proposed is simple in application and constitutes an instrument for studying and comparison of the CHP course severity, the patients prognosis and treatment.

Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the International Classification of Sleep Disorders (ICSD).

PubMed

Ohayon, Maurice M; Reynolds, Charles F

2009-10-01

Although the epidemiology of insomnia in the general population has received considerable attention in the past 20 years, few studies have investigated the prevalence of insomnia using operational definitions such as those set forth in the ICSD and DSM-IV, specifying what proportion of respondents satisfied the criteria to reach a diagnosis of insomnia disorder. This is a cross-sectional study involving 25,579 individuals aged 15 years and over representative of the general population of France, the United Kingdom, Germany, Italy, Portugal, Spain and Finland. The participants were interviewed on sleep habits and disorders managed by the Sleep-EVAL expert system using DSM-IV and ICSD classifications. At the complaint level, too short sleep (20.2%), light sleep (16.6%), and global sleep dissatisfaction (8.2%) were reported by 37% of the subjects. At the symptom level (difficulty initiating or maintaining sleep and non-restorative sleep at least 3 nights per week), 34.5% of the sample reported at least one of them. At the criterion level, (symptoms+daytime consequences), 9.8% of the total sample reported having them. At the diagnostic level, 6.6% satisfied the DSM-IV requirement for positive and differential diagnosis. However, many respondents failed to meet diagnostic criteria for duration, frequency and severity in the two classifications, suggesting that multidimensional measures are needed. A significant proportion of the population with sleep complaints do not fit into DSM-IV and ICSD classifications. Further efforts are needed to identify diagnostic criteria and dimensional measures that will lead to insomnia diagnoses and thus provide a more reliable, valid and clinically relevant classification.

Leucocyte classification for leukaemia detection using image processing techniques.

PubMed

Putzu, Lorenzo; Caocci, Giovanni; Di Ruberto, Cecilia

2014-11-01

The counting and classification of blood cells allow for the evaluation and diagnosis of a vast number of diseases. The analysis of white blood cells (WBCs) allows for the detection of acute lymphoblastic leukaemia (ALL), a blood cancer that can be fatal if left untreated. Currently, the morphological analysis of blood cells is performed manually by skilled operators. However, this method has numerous drawbacks, such as slow analysis, non-standard accuracy, and dependences on the operator's skill. Few examples of automated systems that can analyse and classify blood cells have been reported in the literature, and most of these systems are only partially developed. This paper presents a complete and fully automated method for WBC identification and classification using microscopic images. In contrast to other approaches that identify the nuclei first, which are more prominent than other components, the proposed approach isolates the whole leucocyte and then separates the nucleus and cytoplasm. This approach is necessary to analyse each cell component in detail. From each cell component, different features, such as shape, colour and texture, are extracted using a new approach for background pixel removal. This feature set was used to train different classification models in order to determine which one is most suitable for the detection of leukaemia. Using our method, 245 of 267 total leucocytes were properly identified (92% accuracy) from 33 images taken with the same camera and under the same lighting conditions. Performing this evaluation using different classification models allowed us to establish that the support vector machine with a Gaussian radial basis kernel is the most suitable model for the identification of ALL, with an accuracy of 93% and a sensitivity of 98%. Furthermore, we evaluated the goodness of our new feature set, which displayed better performance with each evaluated classification model. The proposed method permits the analysis of blood cells automatically via image processing techniques, and it represents a medical tool to avoid the numerous drawbacks associated with manual observation. This process could also be used for counting, as it provides excellent performance and allows for early diagnostic suspicion, which can then be confirmed by a haematologist through specialised techniques. Copyright © 2014 Elsevier B.V. All rights reserved.

Intelligent quotient estimation of mental retarded people from different psychometric instruments using artificial neural networks.

PubMed

Di Nuovo, Alessandro G; Di Nuovo, Santo; Buono, Serafino

2012-02-01

The estimation of a person's intelligence quotient (IQ) by means of psychometric tests is indispensable in the application of psychological assessment to several fields. When complex tests as the Wechsler scales, which are the most commonly used and universally recognized parameter for the diagnosis of degrees of retardation, are not applicable, it is necessary to use other psycho-diagnostic tools more suited for the subject's specific condition. But to ensure a homogeneous diagnosis it is necessary to reach a common metric, thus, the aim of our work is to build models able to estimate accurately and reliably the Wechsler IQ, starting from different psycho-diagnostic tools. Four different psychometric tests (Leiter international performance scale; coloured progressive matrices test; the mental development scale; psycho educational profile), along with the Wechsler scale, were administered to a group of 40 mentally retarded subjects, with various pathologies, and control persons. The obtained database is used to evaluate Wechsler IQ estimation models starting from the scores obtained in the other tests. Five modelling methods, two statistical and three from machine learning, that belong to the family of artificial neural networks (ANNs) are employed to build the estimator. Several error metrics for estimated IQ and for retardation level classification are defined to compare the performance of the various models with univariate and multivariate analyses. Eight empirical studies show that, after ten-fold cross-validation, best average estimation error is of 3.37 IQ points and mental retardation level classification error of 7.5%. Furthermore our experiments prove the superior performance of ANN methods over statistical regression ones, because in all cases considered ANN models show the lowest estimation error (from 0.12 to 0.9 IQ points) and the lowest classification error (from 2.5% to 10%). Since the estimation performance is better than the confidence interval of Wechsler scales (five IQ points), we consider models built very accurate and reliable and they can be used into help clinical diagnosis. Therefore a computer software based on the results of our work is currently used in a clinical center and empirical trails confirm its validity. Furthermore positive results in our multivariate studies suggest new approaches for clinicians. Copyright © 2011 Elsevier B.V. All rights reserved.

Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation

PubMed Central

2014-01-01

Background The pediatric complex chronic conditions (CCC) classification system, developed in 2000, requires revision to accommodate the International Classification of Disease 10th Revision (ICD-10). To update the CCC classification system, we incorporated ICD-9 diagnostic codes that had been either omitted or incorrectly specified in the original system, and then translated between ICD-9 and ICD-10 using General Equivalence Mappings (GEMs). We further reviewed all codes in the ICD-9 and ICD-10 systems to include both diagnostic and procedural codes indicative of technology dependence or organ transplantation. We applied the provisional CCC version 2 (v2) system to death certificate information and 2 databases of health utilization, reviewed the resulting CCC classifications, and corrected any misclassifications. Finally, we evaluated performance of the CCC v2 system by assessing: 1) the stability of the system between ICD-9 and ICD-10 codes using data which included both ICD-9 codes and ICD-10 codes; 2) the year-to-year stability before and after ICD-10 implementation; and 3) the proportions of patients classified as having a CCC in both the v1 and v2 systems. Results The CCC v2 classification system consists of diagnostic and procedural codes that incorporate a new neonatal CCC category as well as domains of complexity arising from technology dependence or organ transplantation. CCC v2 demonstrated close comparability between ICD-9 and ICD-10 and did not detect significant discontinuity in temporal trends of death in the United States. Compared to the original system, CCC v2 resulted in a 1.0% absolute (10% relative) increase in the number of patients identified as having a CCC in national hospitalization dataset, and a 0.4% absolute (24% relative) increase in a national emergency department dataset. Conclusions The updated CCC v2 system is comprehensive and multidimensional, and provides a necessary update to accommodate widespread implementation of ICD-10. PMID:25102958

Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT-BCC-SVM algorithm

NASA Astrophysics Data System (ADS)

Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu

2017-07-01

In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

An appraisal of current dysphagia diagnosis and treatment strategies.

PubMed

Kaindlstorfer, Adolf; Pointner, Rudolph

2016-08-01

Dysphagia is a common, serious health problem with a wide variety of etiologies and manifestations. This review gives a general overview of diagnostic and therapeutic options for oropharyngeal as well as esophageal swallowing disorders respecting the considerable progress made over recent years. Diagnosis can be challenging and requires expertise in interpretation of symptoms and patient history. Endoscopy, barium radiography and manometry are still the diagnostic mainstays. Classification of esophageal motor-disorders has been revolutionized with the introduction of high-resolution esophageal pressure topography and a new standardized classification algorithm. Automated integrated impedance manometry is a promising upcoming tool for objective evaluation of oropharyngeal dysphagia, in non-obstructive esophageal dysphagia and prediction of post fundoplication dysphagia risk. Impedance planimetry provides new diagnostic information on esophageal and LES-distensibility and allows controlled therapeutic dilatation without the need for radiation. Peroral endoscopic myotomy is a promising therapeutic approach for achalasia and spastic motility disorders.

The APA classification of mental disorders: future perspectives.

PubMed

Regier, Darrel A; Narrow, William E; First, Michael B; Marshall, Tina

2002-01-01

After 8-10 years of experience with the fourth edition of the Diagnostic and Statistical Manual (DSM-IV) and the tenth edition of the International Classification of Diseases (ICD-10), it is an ideal time to begin looking at the clinical and research consequences of these diagnostic systems. The American Psychiatric Association, in conjunction with the National Institutes of Health, has initiated a research development process intended to accelerate an evaluation of existing criteria while developing and testing hypotheses that would improve the validity of our diagnostic concepts. Over the past year, a multidisciplinary, international panel has developed a series of six white papers which define research opportunities in the following broad areas: Nomenclature, Disability and Impairment, Personality Disorders, Relational Disorders, Developmental Psychopathology, Neuroscience, and Cross-Cultural aspects of Psychopathology. Recommendations for future national and international research in each of these areas will be discussed. Copyright 2002 S. Karger AG, Basel

Diagnostic value of stool DNA testing for multiple markers of colorectal cancer and advanced adenoma: a meta-analysis.

PubMed

Yang, Hua; Xia, Bing-Qing; Jiang, Bo; Wang, Guozhen; Yang, Yi-Peng; Chen, Hao; Li, Bing-Sheng; Xu, An-Gao; Huang, Yun-Bo; Wang, Xin-Ying

2013-08-01

The diagnostic value of stool DNA (sDNA) testing for colorectal neoplasms remains controversial. To compensate for the lack of large-scale unbiased population studies, a meta-analysis was performed to evaluate the diagnostic value of sDNA testing for multiple markers of colorectal cancer (CRC) and advanced adenoma. The PubMed, Science Direct, Biosis Review, Cochrane Library and Embase databases were systematically searched in January 2012 without time restriction. Meta-analysis was performed using a random-effects model using sensitivity, specificity, diagnostic OR (DOR), summary ROC curves, area under the curve (AUC), and 95% CIs as effect measures. Heterogeneity was measured using the χ(2) test and Q statistic; subgroup analysis was also conducted. A total of 20 studies comprising 5876 individuals were eligible. There was no heterogeneity for CRC, but adenoma and advanced adenoma harboured considerable heterogeneity influenced by risk classification and various detection markers. Stratification analysis according to risk classification showed that multiple markers had a high DOR for the high-risk subgroups of both CRC (sensitivity 0.759 [95% CI 0.711 to 0.804]; specificity 0.883 [95% CI 0.846 to 0.913]; AUC 0.906) and advanced adenoma (sensitivity 0.683 [95% CI 0.584 to 0.771]; specificity 0.918 [95% CI 0.866 to 0.954]; AUC 0.946) but not for the average-risk subgroups of either. In the methylation subgroup, sDNA testing had significantly higher DOR for CRC (sensitivity 0.753 [95% CI 0.685 to 0.812]; specificity 0.913 [95% CI 0.860 to 0.950]; AUC 0.918) and advanced adenoma (sensitivity 0.623 [95% CI 0.527 to 0.712]; specificity 0.926 [95% CI 0.882 to 0.958]; AUC 0.910) compared with the mutation subgroup. There was no significant heterogeneity among studies for subgroup analysis. sDNA testing for multiple markers had strong diagnostic significance for CRC and advanced adenoma in high-risk subjects. Methylation makers had more diagnostic value than mutation markers.

Minimally Invasive Molecular Staging (MIMS) RT-PCR Breast Cancer Study

DTIC Science & Technology

2007-03-31

per se. 15. SUBJECT TERMS Breast cancer, real-time PCR, molecular diagnostics , micrometastases 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18...a superior molecular marker for detection of non- small cell lung cancer in peripheral blood. Journal of Molecular Diagnostics . 5:23 7-42, 2003. With...Journal of Molecular Diagnostics . 5:237-42, 2003 10. Mikhitarian K, Allen A, Reott S, Hoover L, Allen A, Cole DJ, Gillanders WE, and Mitas M. Enhanced

Psychopathology, biopsychosocial factors, crime characteristics, and classification of 25 homicidal youths.

PubMed

Myers, W C; Scott, K; Burgess, A W; Burgess, A G

1995-11-01

This study investigates diagnostic, behavioral, offense, and classification characteristics of juvenile murderers. Twenty-five homicidal children and adolescents were assessed using the Diagnostic Interview for Children and Adolescents, clinical interviews, record review, and all available collateral data. DSM-III-R psychopathology was found in 96% of these youths, and one half of them had experienced suicidal ideation at some point in their lives. Nevertheless, only 17% had ever received mental health treatment. Family and school dysfunction were present in virtually all subjects. Histories of abuse, prior violence, arrests, and promiscuous sexual behavior were typical. Motives were equally divided between crime-based and conflict-based causes. A weapon was used in 96% of cases. Significant differences were found between crime classification groups and victim age, physical abuse, IQ, and victim relationship. In addition, those who committed sexual homicide were significantly more likely to have engaged in overkill, used a knife, and been armed beforehand. Ten profile characteristics present in at least 70% of these juveniles were identified. All murders were readily classified according to the FBI Crime Classification Manual (CCM). These findings support juvenile murderers as being an inadequately treated, emotionally and behaviorally disturbed population with profound social problems. The CCM proved to be a useful instrument for the classification of this sample.

Automatic tissue characterization from ultrasound imagery

NASA Astrophysics Data System (ADS)

Kadah, Yasser M.; Farag, Aly A.; Youssef, Abou-Bakr M.; Badawi, Ahmed M.

1993-08-01

In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of parameters representing the most discriminating pattern space for classification. This preprocessing step was applied to over 120 pathology-investigated cases to obtain the learning data for designing the classifier. The extracted features are divided into independent training and test sets and are used to construct both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms for implementing various classification techniques are presented and tested on the data. The best performance was obtained using a single layer tensor model functional link network. Also, the voting k-nearest neighbor classifier provided comparably good diagnostic rates.

[Diagnosis, prognosis, and prediction of non-small cell lung cancer. Importance of morphology, immunohistochemistry and molecular pathology].

PubMed

Warth, A

2015-11-01

Tumor diagnostics are based on histomorphology, immunohistochemistry and molecular pathological analysis of mutations, translocations and amplifications which are of diagnostic, prognostic and/or predictive value. In recent decades only histomorphology was used to classify lung cancer as either small (SCLC) or non-small cell lung cancer (NSCLC), although NSCLC was further subdivided in different entities; however, as no specific therapy options were available classification of specific subtypes was not clinically meaningful. This fundamentally changed with the discovery of specific molecular alterations in adenocarcinoma (ADC), e.g. mutations in KRAS, EGFR and BRAF or translocations of the ALK and ROS1 gene loci, which now form the basis of targeted therapies and have led to a significantly improved patient outcome. The diagnostic, prognostic and predictive value of imaging, morphological, immunohistochemical and molecular characteristics as well as their interaction were systematically assessed in a large cohort with available clinical data including patient survival. Specific and sensitive diagnostic markers and marker panels were defined and diagnostic test algorithms for predictive biomarker assessment were optimized. It was demonstrated that the semi-quantitative assessment of ADC growth patterns is a stage-independent predictor of survival and is reproducibly applicable in the routine setting. Specific histomorphological characteristics correlated with computed tomography (CT) imaging features and thus allowed an improved interdisciplinary classification, especially in the preoperative or palliative setting. Moreover, specific molecular characteristics, for example BRAF mutations and the proliferation index (Ki-67) were identified as clinically relevant prognosticators. Comprehensive clinical, morphological, immunohistochemical and molecular assessment of NSCLCs allow an optimized patient stratification. Respective algorithms now form the backbone of the 2015 lung cancer World Health Organization (WHO) classification.

Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset

NASA Astrophysics Data System (ADS)

Hancock, Matthew C.; Magnan, Jerry F.

2017-03-01

To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capabilities of statistical learning methods for classifying nodule malignancy, utilizing the Lung Image Database Consortium (LIDC) dataset, and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that is achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (+/-1.14)% which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (+/-0.012), which increases to 0.949 (+/-0.007) when diameter and volume features are included, along with the accuracy to 88.08 (+/-1.11)%. Our results are comparable to those in the literature that use algorithmically-derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features, and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

TMS combined with EEG in genetic generalized epilepsy: A phase II diagnostic accuracy study.

PubMed

Kimiskidis, Vasilios K; Tsimpiris, Alkiviadis; Ryvlin, Philippe; Kalviainen, Reetta; Koutroumanidis, Michalis; Valentin, Antonio; Laskaris, Nikolaos; Kugiumtzis, Dimitris

2017-02-01

(A) To develop a TMS-EEG stimulation and data analysis protocol in genetic generalized epilepsy (GGE). (B) To investigate the diagnostic accuracy of TMS-EEG in GGE. Pilot experiments resulted in the development and optimization of a paired-pulse TMS-EEG protocol at rest, during hyperventilation (HV), and post-HV combined with multi-level data analysis. This protocol was applied in 11 controls (C) and 25 GGE patients (P), further dichotomized into responders to antiepileptic drugs (R, n=13) and non-responders (n-R, n=12).Features (n=57) extracted from TMS-EEG responses after multi-level analysis were given to a feature selection scheme and a Bayesian classifier, and the accuracy of assigning participants into the classes P-C and R-nR was computed. On the basis of the optimal feature subset, the cross-validated accuracy of TMS-EEG for the classification P-C was 0.86 at rest, 0.81 during HV and 0.92 at post-HV, whereas for R-nR the corresponding figures are 0.80, 0.78 and 0.65, respectively. Applying a fusion approach on all conditions resulted in an accuracy of 0.84 for the classification P-C and 0.76 for the classification R-nR. TMS-EEG can be used for diagnostic purposes and for assessing the response to antiepileptic drugs. TMS-EEG holds significant diagnostic potential in GGE. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Primary Immunodeficiency Diseases: An Update on the Classification from the International Union of Immunological Societies Expert Committee for Primary Immunodeficiency

PubMed Central

Al-Herz, Waleed; Bousfiha, Aziz; Casanova, Jean-Laurent; Chapel, Helen; Conley, Mary Ellen; Cunningham-Rundles, Charlotte; Etzioni, Amos; Fischer, Alain; Franco, Jose Luis; Geha, Raif S.; Hammarström, Lennart; Nonoyama, Shigeaki; Notarangelo, Luigi Daniele; Ochs, Hans Dieter; Puck, Jennifer M.; Roifman, Chaim M.; Seger, Reinhard; Tang, Mimi L. K.

2011-01-01

We report the updated classification of primary immunodeficiency diseases, compiled by the ad hoc Expert Committee of the International Union of Immunological Societies. As compared to the previous edition, more than 15 novel disease entities have been added in the updated version. For each disorders, the key clinical and laboratory features are provided. This updated classification is meant to help in the diagnostic approach to patients with these diseases. PMID:22566844

To ID or Not to ID? Changes in Classification Rates of Intellectual Disability Using "DSM-5"

ERIC Educational Resources Information Center

Papazoglou, Aimilia; Jacobson, Lisa A.; McCabe, Marie; Kaufmann, Walter; Zabel, T. Andrew

2014-01-01

The "Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition" ("DSM-5") diagnostic criteria for intellectual disability (ID) include a change to the definition of adaptive impairment. New criteria require impairment in one adaptive domain rather than two or more skill areas. The authors examined the diagnostic…

Bibliography of Soviet Laser Developments, Number 58, March-April 1982.

DTIC Science & Technology

1983-05-01

generation and diagnostics . DD IA7 1473 EOITION OF I NOV A OSOLETE UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (When Does Entered) Introduction...10 C. He-Kr............................................. 10 iv 2. Molecular Beam and lIon a. C02...104 K. Plasma Generation and Diagnostics ....................... 105 III. MONOGRAPHS, BOOKS, CONFERENCE PROCEEDINGS................... 113

Re-Conceptualizing ASD within a Dimensional Framework: Positive, Negative, and Cognitive Feature Clusters

ERIC Educational Resources Information Center

Foss-Feig, Jennifer H.; McPartland, James C.; Anticevic, Alan; Wolf, Julie

2016-01-01

Introduction of the National Institute of Mental Health's Research Domain Criteria and revision of diagnostic classification for Autism Spectrum Disorder in the latest diagnostic manual call for a new way of conceptualizing heterogeneous ASD features. We propose a novel conceptualization of ASD, borrowing from the schizophrenia literature in…

Understanding the Latent Structure of the Emotional Disorders in Children and Adolescents

ERIC Educational Resources Information Center

Trosper, Sarah E.; Whitton, Sarah W.; Brown, Timothy A.; Pincus, Donna B.

2012-01-01

Investigators are persistently aiming to clarify structural relationships among the emotional disorders in efforts to improve diagnostic classification. The high co-occurrence of anxiety and mood disorders, however, has led investigators to portray the current structure of anxiety and depression in the "Diagnostic and Statistical Manual of Mental…

Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

ERIC Educational Resources Information Center

Cui, Ying; Gierl, Mark; Guo, Qi

2016-01-01

The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

Diagnosticity and Prototypicality in Category Learning: A Comparison of Inference Learning and Classification Learning

ERIC Educational Resources Information Center

Chin-Parker, Seth; Ross, Brian H.

2004-01-01

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning,…

Three Diagnostic Systems for Autism: DSM-III, DSM-III-R, and ICD-10.

ERIC Educational Resources Information Center

Volkmar, Fred R.; And Others

1992-01-01

This paper compared clinicians' diagnosis and DSM-III (Diagnostic and Statistical Manual), DSM-III-R (Revised), and ICD-10 (International Classification of Diseases) diagnoses of 52 individuals with autism and 62 nonautistic, developmentally disordered individuals. The DSM-III-R system overdiagnosed the presence of autism, and ICD-10 closely…

Current Issues in the Diagnosis of Attention Deficit Hyperactivity Disorder, Oppositional Defiant Disorder, and Conduct Disorder

PubMed Central

Frick, Paul J.; Nigg, Joel T.

2015-01-01

This review evaluates the diagnostic criteria for three of the most common disorders for which children and adolescents are referred for mental health treatment: attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD). Although research supports the validity and clinical utility of these disorders, several issues are highlighted that could enhance the current diagnostic criteria. For ADHD, defining the core features of the disorder and its fit with other disorders, enhancing the validity of the criteria through the lifespan, considering alternative ways to form subtypes of the disorder, and modifying the age-of-onset criterion are discussed relative to the current diagnostic criteria. For ODD, eliminating the exclusionary criteria of CD, recognizing important symptom domains within the disorder, and using the cross-situational pervasiveness of the disorder as an index of severity are highlighted as important issues for improving classification. Finally, for CD, enhancing the current subtypes related to age of onset and integrating callous-unemotional traits into the diagnostic criteria are identified as key issues for improving classification. PMID:22035245

Categorical and dimensional approaches in the evaluation of the relationship between attachment and personality disorders: an empirical study.

PubMed

Chiesa, Marco; Cirasola, Antonella; Williams, Riccardo; Nassisi, Valentina; Fonagy, Peter

2017-04-01

Although several studies have highlighted the relationship between attachment states of mind and personality disorders, their findings have not been consistent, possibly due to the application of the traditional taxonomic classification model of attachment. A more recently developed dimensional classification of attachment representations, including more specific aspects of trauma-related representations, may have advantages. In this study, we compare specific associations and predictive power of the categorical attachment and dimensional models applied to 230 Adult Attachment Interview transcripts obtained from personality disordered and nonpsychiatric subjects. We also investigate the role that current levels of psychiatric distress may have in the prediction of PD. The results showed that both models predict the presence of PD, with the dimensional approach doing better in discriminating overall diagnosis of PD. However, both models are less helpful in discriminating specific PD diagnostic subtypes. Current psychiatric distress was found to be the most consistent predictor of PD capturing a large share of the variance and obscuring the role played by attachment variables. The results suggest that attachment parameters correlate with the presence of PD alone and have no specific associations with particular PD subtypes when current psychiatric distress is taken into account.

Factorial Validity of the ADHD Adult Symptom Rating Scale in a French Community Sample: Results From the ChiP-ARD Study.

PubMed

Morin, Alexandre J S; Tran, Antoine; Caci, Hervé

2016-06-01

Recent publications reported that a bifactor model better represented the underlying structure of ADHD than classical models, at least in youth. The Adult ADHD Symptoms Rating Scale (ASRS) has been translated into many languages, but a single study compared its structure in adults across Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) and International Classification of Diseases (ICD-10) classifications. We investigated the factor structure, reliability, and measurement invariance of the ASRS among a community sample of 1,171 adults. Results support a bifactor model, including one general ADHD factor and three specific Inattention, Hyperactivity, and Impulsivity factors corresponding to ICD-10, albeit the Impulsivity specific factor was weakly defined. Results also support the complete measurement invariance of this model across gender and age groups, and that men have higher scores than women on the ADHD G-factor but lower scores on all three S-factors. Results suggest that a total ASRS-ADHD score is meaningful, reliable, and valid in adults. (J. of Att. Dis. 2016; 20(6) 530-541). © The Author(s) 2013.

Knowledge Representation Of CT Scans Of The Head

NASA Astrophysics Data System (ADS)

Ackerman, Laurens V.; Burke, M. W.; Rada, Roy

1984-06-01

We have been investigating diagnostic knowledge models which assist in the automatic classification of medical images by combining information extracted from each image with knowledge specific to that class of images. In a more general sense we are trying to integrate verbal and pictorial descriptions of disease via representations of knowledge, study automatic hypothesis generation as related to clinical medicine, evolve new mathematical image measures while integrating them into the total diagnostic process, and investigate ways to augment the knowledge of the physician. Specifically, we have constructed an artificial intelligence knowledge model using the technique of a production system blending pictorial and verbal knowledge about the respective CT scan and patient history. It is an attempt to tie together different sources of knowledge representation, picture feature extraction and hypothesis generation. Our knowledge reasoning and representation system (KRRS) works with data at the conscious reasoning level of the practicing physician while at the visual perceptional level we are building another production system, the picture parameter extractor (PPE). This paper describes KRRS and its relationship to PPE.

Enhancements to the Engine Data Interpretation System (EDIS)

NASA Technical Reports Server (NTRS)

Hofmann, Martin O.

1993-01-01

The Engine Data Interpretation System (EDIS) expert system project assists the data review personnel at NASA/MSFC in performing post-test data analysis and engine diagnosis of the Space Shuttle Main Engine (SSME). EDIS uses knowledge of the engine, its components, and simple thermodynamic principles instead of, and in addition to, heuristic rules gathered from the engine experts. EDIS reasons in cooperation with human experts, following roughly the pattern of logic exhibited by human experts. EDIS concentrates on steady-state static faults, such as small leaks, and component degradations, such as pump efficiencies. The objective of this contract was to complete the set of engine component models, integrate heuristic rules into EDIS, integrate the Power Balance Model into EDIS, and investigate modification of the qualitative reasoning mechanisms to allow 'fuzzy' value classification. The results of this contract is an operational version of EDIS. EDIS will become a module of the Post-Test Diagnostic System (PTDS) and will, in this context, provide system-level diagnostic capabilities which integrate component-specific findings provided by other modules.

Enhancements to the Engine Data Interpretation System (EDIS)

NASA Technical Reports Server (NTRS)

Hofmann, Martin O.

1993-01-01

The Engine Data Interpretation System (EDIS) expert system project assists the data review personnel at NASA/MSFC in performing post-test data analysis and engine diagnosis of the Space Shuttle Main Engine (SSME). EDIS uses knowledge of the engine, its components, and simple thermodynamic principles instead of, and in addition to, heuristic rules gathered from the engine experts. EDIS reasons in cooperation with human experts, following roughly the pattern of logic exhibited by human experts. EDIS concentrates on steady-state static faults, such as small leaks, and component degradations, such as pump efficiencies. The objective of this contract was to complete the set of engine component models, integrate heuristic rules into EDIS, integrate the Power Balance Model into EDIS, and investigate modification of the qualitative reasoning mechanisms to allow 'fuzzy' value classification. The result of this contract is an operational version of EDIS. EDIS will become a module of the Post-Test Diagnostic System (PTDS) and will, in this context, provide system-level diagnostic capabilities which integrate component-specific findings provided by other modules.

Understanding the latent structure of the emotional disorders in children and adolescents.

PubMed

Trosper, Sarah E; Whitton, Sarah W; Brown, Timothy A; Pincus, Donna B

2012-05-01

Investigators are persistently aiming to clarify structural relationships among the emotional disorders in efforts to improve diagnostic classification. The high co-occurrence of anxiety and mood disorders, however, has led investigators to portray the current structure of anxiety and depression in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, APA 2000) as more descriptive than empirical. This study assesses various structural models in a clinical sample of youths with emotional disorders. Three a priori factor models were tested, and the model that provided the best fit to the data showed the dimensions of anxiety and mood disorders to be hierarchically organized within a single, higher-order factor. This supports the prevailing view that the co-occurrence of anxiety and mood disorders in children is in part due to a common vulnerability (e.g., negative affectivity). Depression and generalized anxiety loaded more highly onto the higher-order factor than the other disorders, a possible explanation for the particularly high rates of comorbidity between the two. Implications for the taxonomy and treatment of mood and anxiety disorders for children and adolescents are discussed.

The ICD diagnoses of fetishism and sadomasochism.

PubMed

Reiersøl, Odd; Skeid, Svein

2006-01-01

In this article we discuss psychiatric diagnoses of sexual deviation as they appear in the International Classification of Diseases (ICD-10), the internationally accepted classification and diagnostic system of the World Health Organization (WHO). Namely, we discuss the background of three diagnostic categories: Fetishism (F65.0), Fetishistic Transvestism (F65.1), and Sadomasochism (F65.5). Pertinent background issues regarding the above categories are followed by a critique of the usefulness of diagnosing these phenomena today. Specifically, we argue that Fetishism, Fetishistic Transvestism, and Sadomasochism, also labeled Paraphilia or perversion, should not be considered illnesses. Finally, we present the efforts of an initiative known as ReviseF65, which was established in 1997, to abolish these diagnoses.

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.

Usefulness of High-Frequency Ultrasound in the Classification of Histologic Subtypes of Primary Basal Cell Carcinoma.

PubMed

Hernández-Ibáñez, C; Blazquez-Sánchez, N; Aguilar-Bernier, M; Fúnez-Liébana, R; Rivas-Ruiz, F; de Troya-Martín, M

Incisional biopsy may not always provide a correct classification of histologic subtypes of basal cell carcinoma (BCC). High-frequency ultrasound (HFUS) imaging of the skin is useful for the diagnosis and management of this tumor. The main aim of this study was to compare the diagnostic value of HFUS compared with punch biopsy for the correct classification of histologic subtypes of primary BCC. We also analyzed the influence of tumor size and histologic subtype (single subtype vs. mixed) on the diagnostic yield of HFUS and punch biopsy. Retrospective observational study of primary BCCs treated by the Dermatology Department of Hospital Costa del Sol in Marbella, Spain, between october 2013 and may 2014. Surgical excision was preceded by HFUS imaging (Dermascan C © , 20-MHz linear probe) and a punch biopsy in all cases. We compared the overall diagnostic yield and accuracy (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) of HFUS and punch biopsy against the gold standard (excisional biopsy with serial sections) for overall and subgroup results. We studied 156 cases. The overall diagnostic yield was 73.7% for HFUS (sensitivity, 74.5%; specificity, 73%) and 79.9% for punch biopsy (sensitivity, 76%; specificity, 82%). In the subgroup analyses, HFUS had a PPV of 93.3% for superficial BCC (vs. 92% for punch biopsy). In the analysis by tumor size, HFUS achieved an overall diagnostic yield of 70.4% for tumors measuring 40mm 2 or less and 77.3% for larger tumors; the NPV was 82% in both size groups. Punch biopsy performed better in the diagnosis of small lesions (overall diagnostic yield of 86.4% for lesions ≤40mm 2 vs. 72.6% for lesions >40mm 2 ). HFUS imaging was particularly useful for ruling out infiltrating BCCs, diagnosing simple, superficial BCCs, and correctly classifying BCCs larger than 40mm 2 . Copyright © 2016 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.

Diagnostic accuracy of imaging devices in glaucoma: A meta-analysis.

PubMed

Fallon, Monica; Valero, Oliver; Pazos, Marta; Antón, Alfonso

Imaging devices such as the Heidelberg retinal tomograph-3 (HRT3), scanning laser polarimetry (GDx), and optical coherence tomography (OCT) play an important role in glaucoma diagnosis. A systematic search for evidence-based data was performed for prospective studies evaluating the diagnostic accuracy of HRT3, GDx, and OCT. The diagnostic odds ratio (DOR) was calculated. To compare the accuracy among instruments and parameters, a meta-analysis considering the hierarchical summary receiver-operating characteristic model was performed. The risk of bias was assessed using quality assessment of diagnostic accuracy studies, version 2. Studies in the context of screening programs were used for qualitative analysis. Eighty-six articles were included. The DOR values were 29.5 for OCT, 18.6 for GDx, and 13.9 for HRT. The heterogeneity analysis demonstrated statistically a significant influence of degree of damage and ethnicity. Studies analyzing patients with earlier glaucoma showed poorer results. The risk of bias was high for patient selection. Screening studies showed lower sensitivity values and similar specificity values when compared with those included in the meta-analysis. The classification capabilities of GDx, HRT, and OCT were high and similar across the 3 instruments. The highest estimated DOR was obtained with OCT. Diagnostic accuracy could be overestimated in studies including prediagnosed groups of subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study.

PubMed

Hilbert, Kevin; Lueken, Ulrike; Muehlhan, Markus; Beesdo-Baum, Katja

2017-03-01

Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD, MD and healthy subjects to differentiate subjects with a disorder from healthy subjects (case-classification) and to differentiate GAD from MD (disorder-classification). Subjects with GAD ( n  = 19), MD without GAD ( n  = 14), and healthy comparison subjects ( n  = 24) were included. The sample was matched regarding age, sex, handedness and education and free of psychopharmacological medication. Binary support vector machines were used within a nested leave-one-out cross-validation framework. Clinical questionnaires, cortisol release, gray matter (GM), and white matter (WM) volumes were used as input data separately and in combination. Questionnaire data were well-suited for case-classification but not disorder-classification (accuracies: 96.40%, p  < .001; 56.58%, p  > .22). The opposite pattern was found for imaging data (case-classification GM/WM: 58.71%, p  = .09/43.18%, p  > .66; disorder-classification GM/WM: 68.05%, p  = .034/58.27%, p  > .15) and for cortisol data (38.02%, p  = .84; 74.60%, p  = .009). All data combined achieved 90.10% accuracy ( p  < .001) for case-classification and 67.46% accuracy ( p  = .0268) for disorder-classification. In line with previous evidence, classification of GAD was difficult using clinical questionnaire data alone. Particularly cortisol and GM volume data were able to provide incremental value for the classification of GAD. Findings suggest that neurobiological biomarkers are a useful target for further research to delineate their potential contribution to diagnostic processes.

Diagnostic discrepancies in retinopathy of prematurity classification

PubMed Central

Campbell, J. Peter; Ryan, Michael C.; Lore, Emily; Tian, Peng; Ostmo, Susan; Jonas, Karyn; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts. Design Prospective cohort study. Subjects, Participants, and/or Controls 281 infants were identified as part of a multi-center, prospective, ROP cohort study from 7 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine examination using binocular indirect ophthalmoscopy (BIO), and obtained wide-angle retinal images, which were independently classified by two study experts. Methods Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and two experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, overall disease category (no ROP, mild ROP, Type II or pre-plus, and Type I) were compared between the two experts, and to the clinical classification obtained by BIO. Main Outcome Measures Inter-expert image-based agreement and image-based vs. ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic. Results 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620/1553 (40%) of comparisons, plus disease classification (including pre-plus) in 287/1553 (18%), zone in 117/1553 (8%), and overall ROP category in 618/1553 (40%). However, agreement for presence vs. absence of type 1 disease was >95%. There were no differences between image-based and clinical classification except for zone III disease. Conclusions The most common area of discrepancy in ROP classification is stage, although inter-expert agreement for clinically-significant disease such as presence vs. absence of type 1 and type 2 disease is high. There were no differences between image-based grading and the clinical exam in the ability to detect clinically-significant disease. This study provides additional evidence that image-based classification of ROP reliably detects clinically significant levels of ROP with high accuracy compared to the clinical exam. PMID:27238376

An empirical model for dissolution profile and its application to floating dosage forms.

PubMed

Weiss, Michael; Kriangkrai, Worawut; Sungthongjeen, Srisagul

2014-06-02

A sum of two inverse Gaussian functions is proposed as a highly flexible empirical model for fitting of in vitro dissolution profiles. The model was applied to quantitatively describe theophylline release from effervescent multi-layer coated floating tablets containing different amounts of the anti-tacking agents talc or glyceryl monostearate. Model parameters were estimated by nonlinear regression (mixed-effects modeling). The estimated parameters were used to determine the mean dissolution time, as well as to reconstruct the time course of release rate for each formulation, whereby the fractional release rate can serve as a diagnostic tool for classification of dissolution processes. The approach allows quantification of dissolution behavior and could provide additional insights into the underlying processes. Copyright © 2014 Elsevier B.V. All rights reserved.

Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.

PubMed

Patel, Bhavika K; Ranjbar, Sara; Wu, Teresa; Pockaj, Barbara A; Li, Jing; Zhang, Nan; Lobbes, Mark; Zhang, Bin; Mitchell, J Ross

2018-01-01

To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists. This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. An expert breast radiologist manually outlined lesion boundaries on the different views. A set of morphologic and textural features were then extracted from the low-energy and recombined images. Machine-learning algorithms with feature selection were used along with statistical analysis to reduce, select, and combine features. Selected features were then used to construct a predictive model using a support vector machine (SVM) classification method in a leave-one-out-cross-validation approach. The classification performance was compared against the diagnostic predictions of 2 breast radiologists with access to the same CESM cases. Based on the SVM classification, CAD-CESM correctly identified 45 of 50 lesions in the cohort, resulting in an overall accuracy of 90%. The detection rate for the malignant group was 88% (3 false-negative cases) and 92% for the benign group (2 false-positive cases). Compared with the model, radiologist 1 had an overall accuracy of 78% and a detection rate of 92% (2 false-negative cases) for the malignant group and 62% (10 false-positive cases) for the benign group. Radiologist 2 had an overall accuracy of 86% and a detection rate of 100% for the malignant group and 71% (8 false-positive cases) for the benign group. The results of our feasibility study suggest that a CAD-CESM tool can provide complementary information to radiologists, mainly by reducing the number of false-positive findings. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

Discrepancy between the clinical and histopathologic diagnosis of soft tissue vascular malformations.

PubMed

Horbach, Sophie E R; Utami, Amalia M; Meijer-Jorna, Lorine B; Sillevis Smitt, J H; Spuls, Phyllis I; van der Horst, Chantal M A M; van der Wal, Allard C

2017-11-01

Soft tissue vascular malformations are generally diagnosed clinically, according to the International Society for the Study of Vascular Anomalies (ISSVA) classification. Diagnostic histopathologic examination is rarely performed. We sought to evaluate the validity of the current diagnostic workup without routinely performed diagnostic histopathology. We retrospectively determined whether there were discrepancies between clinical and histopathologic diagnoses of patients with clinically diagnosed vascular malformations undergoing therapeutic surgical resections in our center (2000-2015). Beforehand, a pathologist revised the histopathologic diagnoses according to the ISSVA classification. Clinical and histopathologic diagnoses were discrepant in 57% of 142 cases. In these cases, the pathologist indicated the diagnosis was not at all a vascular malformation (n = 24; 17%), a completely different type of vascular malformation (n = 26; 18%), or a partially different type with regard to the combination of vessel-types involved (n = 31; 22%). Possible factors associated with the discrepancies were both clinician-related (eg, diagnostic uncertainty) and pathology-related (eg, lack of immunostaining). Retrospective analysis of a subgroup of patients undergoing surgery. The large discrepancy between clinical and histopathologic diagnoses raises doubt about the validity of the current diagnostic workup for vascular malformations. Clear clinical and histopathologic diagnostic criteria might be essential for a uniform diagnosis. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

PubMed

Ertosun, Mehmet Günhan; Rubin, Daniel L

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository.

Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

NASA Astrophysics Data System (ADS)

Amit, Guy; Ben-Ari, Rami; Hadad, Omer; Monovich, Einat; Granot, Noa; Hashoul, Sharbell

2017-03-01

Diagnostic interpretation of breast MRI studies requires meticulous work and a high level of expertise. Computerized algorithms can assist radiologists by automatically characterizing the detected lesions. Deep learning approaches have shown promising results in natural image classification, but their applicability to medical imaging is limited by the shortage of large annotated training sets. In this work, we address automatic classification of breast MRI lesions using two different deep learning approaches. We propose a novel image representation for dynamic contrast enhanced (DCE) breast MRI lesions, which combines the morphological and kinetics information in a single multi-channel image. We compare two classification approaches for discriminating between benign and malignant lesions: training a designated convolutional neural network and using a pre-trained deep network to extract features for a shallow classifier. The domain-specific trained network provided higher classification accuracy, compared to the pre-trained model, with an area under the ROC curve of 0.91 versus 0.81, and an accuracy of 0.83 versus 0.71. Similar accuracy was achieved in classifying benign lesions, malignant lesions, and normal tissue images. The trained network was able to improve accuracy by using the multi-channel image representation, and was more robust to reductions in the size of the training set. A small-size convolutional neural network can learn to accurately classify findings in medical images using only a few hundred images from a few dozen patients. With sufficient data augmentation, such a network can be trained to outperform a pre-trained out-of-domain classifier. Developing domain-specific deep-learning models for medical imaging can facilitate technological advancements in computer-aided diagnosis.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

PubMed Central

Ertosun, Mehmet Günhan; Rubin, Daniel L.

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository. PMID:26958289

Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD).

PubMed

Poil, S-S; Bollmann, S; Ghisleni, C; O'Gorman, R L; Klaver, P; Ball, J; Eich-Höchli, D; Brandeis, D; Michels, L

2014-08-01

Objective biomarkers for attention-deficit/hyperactivity disorder (ADHD) could improve diagnostics or treatment monitoring of this psychiatric disorder. The resting electroencephalogram (EEG) provides non-invasive spectral markers of brain function and development. Their accuracy as ADHD markers is increasingly questioned but may improve with pattern classification. This study provides an integrated analysis of ADHD and developmental effects in children and adults using regression analysis and support vector machine classification of spectral resting (eyes-closed) EEG biomarkers in order to clarify their diagnostic value. ADHD effects on EEG strongly depend on age and frequency. We observed typical non-linear developmental decreases in delta and theta power for both ADHD and control groups. However, for ADHD adults we found a slowing in alpha frequency combined with a higher power in alpha-1 (8-10Hz) and beta (13-30Hz). Support vector machine classification of ADHD adults versus controls yielded a notable cross validated sensitivity of 67% and specificity of 83% using power and central frequency from all frequency bands. ADHD children were not classified convincingly with these markers. Resting state electrophysiology is altered in ADHD, and these electrophysiological impairments persist into adulthood. Spectral biomarkers may have both diagnostic and prognostic value. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease

PubMed Central

Gray, Katherine R.; Wolz, Robin; Heckemann, Rolf A.; Aljabar, Paul; Hammers, Alexander; Rueckert, Daniel

2012-01-01

Imaging biomarkers for Alzheimer’s disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer’s disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer’s disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer’s disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials. PMID:22236449

A comparison of different chemometrics approaches for the robust classification of electronic nose data.

PubMed

Gromski, Piotr S; Correa, Elon; Vaughan, Andrew A; Wedge, David C; Turner, Michael L; Goodacre, Royston

2014-11-01

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365-372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56% accuracy) and SVM with a polynomial kernel (91.66% accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data.

Proposal for a New Predictive Model of Short-Term Mortality After Living Donor Liver Transplantation due to Acute Liver Failure.

PubMed

Chung, Hyun Sik; Lee, Yu Jung; Jo, Yun Sung

2017-02-21

BACKGROUND Acute liver failure (ALF) is known to be a rapidly progressive and fatal disease. Various models which could help to estimate the post-transplant outcome for ALF have been developed; however, none of them have been proved to be the definitive predictive model of accuracy. We suggest a new predictive model, and investigated which model has the highest predictive accuracy for the short-term outcome in patients who underwent living donor liver transplantation (LDLT) due to ALF. MATERIAL AND METHODS Data from a total 88 patients were collected retrospectively. King's College Hospital criteria (KCH), Child-Turcotte-Pugh (CTP) classification, and model for end-stage liver disease (MELD) score were calculated. Univariate analysis was performed, and then multivariate statistical adjustment for preoperative variables of ALF prognosis was performed. A new predictive model was developed, called the MELD conjugated serum phosphorus model (MELD-p). The individual diagnostic accuracy and cut-off value of models in predicting 3-month post-transplant mortality were evaluated using the area under the receiver operating characteristic curve (AUC). The difference in AUC between MELD-p and the other models was analyzed. The diagnostic improvement in MELD-p was assessed using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS The MELD-p and MELD scores had high predictive accuracy (AUC >0.9). KCH and serum phosphorus had an acceptable predictive ability (AUC >0.7). The CTP classification failed to show discriminative accuracy in predicting 3-month post-transplant mortality. The difference in AUC between MELD-p and the other models had statistically significant associations with CTP and KCH. The cut-off value of MELD-p was 3.98 for predicting 3-month post-transplant mortality. The NRI was 9.9% and the IDI was 2.9%. CONCLUSIONS MELD-p score can predict 3-month post-transplant mortality better than other scoring systems after LDLT due to ALF. The recommended cut-off value of MELD-p is 3.98.

21 CFR 868.1800 - Rhinoanemometer.

Code of Federal Regulations, 2010 CFR

2010-04-01

... DEVICES ANESTHESIOLOGY DEVICES Diagnostic Devices § 868.1800 Rhinoanemometer. (a) Identification. A... differential pressure across, a patient's nasal passages. (b) Classification. Class II (performance standards). ...

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Molecular diagnostics of inflammatory disease: New tools and perspectives.

PubMed

Garzorz-Stark, Natalie; Lauffer, Felix

2017-08-01

This essay reviews current approaches to establish novel molecular diagnostic tools for inflammatory skin diseases. Moreover, it highlights the importance of stratifying patients according to molecular signatures and revising current outdated disease classification systems to eventually reach the goal of personalized medicine. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

21 CFR 864.9225 - Cell-freezing apparatus and reagents for in vitro diagnostic use.

Code of Federal Regulations, 2012 CFR

2012-04-01

... use are devices used to freeze human red blood cells for in vitro diagnostic use. (b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in subpart E... Establishments That Manufacture Blood and Blood Products § 864.9225 Cell-freezing apparatus and reagents for in...

21 CFR 864.9225 - Cell-freezing apparatus and reagents for in vitro diagnostic use.

Code of Federal Regulations, 2014 CFR

2014-04-01

... use are devices used to freeze human red blood cells for in vitro diagnostic use. (b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in subpart E... Establishments That Manufacture Blood and Blood Products § 864.9225 Cell-freezing apparatus and reagents for in...

21 CFR 864.9225 - Cell-freezing apparatus and reagents for in vitro diagnostic use.

Code of Federal Regulations, 2013 CFR

2013-04-01

... use are devices used to freeze human red blood cells for in vitro diagnostic use. (b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in subpart E... Establishments That Manufacture Blood and Blood Products § 864.9225 Cell-freezing apparatus and reagents for in...

Overview and Analysis of the Behaviourist Criticism of the "Diagnostic and Statistical Manual of Mental Disorders (DSM)"

ERIC Educational Resources Information Center

Andersson, Gerhard; Ghaderi, Ata

2006-01-01

While a majority of cognitive behavioural researchers and clinicians adhere to the classification system provided in the "Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)," strong objections have been voiced among behaviourists who find the dichotomous allocation of patients into psychiatric diagnoses incompatible with the philosophy…

Social Communication Disorder outside Autism? A Diagnostic Classification Approach to Delineating Pragmatic Language Impairment, High Functioning Autism and Specific Language Impairment

ERIC Educational Resources Information Center

Gibson, Jenny; Adams, Catherine; Lockton, Elaine; Green, Jonathan

2013-01-01

Background: Developmental disorders of language and communication present considerable diagnostic challenges due to overlapping of symptomatology and uncertain aetiology. We aimed to further elucidate the behavioural and linguistic profile associated with impairments of social communication occurring outside of an autism diagnosis. Methods: Six to…

Profiles of Receptive and Expressive Language Abilities in Boys with Comorbid Fragile X Syndrome and Autism

ERIC Educational Resources Information Center

McDuffie, Andrea; Kover, Sara; Abbeduto, Leonard; Lewis, Pamela; Brown, Ted

2012-01-01

The authors examined receptive and expressive language profiles for a group of verbal male children and adolescents who had fragile X syndrome along with varying degrees of autism symptoms. A categorical approach for assigning autism diagnostic classification, based on the combined use of the Autism Diagnostic Interview--Revised and the Autism…

The Impact of Q-Matrix Designs on Diagnostic Classification Accuracy in the Presence of Attribute Hierarchies

ERIC Educational Resources Information Center

Liu, Ren; Huggins-Manley, Anne Corinne; Bradshaw, Laine

2017-01-01

There is an increasing demand for assessments that can provide more fine-grained information about examinees. In response to the demand, diagnostic measurement provides students with feedback on their strengths and weaknesses on specific skills by classifying them into mastery or nonmastery attribute categories. These attributes often form a…

Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

PubMed Central

Shamonin, Denis P.; Bron, Esther E.; Lelieveldt, Boudewijn P. F.; Smits, Marion; Klein, Stefan; Staring, Marius

2013-01-01

Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license. PMID:24474917

The ACTTION-American Pain Society Pain Taxonomy (AAPT): an evidence-based and multidimensional approach to classifying chronic pain conditions.

PubMed

Fillingim, Roger B; Bruehl, Stephen; Dworkin, Robert H; Dworkin, Samuel F; Loeser, John D; Turk, Dennis C; Widerstrom-Noga, Eva; Arnold, Lesley; Bennett, Robert; Edwards, Robert R; Freeman, Roy; Gewandter, Jennifer; Hertz, Sharon; Hochberg, Marc; Krane, Elliot; Mantyh, Patrick W; Markman, John; Neogi, Tuhina; Ohrbach, Richard; Paice, Judith A; Porreca, Frank; Rappaport, Bob A; Smith, Shannon M; Smith, Thomas J; Sullivan, Mark D; Verne, G Nicholas; Wasan, Ajay D; Wesselmann, Ursula

2014-03-01

Current approaches to classification of chronic pain conditions suffer from the absence of a systematically implemented and evidence-based taxonomy. Moreover, existing diagnostic approaches typically fail to incorporate available knowledge regarding the biopsychosocial mechanisms contributing to pain conditions. To address these gaps, the Analgesic, Anesthetic, and Addiction Clinical Trial Translations Innovations Opportunities and Networks (ACTTION) public-private partnership with the U.S. Food and Drug Administration and the American Pain Society (APS) have joined together to develop an evidence-based chronic pain classification system called the ACTTION-APS Pain Taxonomy. This paper describes the outcome of an ACTTION-APS consensus meeting, at which experts agreed on a structure for this new taxonomy of chronic pain conditions. Several major issues around which discussion revolved are presented and summarized, and the structure of the taxonomy is presented. ACTTION-APS Pain Taxonomy will include the following dimensions: 1) core diagnostic criteria; 2) common features; 3) common medical comorbidities; 4) neurobiological, psychosocial, and functional consequences; and 5) putative neurobiological and psychosocial mechanisms, risk factors, and protective factors. In coming months, expert working groups will apply this taxonomy to clusters of chronic pain conditions, thereby developing a set of diagnostic criteria that have been consistently and systematically implemented across nearly all common chronic pain conditions. It is anticipated that the availability of this evidence-based and mechanistic approach to pain classification will be of substantial benefit to chronic pain research and treatment. The ACTTION-APS Pain Taxonomy is an evidence-based chronic pain classification system designed to classify chronic pain along the following dimensions: 1) core diagnostic criteria; 2) common features; 3) common medical comorbidities; 4) neurobiological, psychosocial, and functional consequences; and 5) putative neurobiological and psychosocial mechanisms, risk factors, and protective factors. Copyright © 2014 American Pain Society. Published by Elsevier Inc. All rights reserved.

External Cervical Resorption: A Comparison of the Diagnostic Efficacy Using 2 Different Cone-beam Computed Tomographic Units and Periapical Radiographs.

PubMed

Vaz de Souza, Daniel; Schirru, Elia; Mannocci, Francesco; Foschi, Federico; Patel, Shanon

2017-01-01

The aim of this study was to compare the diagnostic efficacy of 2 cone-beam computed tomographic (CBCT) units with parallax periapical (PA) radiographs for the detection and classification of simulated external cervical resorption (ECR) lesions. Simulated ECR lesions were created on 13 mandibular teeth from 3 human dry mandibles. PA and CBCT scans were taken using 2 different units, Kodak CS9300 (Carestream Health Inc, Rochester, NY) and Morita 3D Accuitomo 80 (J Morita, Kyoto, Japan), before and after the creation of the ECR lesions. The lesions were then classified according to Heithersay's classification and their position on the root surface. Sensitivity, specificity, positive predictive values, negative predictive values, and receiver operator characteristic curves as well as the reproducibility of each technique were determined for diagnostic accuracy. The area under the receiver operating characteristic value for diagnostic accuracy for PA radiography and Kodak and Morita CBCT scanners was 0.872, 0.99, and 0.994, respectively. The sensitivity and specificity for both CBCT scanners were significantly better than PA radiography (P < .001). There was no statistical difference between the sensitivity and specificity of the 2 scanners. The percentage of correct diagnoses according to the tooth type was 87.4% for the Kodak scanner, 88.3% for the Morita scanner, and 48.5% for PA radiography.The ECR lesions were correctly identified according to the tooth surface in 87.8% Kodak, 89.1% Morita and 49.4% PA cases. The ECR lesions were correctly classified according to Heithersay classification in 70.5% of Kodak, 69.2% of Morita, and 39.7% of PA cases. This study revealed that both CBCT scanners tested were equally accurate in diagnosing ECR and significantly better than PA radiography. CBCT scans were more likely to correctly categorize ECR according to the Heithersay classification compared with parallax PA radiographs. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Malignancy rates and diagnostic performance of the Bosniak classification for the diagnosis of cystic renal lesions in computed tomography - a systematic review and meta-analysis.

PubMed

Sevcenco, Sabina; Spick, Claudio; Helbich, Thomas H; Heinz, Gertraud; Shariat, Shahrokh F; Klingler, Hans C; Rauchenwald, Michael; Baltzer, Pascal A

2017-06-01

To systematically review the literature on the Bosniak classification system in CT to determine its diagnostic performance to diagnose malignant cystic lesions and the prevalence of malignancy in Bosniak categories. A predefined database search was performed from 1 January 1986 to 18 January 2016. Two independent reviewers extracted data on malignancy rates in Bosniak categories and several covariates using predefined criteria. Study quality was assessed using QUADAS-2. Meta-analysis included data pooling, subgroup analyses, meta-regression and investigation of publication bias. A total of 35 studies, which included 2,578 lesions, were investigated. Data on observer experience, inter-observer variation and technical CT standards were insufficiently reported. The pooled rate of malignancy increased from Bosniak I (3.2 %, 95 % CI 0-6.8, I 2  = 5 %) to Bosniak II (6 %, 95 % CI 2.7-9.3, I 2  = 32 %), IIF (6.7 %, 95 % CI 5-8.4, I 2  = 0 %), III (55.1 %, 95 % CI 45.7-64.5, I 2  = 89 %) and IV (91 %, 95 % CI 87.7-94.2, I 2  = 36). Several study design-related influences on malignancy rates and subsequent diagnostic performance indices were identified. The Bosniak classification is an accurate tool with which to stratify the risk of malignancy in renal cystic lesions. • The Bosniak classification can accurately rule out malignancy. • Specificity remains moderate at 74 % (95 % CI 64-82). • Follow-up examinations should be considered in Bosniak IIF and Bosniak II cysts. • Data on the influence of reader experience and inter-reader variability are insufficient. • Technical CT standards and publication year did not influence diagnostic performance.

Status of Artist Upgrade

DTIC Science & Technology

1988-09-01

Autodrift, ARTIST Autoscaling , Electron Density 16. PRICE CODE Profiles 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY...FIGURES Figure No. Page 2.1 ARTIST Scaled Parameters 4 2.2 ARTIST ASCII Ionogram 6 2.3 ARTISTSV Optifont lonogram 7 2.4 Autoscaling of Es Trace Before...diagnostic programs for testing communication ports. The aforementioned contract required a performance evaluation of ARTIST . Manual and autoscaled

[Diagnostic algorithm in chronic myeloproliferative diseases (CMPD)].

PubMed

Haferlach, Torsten; Bacher, Ulrike; Kern, Wolfgang; Schnittger, Susanne; Haferlach, Claudia

2007-09-15

The Philadelphia-negative chronic myeloproliferative diseases (CMPD) are very complex and heterogeneous disorders. They are represented by polycythemia vera (PV), chronic idiopathic myelofibrosis (CIMF), essential thrombocythemia (ET), CMPD/unclassifiable (CMPD-U), chronic neutrophilic leukemia (CNL), and chronic eosinophilic leukemia/hypereosinophilic syndrome (CEL/HES) according to the WHO classification. Before, diagnostics were mainly focused on clinical and morphological aspects, but in recent years cytogenetics and fluorescence in situ hybridization (FISH) found entrance in routine schedules as chromosomal abnormalities are relevant for prognosis and classification. Recently, there is rapid progress in the field of molecular characterization: the JAK2V617F mutation which shows a high incidence in PV, CIMF, and ET already plays a central role and will probably soon be included in follow-up procedures. Due to the detection of mutations in exon 12 of the JAK2 gene or mutations in the MPL gene the variety of activating mutations in the CMPD is still increasing. In CEL/HES the detection of the FIP1L1-PDGFRA fusion gene and overexpression of PDGFRA and PDGFRB led to targeted therapy with tyrosine kinase inhibitors. Thus, diagnostics in the CMPD transform toward a multimodal diagnostic concept based on a combination of methods - cyto-/histomorphology, cytogenetics, and individual molecular methods which can be included in a diagnostic algorithm.

Refinement of diagnosis and disease classification in psychiatry.

PubMed

Lecrubier, Yves

2008-03-01

Knowledge concerning the classification of mental disorders progressed substantially with the use of DSM III-IV and IDCD 10 because it was based on observed data, with precise definitions. These classifications a priori avoided to generate definitions related to etiology or treatment response. They are based on a categorical approach where diagnostic entities share common phenomenological features. Modifications proposed or discussed are related to the weak validity of the classification strategy described above. (a) Disorders are supposed to be independent but the current coexistence of two or more disorders is the rule; (b) They also are supposed to have stability, however anxiety disorders most of the time precede major depression. For GAD age at onset, family history, biology and symptomatology are close to those of depression. As a consequence broader entities such as depression-GAD spectrum, panic-phobias spectrum and OCD spectrum including eating disorders and pathological gambling are taken into consideration; (c) Diagnostic categories use thresholds to delimitate a border with normals. This creates "subthreshold" conditions. The relevance of such conditions is well documented. Measuring the presence and severity of different dimensions, independent from a threshold, will improve the relevance of the description of patients pathology. In addition, this dimensional approach will improve the problems posed by the mutually exclusive diagnoses (depression and GAD, schizophrenia and depression); (d) Some disorders are based on the coexistence of different dimensions. Patients may present only one set of symptoms and have different characteristics, evolution and response to treatment. An example would be negative symptoms in Schizophrenia; (e) Because no etiological model is available and most measures are subjective, objective measures (cognitive, biological) and genetics progresses created important hopes. None of these measures is pathognomonic and most appear to be related to risk factors especially at certain periods when associated with environmental events. One of the major aims for a classification of patients is to identify groups to whom a best possible therapeutic strategy can be proposed. Drugs may improve fear extinction while the genetic and/or acquired avoidance may be called phobia. The basic mechanism and or the corresponding phenotype should appear in the classification. Progresses in early identification of disturbances by taking into account all the information available (prodromal symptoms, cognitive, biological, imaging, genetic, family information) are crucial for the future therapeutic strategy: prevention.

[Establishment of diagnostic model to monitor minimal residual disease of acute promyelocytic leukemia by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry].

PubMed

Zhang, Lin-lin; Xu, Zhi-fang; Tan, Yan-hong; Chen, Xiu-hua; Xu, Ai-ning; Ren, Fang-gang; Wang, Hong-wei

2013-01-01

To screen the potential protein biomarkers in minimal residual disease (MRD) of the acute promyelocytic leukemia (APL) by comparison of differentially expressed serum protein between APL patients at diagnosis and after complete remission (CR) and healthy controls, and to establish and verify a diagnostic model. Serum proteins from 36 cases of primary APL, 29 cases of APL during complete remission and 32 healthy controls were purified by magnetic beads and then analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The spectra were analyzed statistically using FlexAnalysis(TM) and ClinProt(TM) software. Two prediction model of primary APL/healthy control, primary APL/APL CR were developed. Thirty four statistically significant peptide peaks were obtained with the m/z value ranging from 1000 to 10 000 (P < 0.001) in primary APL/healthy control model. Seven statistically significant peptide peaks were obtained in primary APL/APL CR model (P < 0.001). Comparison of the protein profiles between the two models, three peptides with m/z 4642, 7764 and 9289 were considered as the protein biomarker of APL MRD. A diagnostic pattern for APL CR using m/z 4642 and 9289 was established. Blind validation yielded correct classification of 6 out of 8 cases. The MALDI-TOF MS analysis of APL patients serum protein can be used as a promising dynamic method for MRD detection and the two peptides with m/z 4642 and 9289 may be better biomarkers.

Classification of hand eczema.

PubMed

Agner, T; Aalto-Korte, K; Andersen, K E; Foti, C; Gimenéz-Arnau, A; Goncalo, M; Goossens, A; Le Coz, C; Diepgen, T L

2015-12-01

Classification of hand eczema (HE) is mandatory in epidemiological and clinical studies, and also important in clinical work. The aim was to test a recently proposed classification system of HE in clinical practice in a prospective multicentre study. Patients were recruited from nine different tertiary referral centres. All patients underwent examination by specialists in dermatology and were checked using relevant allergy testing. Patients were classified into one of the six diagnostic subgroups of HE: allergic contact dermatitis, irritant contact dermatitis, atopic HE, protein contact dermatitis/contact urticaria, hyperkeratotic endogenous eczema and vesicular endogenous eczema, respectively. An additional diagnosis was given if symptoms indicated that factors additional to the main diagnosis were of importance for the disease. Four hundred and twenty-seven patients were included, 379 (89%) of the patients could be classified directly into one of the six diagnostic subgroups, with irritant and allergic contact dermatitis comprising 249 patients (58%). For 32 (7%) more than one of the six diagnostic subgroups had been formulated as a main diagnosis, and 16 (4%) could not be classified. 38% had one additional diagnosis and 26% had two or more additional diagnoses. Eczema on feet was found in 30% of the patients, statistically significantly more frequently associated with hyperkeratotic and vesicular endogenous eczema. We find that the classification system investigated in the present study was useful, being able to give an appropriate main diagnosis for 89% of HE patients, and for another 7% when using two main diagnoses. The fact that more than half of the patients had one or more additional diagnoses illustrates that HE is a multifactorial disease. © 2015 European Academy of Dermatology and Venereology.

Development and Psychometric Evaluation of the Brief Adolescent Gambling Screen (BAGS)

PubMed Central

Stinchfield, Randy; Wynne, Harold; Wiebe, Jamie; Tremblay, Joel

2017-01-01

The purpose of this study was to develop and evaluate the initial reliability, validity and classification accuracy of a new brief screen for adolescent problem gambling. The three-item Brief Adolescent Gambling Screen (BAGS) was derived from the nine-item Gambling Problem Severity Subscale (GPSS) of the Canadian Adolescent Gambling Inventory (CAGI) using a secondary analysis of existing CAGI data. The sample of 105 adolescents included 49 females and 56 males from Canada who completed the CAGI, a self-administered measure of DSM-IV diagnostic criteria for Pathological Gambling, and a clinician-administered diagnostic interview including the DSM-IV diagnostic criteria for Pathological Gambling (both of which were adapted to yield DSM-5 Gambling Disorder diagnosis). A stepwise multivariate discriminant function analysis selected three GPSS items as the best predictors of a diagnosis of Gambling Disorder. The BAGS demonstrated satisfactory estimates of reliability, validity and classification accuracy and was equivalent to the nine-item GPSS of the CAGI and the BAGS was more accurate than the SOGS-RA. The BAGS estimates of classification accuracy include hit rate = 0.95, sensitivity = 0.88, specificity = 0.98, false positive rate = 0.02, and false negative rate = 0.12. Since these classification estimates are preliminary, derived from a relatively small sample size, and based upon the same sample from which the items were selected, it will be important to cross-validate the BAGS with larger and more diverse samples. The BAGS should be evaluated for use as a screening tool in both clinical and school settings as well as epidemiological surveys. PMID:29312064

CO2 exposure as translational cross-species experimental model for panic.

PubMed

Leibold, N K; van den Hove, D L A; Viechtbauer, W; Buchanan, G F; Goossens, L; Lange, I; Knuts, I; Lesch, K P; Steinbusch, H W M; Schruers, K R J

2016-09-06

The current diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders are being challenged by the heterogeneity and the symptom overlap of psychiatric disorders. Therefore, a framework toward a more etiology-based classification has been initiated by the US National Institute of Mental Health, the research domain criteria project. The basic neurobiology of human psychiatric disorders is often studied in rodent models. However, the differences in outcome measurements hamper the translation of knowledge. Here, we aimed to present a translational panic model by using the same stimulus and by quantitatively comparing the same outcome measurements in rodents, healthy human subjects and panic disorder patients within one large project. We measured the behavioral-emotional and bodily response to CO2 exposure in all three samples, allowing for a reliable cross-species comparison. We show that CO2 exposure causes a robust fear response in terms of behavior in mice and panic symptom ratings in healthy volunteers and panic disorder patients. To improve comparability, we next assessed the respiratory and cardiovascular response to CO2, demonstrating corresponding respiratory and cardiovascular effects across both species. This project bridges the gap between basic and human research to improve the translation of knowledge between these disciplines. This will allow significant progress in unraveling the etiological basis of panic disorder and will be highly beneficial for refining the diagnostic categories as well as treatment strategies.

CO2 exposure as translational cross-species experimental model for panic

PubMed Central

Leibold, N K; van den Hove, D L A; Viechtbauer, W; Buchanan, G F; Goossens, L; Lange, I; Knuts, I; Lesch, K P; Steinbusch, H W M; Schruers, K R J

2016-01-01

The current diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders are being challenged by the heterogeneity and the symptom overlap of psychiatric disorders. Therefore, a framework toward a more etiology-based classification has been initiated by the US National Institute of Mental Health, the research domain criteria project. The basic neurobiology of human psychiatric disorders is often studied in rodent models. However, the differences in outcome measurements hamper the translation of knowledge. Here, we aimed to present a translational panic model by using the same stimulus and by quantitatively comparing the same outcome measurements in rodents, healthy human subjects and panic disorder patients within one large project. We measured the behavioral–emotional and bodily response to CO2 exposure in all three samples, allowing for a reliable cross-species comparison. We show that CO2 exposure causes a robust fear response in terms of behavior in mice and panic symptom ratings in healthy volunteers and panic disorder patients. To improve comparability, we next assessed the respiratory and cardiovascular response to CO2, demonstrating corresponding respiratory and cardiovascular effects across both species. This project bridges the gap between basic and human research to improve the translation of knowledge between these disciplines. This will allow significant progress in unraveling the etiological basis of panic disorder and will be highly beneficial for refining the diagnostic categories as well as treatment strategies. PMID:27598969

Proposed morphologic classification of prostate cancer with neuroendocrine differentiation.

PubMed

Epstein, Jonathan I; Amin, Mahul B; Beltran, Himisha; Lotan, Tamara L; Mosquera, Juan-Miguel; Reuter, Victor E; Robinson, Brian D; Troncoso, Patricia; Rubin, Mark A

2014-06-01

On July 31, 2013, the Prostate Cancer Foundation assembled a working committee on the molecular biology and pathologic classification of neuroendocrine (NE) differentiation in prostate cancer. New clinical and molecular data emerging from prostate cancers treated by contemporary androgen deprivation therapies, as well as primary lesions, have highlighted the need for refinement of diagnostic terminology to encompass the full spectrum of NE differentiation. The classification system consists of: Usual prostate adenocarcinoma with NE differentiation; 2) Adenocarcinoma with Paneth cell NE differentiation; 3) Carcinoid tumor; 4) Small cell carcinoma; 5) Large cell NE carcinoma; and 5) Mixed NE carcinoma - acinar adenocarcinoma. The article also highlights "prostate carcinoma with overlapping features of small cell carcinoma and acinar adenocarcinoma" and "castrate-resistant prostate cancer with small cell cancer-like clinical presentation". It is envisioned that specific criteria associated with the refined diagnostic terminology will lead to clinically relevant pathologic diagnoses that will stimulate further clinical and molecular investigation and identification of appropriate targeted therapies.

[Joint endoprosthesis pathology. Histopathological diagnostics and classification].

PubMed

Krenn, V; Morawietz, L; Jakobs, M; Kienapfel, H; Ascherl, R; Bause, L; Kuhn, H; Matziolis, G; Skutek, M; Gehrke, T

2011-05-01

Prosthesis durability has steadily increased with high 10-year rates of 88-95%. However, four pathogenetic groups of diseases can decrease prosthesis durability: (1) periprosthetic wear particle disease (aseptic loosening) (2) bacterial infection (septic loosening) (3) periprosthetic ossification, and (4) arthrofibrosis. The histopathological "extended consensus classification of periprosthetic membranes" includes four types of membranes, arthrofibrosis, and osseous diseases of endoprosthetics: The four types of neosynovia are: wear particle-induced type (type I), mean prosthesis durability (MPD) in years 12.0; infectious type (type II), MPD 2.5; combined type (type III) MPD 4.2; and indeterminate type (type IV), MPD 5.5. Arthrofibrosis can be determined in three grades: grade 1 needs clinical information to be differentiated from a type IV membrane, and grades 2 & 3 can be diagnosed histopathologically. Periprosthetic ossification, osteopenia-induced fractures, and aseptic osteonecrosis can be histopathologically diagnosed safely with clinical information. The extended consensus classification of periprosthetic membranes may be a diagnostic groundwork for a future national endoprosthesis register.

[Evaluation of Gastric Atrophy. Comparison between Sidney and OLGA Systems].

PubMed

Ramírez-Mendoza, Pablo; González-Angulo, Jorge; Angeles-Garay, Ulises; Segovia-Cueva, Gustavo Adolfo

2008-01-01

histopathologic identification of atrophy and metaplasia is decisive to stop the way of gastritis?carcinoma in patients with chronic gastritis. to compare diagnostic concordance between Sidney system and the operative Link on Gastritis Assessment (OLGA) system. 120 consecutive biopsies were analyzed by general pathologists according to the Sidney system. All of them were evaluated by a second pathologist who used OLGA System. We employed kappa index to evaluate diagnostic concordance between the classifications. the clinical picture includes dyspepsia (94 %), abdominal pain (50 %), gastroesophageal reflux (30 %), bleed of the upper digestive system (24 %), and presence of Helicobacter pylori (47.5 %). Four were diagnosed as atrophy by Sidney system and 26 cases with atrophy by OLGA system. The concordance between two classifications systems was too low (p = 0.05). the atrophy diagnosis, between systems, had low concordance. The description of metaplastic atrophy in the OLGA system represents the only one difference. The non-metaplastic atrophy is the same for both classifications. Therefore, the general pathologist should include this evaluation more consistently using OLGA system.

Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches.

PubMed

Çelik, Ufuk; Yurtay, Nilüfer; Koç, Emine Rabia; Tepe, Nermin; Güllüoğlu, Halil; Ertaş, Mustafa

2015-01-01

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.

Exclusion of overlapping symptoms in DSM-5 mixed features specifier: heuristic diagnostic and treatment implications.

PubMed

Malhi, Gin S; Byrow, Yulisha; Outhred, Tim; Fritz, Kristina

2017-04-01

This article focuses on the controversial decision to exclude the overlapping symptoms of distractibility, irritability, and psychomotor agitation (DIP) with the introduction of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) mixed features specifier. In order to understand the placement of mixed states within the current classification system, we first review the evolution of mixed states. Then, using Kraepelin's original classification of mixed states, we compare and contrast his conceptualization with modern day definitions. The DSM-5 workgroup excluded DIP symptoms, arguing that they lack the ability to differentiate between manic and depressive states; however, accumulating evidence suggests that DIP symptoms may be core features of mixed states. We suggest a return to a Kraepelinian approach to classification-with mood, ideation, and activity as key axes-and reintegration of DIP symptoms as features that are expressed across presentations. An inclusive definition of mixed states is urgently needed to resolve confusion in clinical practice and to redirect future research efforts.

Where can pixel counting area estimates meet user-defined accuracy requirements?

NASA Astrophysics Data System (ADS)

Waldner, François; Defourny, Pierre

2017-08-01

Pixel counting is probably the most popular way to estimate class areas from satellite-derived maps. It involves determining the number of pixels allocated to a specific thematic class and multiplying it by the pixel area. In the presence of asymmetric classification errors, the pixel counting estimator is biased. The overarching objective of this article is to define the applicability conditions of pixel counting so that the estimates are below a user-defined accuracy target. By reasoning in terms of landscape fragmentation and spatial resolution, the proposed framework decouples the resolution bias and the classifier bias from the overall classification bias. The consequence is that prior to any classification, part of the tolerated bias is already committed due to the choice of the spatial resolution of the imagery. How much classification bias is affordable depends on the joint interaction of spatial resolution and fragmentation. The method was implemented over South Africa for cropland mapping, demonstrating its operational applicability. Particular attention was paid to modeling a realistic sensor's spatial response by explicitly accounting for the effect of its point spread function. The diagnostic capabilities offered by this framework have multiple potential domains of application such as guiding users in their choice of imagery and providing guidelines for space agencies to elaborate the design specifications of future instruments.

Inclusion of gaming disorder in the diagnostic classifications and promotion of public health response.

PubMed

Shadloo, Behrang; Farnam, Rabert; Amin-Esmaeili, Masoumeh; Hamzehzadeh, Marziyeh; Rafiemanesh, Hosein; Jobehdar, Maral Mardaneh; Ghani, Kamyar; Charkhgard, Nader; Rahimi-Movaghar, Afarin

2017-09-01

There are ongoing controversies regarding the upcoming ICD-11 concept of gaming disorder. Recently, Aarseth et al. have put this diagnostic entity into scrutiny. Although we, a group of Iranian researchers and clinicians, acknowledge some of Aarseth et al.'s concerns, believe that the inclusion of gaming disorder in the upcoming ICD-11 would facilitate necessary steps to raise public awareness, enhance development of proper diagnostic approaches and treatment interventions, and improve health and non-health policies.

Inclusion of gaming disorder in the diagnostic classifications and promotion of public health response

PubMed Central

Shadloo, Behrang; Farnam, Rabert; Amin-Esmaeili, Masoumeh; Hamzehzadeh, Marziyeh; Rafiemanesh, Hosein; Jobehdar, Maral Mardaneh; Ghani, Kamyar; Charkhgard, Nader; Rahimi-Movaghar, Afarin

2017-01-01

There are ongoing controversies regarding the upcoming ICD-11 concept of gaming disorder. Recently, Aarseth et al. have put this diagnostic entity into scrutiny. Although we, a group of Iranian researchers and clinicians, acknowledge some of Aarseth et al.’s concerns, believe that the inclusion of gaming disorder in the upcoming ICD-11 would facilitate necessary steps to raise public awareness, enhance development of proper diagnostic approaches and treatment interventions, and improve health and non-health policies. PMID:28816499

Raman biophysical markers in skin cancer diagnosis.

PubMed

Feng, Xu; Moy, Austin J; Nguyen, Hieu T M; Zhang, Yao; Zhang, Jason; Fox, Matthew C; Sebastian, Katherine R; Reichenberg, Jason S; Markey, Mia K; Tunnell, James W

2018-05-01

Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

The Value of Ensari's Proposal in Evaluating the Mucosal Pathology of Childhood Celiac Disease: Old Classification versus New Version.

PubMed

Güreşci, Servet; Hızlı, Samil; Simşek, Gülçin Güler

2012-09-01

Small intestinal biopsy remains the gold standard in diagnosing celiac disease (CD); however, the wide spectrum of histopathological states and differential diagnosis of CD is still a diagnostic problem for pathologists. Recently, Ensari reviewed the literature and proposed an update of the histopathological diagnosis and classification for CD. In this study, the histopathological materials of 54 children in whom CD was diagnosed at our hospital were reviewed to compare the previous Marsh and Modified Marsh-Oberhuber classifications with this new proposal. In this study, we show that the Ensari classification is as accurate as the Marsh and Modified Marsh classifications in describing the consecutive states of mucosal damage seen in CD. Ensari's classification is simple, practical and facilitative in diagnosing and subtyping of mucosal pathology of CD.

Cognitive diagnosis modelling incorporating item response times.

PubMed

Zhan, Peida; Jiao, Hong; Liao, Dandan

2018-05-01

To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy 'and' gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. The PISA 2012 computer-based mathematics data were analysed first. These real data estimates were treated as true values in a subsequent simulation study. A follow-up simulation study with ideal testing conditions was conducted as well to further evaluate model parameter recovery. The results indicated that model parameters could be well recovered using the MCMC approach. Further, incorporating RTs into the DINA model would improve attribute and profile correct classification rates and result in more accurate and precise estimation of the model parameters. © 2017 The British Psychological Society.

Diagnostic depressive symptoms of the mixed bipolar episode.

PubMed

Cassidy, F; Ahearn, E; Murry, E; Forest, K; Carroll, B J

2000-03-01

There is not yet consensus on the best diagnostic definition of mixed bipolar episodes. Many have suggested the DSM-III-R/-IV definition is too rigid. We propose alternative criteria using data from a large patient cohort. We evaluated 237 manic in-patients using DSM-III-R criteria and the Scale for Manic States (SMS). A bimodally distributed factor of dysphoric mood has been reported from the SMS data. We used both the factor and the DSM-III-R classifications to identify candidate depressive symptoms and then developed three candidate depressive symptom sets. Using ROC analysis we determined the optimal threshold number of symptoms in each set and compared the three ROC solutions. The optimal solution was tested against the DSM-III-R classification for crossvalidation. The optimal ROC solution was a set, derived from both the DSM-III-R and the SMS, and the optimal threshold for diagnosis was two or more symptoms. Applying this set iteratively to the DSM-III-R classification produced the identical ROC solution. The prevalence of mixed episodes in the cohort was 13.9% by DSM-III-R, 20.2% by the dysphoria factor and 27.4% by the new ROC solution. A diagnostic set of six dysphoric symptoms (depressed mood, anhedonia, guilt, suicide, fatigue and anxiety), with a threshold of two symptoms, is proposed for a mixed episode. This new definition has a foundation in clinical data, in the proved diagnostic performance of the qualifying symptoms, and in ROC validation against two previous definitions that each have face validity.

[The functional state classification and evaluation of the stability level in mental loads based on the factor structure of heart rate variability parameters].

PubMed

Mashin, V A; Mashina, M N

2004-12-01

In the paper, outcomes of the researches devoted to factor analysis of heart rate variability parameters and definition of the most informative parameters for diagnostics of functional states and an evaluation of level of stability to mental loads, are presented. The factor structure of parameters, which unclude integral level of heart rate variability (1), balance between activity of vagus and brain cortical-limbic systems (2), integrated level of cardiovascular system functioning (3), is substantiated. Factor analysis outcomes have been used for construction of functional state classification, for their differential diagnostics, and for development and check of algorithm for evaluation of the stability level in mental loads.

Behavioral addictions in addiction medicine: from mechanisms to practical considerations.

PubMed

Banz, Barbara C; Yip, Sarah W; Yau, Yvonne H C; Potenza, Marc N

2016-01-01

Recent progress has been made in our understanding of nonsubstance or "behavioral" addictions, although these conditions and their most appropriate classification remain debated and the knowledge basis for understanding the pathophysiology of and treatments for these conditions includes important gaps. Recent developments include the classification of gambling disorder as a "Substance-Related and Addictive Disorder" in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and proposed diagnostic criteria for Internet Gaming Disorder in Section 3 of DSM-5. This chapter reviews current neuroscientific understandings of behavioral addictions and the potential of neurobiological data to assist in the development of improved policy, prevention, and treatment efforts. © 2016 Elsevier B.V. All rights reserved.

Comprehensive evaluation of functional and anatomical disorders of the patients with distal occlusion and accompanying obstructive sleep apnea syndrome

NASA Astrophysics Data System (ADS)

Nabiev, F. H.; Dobrodeev, A. S.; Libin, P. V.; Kotov, I. I.; Ovsyannikov, A. G.

2015-11-01

The paper defines the therapeutic and rehabilitation approach to the patients with Angle's classification Class II dento-facial anomalies, accompanied by obstructive sleep apnea (OSA). The proposed comprehensive approach to the diagnostics and treatment of patients with posterior occlusion, accompanied by OSA, allows for objective evaluation of intensity of a dento-facial anomaly and accompanying respiratory disorders in the nasal and oral pharynx, which allows for the pathophysiological mechanisms of OSA to be identified, and an optimal plan for surgical procedures to be developed. The proposed comprehensive approach to the diagnostics and treatment of patients with Angle's classification Class II dento-facial anomalies provides high functional and aesthetic results.

Expanding the definition of addiction: DSM-5 vs. ICD-11.

PubMed

Grant, Jon E; Chamberlain, Samuel R

2016-08-01

While considerable efforts have been made to understand the neurobiological basis of substance addiction, the potentially "addictive" qualities of repetitive behaviors, and whether such behaviors constitute "behavioral addictions," is relatively neglected. It has been suggested that some conditions, such as gambling disorder, compulsive stealing, compulsive buying, compulsive sexual behavior, and problem Internet use, have phenomenological and neurobiological parallels with substance use disorders. This review considers how the issue of "behavioral addictions" has been handled by latest revisions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), leading to somewhat divergent approaches. We also consider key areas for future research in order to address optimal diagnostic classification and treatments for such repetitive, debilitating behaviors.

A theory of fine structure image models with an application to detection and classification of dementia

PubMed Central

Penn, Richard; Werner, Michael; Thomas, Justin

2015-01-01

Background Estimation of stochastic process models from data is a common application of time series analysis methods. Such system identification processes are often cast as hypothesis testing exercises whose intent is to estimate model parameters and test them for statistical significance. Ordinary least squares (OLS) regression and the Levenberg-Marquardt algorithm (LMA) have proven invaluable computational tools for models being described by non-homogeneous, linear, stationary, ordinary differential equations. Methods In this paper we extend stochastic model identification to linear, stationary, partial differential equations in two independent variables (2D) and show that OLS and LMA apply equally well to these systems. The method employs an original nonparametric statistic as a test for the significance of estimated parameters. Results We show gray scale and color images are special cases of 2D systems satisfying a particular autoregressive partial difference equation which estimates an analogous partial differential equation. Several applications to medical image modeling and classification illustrate the method by correctly classifying demented and normal OLS models of axial magnetic resonance brain scans according to subject Mini Mental State Exam (MMSE) scores. Comparison with 13 image classifiers from the literature indicates our classifier is at least 14 times faster than any of them and has a classification accuracy better than all but one. Conclusions Our modeling method applies to any linear, stationary, partial differential equation and the method is readily extended to 3D whole-organ systems. Further, in addition to being a robust image classifier, estimated image models offer insights into which parameters carry the most diagnostic image information and thereby suggest finer divisions could be made within a class. Image models can be estimated in milliseconds which translate to whole-organ models in seconds; such runtimes could make real-time medicine and surgery modeling possible. PMID:26029638

Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

NASA Astrophysics Data System (ADS)

Lenhardt, L.; Zeković, I.; Dramićanin, T.; Dramićanin, M. D.

2013-11-01

Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

DNA methylation-based classification of central nervous system tumours.

PubMed

Capper, David; Jones, David T W; Sill, Martin; Hovestadt, Volker; Schrimpf, Daniel; Sturm, Dominik; Koelsche, Christian; Sahm, Felix; Chavez, Lukas; Reuss, David E; Kratz, Annekathrin; Wefers, Annika K; Huang, Kristin; Pajtler, Kristian W; Schweizer, Leonille; Stichel, Damian; Olar, Adriana; Engel, Nils W; Lindenberg, Kerstin; Harter, Patrick N; Braczynski, Anne K; Plate, Karl H; Dohmen, Hildegard; Garvalov, Boyan K; Coras, Roland; Hölsken, Annett; Hewer, Ekkehard; Bewerunge-Hudler, Melanie; Schick, Matthias; Fischer, Roger; Beschorner, Rudi; Schittenhelm, Jens; Staszewski, Ori; Wani, Khalida; Varlet, Pascale; Pages, Melanie; Temming, Petra; Lohmann, Dietmar; Selt, Florian; Witt, Hendrik; Milde, Till; Witt, Olaf; Aronica, Eleonora; Giangaspero, Felice; Rushing, Elisabeth; Scheurlen, Wolfram; Geisenberger, Christoph; Rodriguez, Fausto J; Becker, Albert; Preusser, Matthias; Haberler, Christine; Bjerkvig, Rolf; Cryan, Jane; Farrell, Michael; Deckert, Martina; Hench, Jürgen; Frank, Stephan; Serrano, Jonathan; Kannan, Kasthuri; Tsirigos, Aristotelis; Brück, Wolfgang; Hofer, Silvia; Brehmer, Stefanie; Seiz-Rosenhagen, Marcel; Hänggi, Daniel; Hans, Volkmar; Rozsnoki, Stephanie; Hansford, Jordan R; Kohlhof, Patricia; Kristensen, Bjarne W; Lechner, Matt; Lopes, Beatriz; Mawrin, Christian; Ketter, Ralf; Kulozik, Andreas; Khatib, Ziad; Heppner, Frank; Koch, Arend; Jouvet, Anne; Keohane, Catherine; Mühleisen, Helmut; Mueller, Wolf; Pohl, Ute; Prinz, Marco; Benner, Axel; Zapatka, Marc; Gottardo, Nicholas G; Driever, Pablo Hernáiz; Kramm, Christof M; Müller, Hermann L; Rutkowski, Stefan; von Hoff, Katja; Frühwald, Michael C; Gnekow, Astrid; Fleischhack, Gudrun; Tippelt, Stephan; Calaminus, Gabriele; Monoranu, Camelia-Maria; Perry, Arie; Jones, Chris; Jacques, Thomas S; Radlwimmer, Bernhard; Gessi, Marco; Pietsch, Torsten; Schramm, Johannes; Schackert, Gabriele; Westphal, Manfred; Reifenberger, Guido; Wesseling, Pieter; Weller, Michael; Collins, Vincent Peter; Blümcke, Ingmar; Bendszus, Martin; Debus, Jürgen; Huang, Annie; Jabado, Nada; Northcott, Paul A; Paulus, Werner; Gajjar, Amar; Robinson, Giles W; Taylor, Michael D; Jaunmuktane, Zane; Ryzhova, Marina; Platten, Michael; Unterberg, Andreas; Wick, Wolfgang; Karajannis, Matthias A; Mittelbronn, Michel; Acker, Till; Hartmann, Christian; Aldape, Kenneth; Schüller, Ulrich; Buslei, Rolf; Lichter, Peter; Kool, Marcel; Herold-Mende, Christel; Ellison, David W; Hasselblatt, Martin; Snuderl, Matija; Brandner, Sebastian; Korshunov, Andrey; von Deimling, Andreas; Pfister, Stefan M

2018-03-22

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

Autism according to diagnostic and statistical manual of mental disorders 5(th) edition: The need for further improvements.

PubMed

Posar, Annio; Resca, Federica; Visconti, Paola

2015-01-01

The fifth edition of the diagnostic and statistical manual of mental disorders (DSM-5) introduced significant changes in the classification of autism spectrum disorders (ASD), including the abolition of the diagnostic subcategories proposed by DSM-IV-Text Revision. DSM-5 describes three levels of increasing severity of ASD. The authors report two explanatory cases with ASD (verbal boys, aged about 7 and a half years, without intellectual disability). According to DSM-5, both cases fall into the lowest severity level of ASD. However, their neuropsychological and neurobehavioral profile varies significantly. While the first boy showed a prevalent impairment of visuoconstructional and visuoperceptual abilities, the second one presented a predominant involvement of verbal functions, with qualitative impairments in communication. A further step forward in the definition and classification of ASD, taking into account both intensity and quality of symptoms, is recommended in order to formulate a reliable prognosis, plan an individualized treatment and monitor the clinical course over time.

The Mouse Model of Pancreatic Cancer Atlas (MMPCA) for classification of pancreatic cancer lesions: A large histological investigation of the Ptf1aCre/+;LSL-KrasG12D/+ transgenic mouse model of pancreatic cancer

PubMed Central

Veite-Schmahl, Michelle J.; Rivers, Adam C.; Regan, Daniel P.

2017-01-01

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading forms of cancer related deaths in the United States. With limited treatment options and unreliable diagnostic methods, long-term survival rates following a diagnosis of pancreatic cancer remain poor. Pancreatic intraepithelial neoplasia (PanIN) are precancerous lesions that precede progression towards PDAC. PanIN occur in increasing complexity as the disease progresses and the description of PanIN plays a critical role in describing, staging and diagnosing PDAC. Inconsistencies in PanIN classifications exist even amongst leading pathologists. This has led to debate and confusion among researchers and pathologists involved in pancreatic cancer research, diagnosis and treatment. We have sought to initiate a discussion with leading pathologists with a goal of increasing consensus in the interpretation of PanIN and associated structures within the precancerous pancreas. Toward achieving this goal, we are in the process of conducting an extensive study of over 1000 male and female pancreata in varying stages of PanIN progression isolated from the Ptf1aCre/+;LSL-KrasG12D/+ transgenic mouse model of pancreatic cancer. Using this extensive database, we have established the Mouse Model of Pancreatic Cancer Atlas (MMPCA) to serve as a platform for meaningful and interactive discussion among researchers and pathologists who study pancreatic disease. We hope that the MMPCA will be an effective tool for promoting a more consistent and accurate consensus of PanIN classifications in the future. PMID:29121082

The Diagnosticity of Color for Emotional Objects

PubMed Central

McMenamin, Brenton W.; Radue, Jasmine; Trask, Joanna; Huskamp, Kristin; Kersten, Daniel; Marsolek, Chad J.

2012-01-01

Object classification can be facilitated if simple diagnostic features can be used to determine class membership. Previous studies have found that simple shapes may be diagnostic for emotional content and automatically alter the allocation of visual attention. In the present study, we analyzed whether color is diagnostic of emotional content and tested whether emotionally diagnostic hues alter the allocation of visual attention. Reddish-yellow hues are more common in (i.e., diagnostic of) emotional images, particularly images with positive emotional content. An exogenous cueing paradigm was employed to test whether these diagnostic hues orient attention differently from other hues due to the emotional diagnosticity. In two experiments, we found that participants allocated attention differently to diagnostic hues than to non-diagnostic hues, in a pattern indicating a broadening of spatial attention when cued with diagnostic hues. Moreover, the attentional broadening effect was predicted by self-reported measures of affective style, linking the behavioral effect to emotional processes. These results confirm the existence and use of diagnostic features for the rapid detection of emotional content. PMID:24659831

Integrating molecular markers into the World Health Organization classification of CNS tumors: a survey of the neuro-oncology community.

PubMed

Aldape, Kenneth; Nejad, Romina; Louis, David N; Zadeh, Gelareh

2017-03-01

Molecular markers provide important biological and clinical information related to the classification of brain tumors, and the integration of relevant molecular parameters into brain tumor classification systems has been a widely discussed topic in neuro-oncology over the past decade. With recent advances in the development of clinically relevant molecular signatures and the 2016 World Health Organization (WHO) update, the views of the neuro-oncology community on such changes would be informative for implementing this process. A survey with 8 questions regarding molecular markers in tumor classification was sent to an email list of Society for Neuro-Oncology members and attendees of prior meetings (n=5065). There were 403 respondents. Analysis was performed using whole group response, based on self-reported subspecialty. The survey results show overall strong support for incorporating molecular knowledge into the classification and clinical management of brain tumors. Across all 7 subspecialty groups, ≥70% of respondents agreed to this integration. Interestingly, some variability is seen among subspecialties, notably with lowest support from neuropathologists, which may reflect their roles in implementing such diagnostic technologies. Based on a survey provided to the neuro-oncology community, we report strong support for the integration of molecular markers into the WHO classification of brain tumors, as well as for using an integrated "layered" diagnostic format. While membership from each specialty showed support, there was variation by specialty in enthusiasm regarding proposed changes. The initial results of this survey influenced the deliberations underlying the 2016 WHO classification of tumors of the central nervous system. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

Unique and Overlapping Symptoms in Schizophrenia Spectrum and Dissociative Disorders in Relation to Models of Psychopathology: A Systematic Review

PubMed Central

Renard, Selwyn B.; Huntjens, Rafaele J. C.; Lysaker, Paul H.; Moskowitz, Andrew; Aleman, André; Pijnenborg, Gerdina H. M.

2017-01-01

Schizophrenia spectrum disorders (SSDs) and dissociative disorders (DDs) are described in the fifth edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) and tenth edition of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) as 2 categorically distinct diagnostic categories. However, several studies indicate high levels of co-occurrence between these diagnostic groups, which might be explained by overlapping symptoms. The aim of this systematic review is to provide a comprehensive overview of the research concerning overlap and differences in symptoms between schizophrenia spectrum and DDs. For this purpose the PubMed, PsycINFO, and Web of Science databases were searched for relevant literature. The literature contained a large body of evidence showing the presence of symptoms of dissociation in SSDs. Although there are quantitative differences between diagnoses, overlapping symptoms are not limited to certain domains of dissociation, nor to nonpathological forms of dissociation. In addition, dissociation seems to be related to a history of trauma in SSDs, as is also seen in DDs. There is also evidence showing that positive and negative symptoms typically associated with schizophrenia may be present in DD. Implications of these results are discussed with regard to different models of psychopathology and clinical practice. PMID:27209638

Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer

PubMed Central

2015-01-01

Background microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies. Methods In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) data depositories. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer. Results Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis. Conclusions Our prediction models have strong potential for the diagnosis of pancreatic cancer. PMID:26328610

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

An international comparative family medicine study of the Transition Project data from the Netherlands, Malta and Serbia. Is family medicine an international discipline? Comparing diagnostic odds ratios across populations.

PubMed

Soler, Jean K; Okkes, Inge; Oskam, Sibo; van Boven, Kees; Zivotic, Predrag; Jevtic, Milan; Dobbs, Frank; Lamberts, Henk

2012-06-01

This is an international study of the epidemiology of family medicine (FM) in three practice populations from the Netherlands, Malta and Serbia. Diagnostic associations between common reasons for encounter (RfEs) and episodes titles are compared and similarities and differences are described and analysed. Participating family doctors (FDs) recorded details of all their patient contacts in an 'episode of care (EoC)' structure using the International Classification of Primary Care (ICPC). RfEs presented by the patient and episode titles (diagnostic labels of EoCs) were classified with ICPC. The relationships between RfEs and episode titles were studied with Bayesian methods. Distributions of diagnostic odds ratios (ORs) from the three population databases are presented and compared. ICPC, the RfE and the EoC data model are appropriate tools to study the process of diagnosis in FM. Distributions of diagnostic associations between RfEs and episode titles in the Transition Project international populations show remarkable similarities and congruencies in the process of diagnosis from both the RfE and the episode title perspectives. The congruence of diagnostic associations between populations supports the use of such data from one population to inform diagnostic decisions in another. Differences in the magnitude of such diagnostic associations are significant, and population-specific data are therefore desirable. We propose that both an international (common) and a local (health care system specific) content of FM exist and that the empirical distributions of diagnostic associations presented in this paper are a reflection of both these effects. We also observed that the frequency of exposure to such diagnostic challenges had a strong effect on the confidence intervals of diagnostic ORs reflecting these diagnostic associations. We propose that this constitutes evidence that expertise in FM is associated with frequency of exposure to diagnostic challenges.

Diagnostic Exercise: Neurologic Disorder in a Cat

DTIC Science & Technology

1989-12-21

IWORK UNIT ELEMENT NO. NO. NO. ACCESSION NO. 11. TITLE (Include Security Classification) Diagnostic Exercise - Neurologic Disorder in a Cat 12...and identify by block number) This report documents the fifth reported occurrance of cerebral phaeophyphomycosis in cats . Because mycotic...Exercise: Neurologic Disorder in a Cat Ronald C. Bell United States Army Medical Research Institute of Infectious Diseases (USAMRIID), Fort Detrick

Utility of the 3Di Short Version for the Diagnostic Assessment of Autism Spectrum Disorder and Compatibility with DSM-5

ERIC Educational Resources Information Center

Slappendel, Geerte; Mandy, William; van der Ende, Jan; Verhulst, Frank C.; van der Sijde, Ad; Duvekot, Jorieke; Skuse, David; Greaves-Lord, Kirstin

2016-01-01

The Developmental Diagnostic Dimensional Interview-short version (3Di-sv) provides a brief standardized parental interview for diagnosing autism spectrum disorder (ASD). This study explored its validity, and compatibility with DSM-5 ASD. 3Di-sv classifications showed good sensitivity but low specificity when compared to ADOS-2-confirmed clinical…

Clinician's Primer to ICD-10-CM Coding for Cleft Lip/Palate Care.

PubMed

Allori, Alexander C; Cragan, Janet D; Della Porta, Gina C; Mulliken, John B; Meara, John G; Bruun, Richard; Shusterman, Stephen; Cassell, Cynthia H; Raynor, Eileen; Santiago, Pedro; Marcus, Jeffrey R

2017-01-01

On October 1, 2015, the United States required use of the Clinical Modification of the International Classification of Diseases, 10th Revision (ICD-10-CM) for diagnostic coding. This primer was written to assist the cleft care community with understanding and use of ICD-10-CM for diagnostic coding related to cleft lip and/or palate (CL/P).

Implications of "DSM"-IV to "DSM"-5 Substance Use Disorder Diagnostic Changes in Adolescents Enrolled in a School-Based Intervention

ERIC Educational Resources Information Center

Stewart, David G.; Arlt, Virginia K.; Siebert, Erin C.; Chapman, Meredith K.; Hu, Emily M.

2016-01-01

This study aimed to examine (a) the impact of the change in the "Diagnostic and Statistical Manual of Mental Disorders" ("DSM") from a categorical to dimensional classification of substance use diagnoses, (b) the elimination of the legal criterion, and (c) the inclusion of a craving criterion in the "DSM"-5.…

Auditory Processing Disorder in Relation to Developmental Disorders of Language, Communication and Attention: A Review and Critique

ERIC Educational Resources Information Center

Dawes, Piers; Bishop, Dorothy

2009-01-01

Background: Auditory Processing Disorder (APD) does not feature in mainstream diagnostic classifications such as the "Diagnostic and Statistical Manual of Mental Disorders, 4th Edition" (DSM-IV), but is frequently diagnosed in the United States, Australia and New Zealand, and is becoming more frequently diagnosed in the United Kingdom. Aims: To…

Rolling element bearings diagnostics using the Symbolic Aggregate approXimation

NASA Astrophysics Data System (ADS)

Georgoulas, George; Karvelis, Petros; Loutas, Theodoros; Stylios, Chrysostomos D.

2015-08-01

Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults.

Integrating normal and abnormal personality structure: a proposal for DSM-V.

PubMed

Widiger, Thomas A

2011-06-01

The personality disorders section of the American Psychiatric Association's fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) is currently being developed. The purpose of the current paper is to encourage the authors of DSM-V to integrate normal and abnormal personality structure within a common, integrative model, and to suggest that the optimal choice for such an integration would be the five-factor model (FFM) of general personality structure. A proposal for the classification of personality disorder from the perspective of the FFM is provided. Discussed as well are implications and issues associated with an FFM of personality disorder, including validity, coverage, feasibility, clinical utility, and treatment implications.

Automated particle identification through regression analysis of size, shape and colour

NASA Astrophysics Data System (ADS)

Rodriguez Luna, J. C.; Cooper, J. M.; Neale, S. L.

2016-04-01

Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to "predict" with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own.

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed

K, Jalal Deen; R, Ganesan; A, Merline

2017-07-27

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed Central

K, Jalal Deen; R, Ganesan; A, Merline

2017-01-01

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127

Raman Spectroscopy of Experimental Oral Carcinogenesis: Study on Sequential Cancer Progression in Hamster Buccal Pouch Model.

PubMed

Kumar, Piyush; Bhattacharjee, Tanmoy; Ingle, Arvind; Maru, Girish; Krishna, C Murali

2016-10-01

Oral cancers suffer from poor 5-year survival rates, owing to late detection of the disease. Current diagnostic/screening tools need to be upgraded in view of disadvantages like invasiveness, tedious sample preparation, long output times, and interobserver variances. Raman spectroscopy has been shown to identify many disease conditions, including oral cancers, from healthy conditions. Further studies in exploring sequential changes in oral carcinogenesis are warranted. In this Raman spectroscopy study, sequential progression in experimental oral carcinogenesis in Hamster buccal pouch model was investigated using 3 approaches-ex vivo, in vivo sequential, and in vivo follow-up. In all these studies, spectral changes show lipid dominance in early stages while later stages and tumors showed increased protein to lipid ratio and nucleic acids. On similar lines, early weeks of 7,12-dimethylbenz(a)anthracene-treated and control groups showed higher overlap and low classification. The classification efficiency increased progressively, reached a plateau phase and subsequently increased up to 100% by 14 weeks. The misclassifications between treated and control spectra suggested some changes in controls as well, which was confirmed by a careful reexamination of histopathological slides. These findings suggests Raman spectroscopy may be able to identify microheterogeneity, which may often go unnoticed in conventional biochemistry wherein tissue extracts are employed, as well as in histopathology. In vivo findings, quite comparable to gold-standard supported ex vivo findings, give further proof of Raman spectroscopy being a promising label-free, noninvasive diagnostic adjunct for future clinical applications. © The Author(s) 2015.

The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies.

PubMed

Kotov, Roman; Krueger, Robert F; Watson, David; Achenbach, Thomas M; Althoff, Robert R; Bagby, R Michael; Brown, Timothy A; Carpenter, William T; Caspi, Avshalom; Clark, Lee Anna; Eaton, Nicholas R; Forbes, Miriam K; Forbush, Kelsie T; Goldberg, David; Hasin, Deborah; Hyman, Steven E; Ivanova, Masha Y; Lynam, Donald R; Markon, Kristian; Miller, Joshua D; Moffitt, Terrie E; Morey, Leslie C; Mullins-Sweatt, Stephanie N; Ormel, Johan; Patrick, Christopher J; Regier, Darrel A; Rescorla, Leslie; Ruggero, Camilo J; Samuel, Douglas B; Sellbom, Martin; Simms, Leonard J; Skodol, Andrew E; Slade, Tim; South, Susan C; Tackett, Jennifer L; Waldman, Irwin D; Waszczuk, Monika A; Widiger, Thomas A; Wright, Aidan G C; Zimmerman, Mark

2017-05-01

The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Professional Fee Ratios for US Hospital Discharge Data.

PubMed

Peterson, Cora; Xu, Likang; Florence, Curtis; Grosse, Scott D; Annest, Joseph L

2015-10-01

US hospital discharge datasets typically report facility charges (ie, room and board), excluding professional fees (ie, attending physicians' charges). We aimed to estimate professional fee ratios (PFR) by year and clinical diagnosis for use in cost analyses based on hospital discharge data. The subjects consisted of a retrospective cohort of Truven Health MarketScan 2004-2012 inpatient admissions (n=23,594,605) and treat-and-release emergency department (ED) visits (n=70,771,576). PFR per visit was assessed as total payments divided by facility-only payments. Using ordinary least squares regression models controlling for selected characteristics (ie, patient age, comorbidities, etc.), we calculated adjusted mean PFR for admissions by health insurance type (commercial or Medicaid) per year overall and by Major Diagnostic Category (MDC), Diagnostic Related Group, Healthcare Cost and Utilization Project Clinical Classification Software, and primary International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) diagnosis, and for ED visits per year overall and by MDC and primary ICD-9-CM diagnosis. Adjusted mean PFR for 2012 admissions, including preceding ED visits, was 1.264 (95% CI, 1.264, 1.265) for commercially insured admissions (n=2,614,326) and 1.177 (1.176, 1.177) for Medicaid admissions (n=816,503), indicating professional payments increased total per-admission payments by an average 26.4% and 17.7%, respectively, above facility-only payments. Adjusted mean PFR for 2012 ED visits was 1.286 (1.286, 1.286) for commercially insured visits (n=8,808,734) and 1.440 (1.439, 1.440) for Medicaid visits (n=2,994,696). Supplemental tables report 2004-2012 annual PFR estimates by clinical classifications. Adjustments for professional fees are recommended when hospital facility-only financial data from US hospital discharge datasets are used to estimate health care costs.

Identification of patients with nasopharyngeal carcinoma by serum protein profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

PubMed

Zhu, Xiao-Dong; Su, Fang; Liang, Zhong-Guo; Li, Ling; Qu, Song; Liang, Xia; Wang, Qi; Liang, Shi-Xiong; Chen, Long

2014-08-01

As diagnosis of nasopharyngeal carcinoma at an early disease stage is important, we attempted to distinguish between patients with nasopharyngeal carcinoma and noncancer controls by using serum protein profiles. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry and CM10 protein chip were used to detect the serum proteomic patterns of 65 patients with nasopharyngeal carcinoma before radiotherapy and 93 noncancer controls. Proteomic spectra of serum samples from 50 nasopharyngeal carcinoma patients and 60 noncancer controls were used as a training set. The validity of the classification tree was then challenged with a blind test set which included another 15 patients with nasopharyngeal carcinoma and 33 noncancer controls. Biomarker Wizard 3.01 and Biomarker Pattern 5.01 were used in combination to analyze the data and to develop diagnostic models. 21 protein peaks were significantly different between nasopharyngeal carcinoma and controls. 4 mass peaks (M4182, M5343, M5913 and M8702 mass/charge ratio) were chosen automatically to construct a classification tree. The classification tree correctly determined 93.8 % (45/48) of the test samples with 93.3 % (14/15) of the nasopharyngeal carcinoma samples and 93.9 % (31/33) of the noncancer samples. Using a combination of serum protein profiles and Epstein-Barr viral capsid antigen immunoglobulin A antibody tests, the diagnostic sensitivity and specificity were increased to 100 and 97 %, respectively. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry could correctly distinguish nasopharyngeal carcinoma from noncancer individuals and showed great potential for the development of a screening test for the detection of nasopharyngeal carcinoma.

Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach.

PubMed

Cai, Hongmin; Peng, Yanxia; Ou, Caiwen; Chen, Minsheng; Li, Li

2014-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported. The study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination. Seven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively. Multi-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.

Substance dependence and non-dependence in the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD): can an identical conceptualization be achieved?

PubMed

Saunders, John B

2006-09-01

This review summarizes the history of the development of diagnostic constructs that apply to repetitive substance use, and compares and contrasts the nature, psychometric performance and utility of the major diagnoses in the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) diagnostic systems. The available literature was reviewed with a particular focus on diagnostic concepts that are relevant for clinical and epidemiological practice, and so that research questions could be generated that might inform the development of the next generation of DSM and ICD diagnoses. The substance dependence syndrome is a psychometrically robust and clinically useful construct, which applies to a range of psychoactive substances. The differences between the DSM fourth edition (DSM-IV) and the ICD tenth edition (ICD-10) versions are minimal and could be resolved. DSM-IV substance abuse performs moderately well but, being defined essentially by social criteria, may be culture-dependent. ICD-10 harmful substance use performs poorly as a diagnostic entity. There are good prospects for resolving many of the differences between the DSM and ICD systems. A new non-dependence diagnosis is required. There would also be advantages in a subthreshold diagnosis of hazardous or risky substance use being incorporated into the two systems. Biomedical research can be drawn upon to define a psychophysiological 'driving force' which could underpin a broad spectrum of substance use disorders.

Influence of nuclei segmentation on breast cancer malignancy classification

NASA Astrophysics Data System (ADS)

Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

A comprehensive classification of galaxies in the Sloan Digital Sky Survey: how to tell true from fake AGN?

NASA Astrophysics Data System (ADS)

Cid Fernandes, R.; Stasińska, G.; Mateus, A.; Vale Asari, N.

2011-05-01

We use the WHα versus [N II]/Hα (WHAN) diagram introduced by us in previous work to provide a comprehensive emission-line classification of Sloan Digital Sky Survey galaxies. This classification is able to cope with the large population of weak line galaxies that do not appear in traditional diagrams due to a lack of some of the diagnostic lines. A further advantage of the WHAN diagram is to allow the differentiation between two very distinct classes that overlap in the low-ionization nuclear emission-line region (LINER) region of traditional diagnostic diagrams. These are galaxies hosting a weakly active galactic nucleus (wAGN) and 'retired galaxies' (RGs), i.e. galaxies that have stopped forming stars and are ionized by their hot low-mass evolved stars. A useful criterion to distinguish true from fake AGN (i.e. the RGs) is the value of ξ, which measures the ratio of the extinction-corrected Hα luminosity with respect to the Hα luminosity expected from photoionization by stellar populations older than 108 yr. We find that ξ follows a markedly bimodal distribution, with a ξ≫ 1 population composed by systems undergoing star formation and/or nuclear activity, and a peak at ξ˜ 1 corresponding to the prediction of the RG model. We base our classification scheme not on ξ but on a more readily available and model-independent quantity which provides an excellent observational proxy for ξ: the equivalent width of Hα. Based on the bimodal distribution of WHα, we set the practical division between wAGN and RGs at WHα= 3 Å. Five classes of galaxies are identified within the WHAN diagram: (i) pure star-forming galaxies: ? and WHα > 3 Å; (ii) strong AGN (i.e. Seyferts): ? and WHα > 6 Å; (iii) weak AGN: ? and WHα between 3 and 6 Å; (iv) RGs (i.e. fake AGN): WHα < 3 Å; (v) passive galaxies (actually, lineless galaxies): WHα and W[N II] < 0.5 Å. A comparative analysis of star formation histories and of other physical and observational properties in these different classes of galaxies corroborates our proposed differentiation between RGs and wAGN in the LINER-like family. This analysis also shows similarities between strong and weak AGN on the one hand, and retired and passive galaxies on the other.

Personality disorders in DSM-5: emerging research on the alternative model.

PubMed

Morey, Leslie C; Benson, Kathryn T; Busch, Alexander J; Skodol, Andrew E

2015-04-01

The current categorical classification of personality disorders, originally introduced in the Diagnostic and Statistical Manual of Mental Disorders (DSM-III), has been found to suffer from numerous shortcomings that hamper its usefulness for research and for clinical application. The Personality and Personality Disorders Work Group for DSM-5 was charged with developing an alternative model that would address many of these concerns. The developed model involved a hybrid dimensional/categorical model that represented personality disorders as combinations of core impairments in personality functioning with specific configurations of problematic personality traits. The Board of Trustees of the American Psychiatric Association did not accept the Task Force recommendation to implement this novel approach, and thus this alternative model was included in Sect. III of the DSM-5 among concepts requiring additional study. This review provides an overview of the emerging research on this alternative model, addressing each of the primary components of the model.

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

Risk of preterm birth by subtype among Medi-Cal participants with mental illness.

PubMed

Baer, Rebecca J; Chambers, Christina D; Bandoli, Gretchen; Jelliffe-Pawlowski, Laura L

2016-10-01

Previous studies have demonstrated an association between mental illness and preterm birth (before 37 weeks). However, these investigations have not simultaneously considered gestation of preterm birth, the indication (eg, spontaneous or medically indicated), and specific mental illness classifications. The objective of the study was to examine the likelihood of preterm birth across gestational lengths and indications among Medi-Cal (California's Medicaid program) participants with a diagnostic code for mental illness. Mental illnesses were studied by specific illness classification. The study population was drawn from singleton live births in California from 2007 through 2011 in the birth cohort file maintained by the California Office of Statewide Health Planning and Development, which includes birth certificate and hospital discharge records. The sample was restricted to women with Medi-Cal coverage for prenatal care. Women with mental illness were identified using International Classification of Diseases, ninth revision, codes from their hospital discharge record. Women without a mental illness International Classification of Diseases, ninth revision, code were randomly selected at a 4:1 ratio. Adjusting for maternal characteristics and obstetric complications, relative risks and 95% confidence intervals were calculated for preterm birth comparing women with a mental illness diagnostic code with women without such a code. We identified 6198 women with a mental illness diagnostic code and selected 24,792 women with no such code. The risk of preterm birth in women with a mental illness were 1.2 times higher than women without a mental illness (adjusted relative risk, 1.2, 95% confidence interval, 1.1-1.3). Among the specific mental illnesses, schizophrenia, major depression, and personality disorders had the strongest associations with preterm birth (adjusted relative risks, 2.0, 2.0 and 3.3, respectively). Women receiving prenatal care through California's low-income health insurance who had at least 1 mental illness diagnostic code were 1.2-3.3-times more likely to have a preterm birth than women without a mental illness, and these risks persisted across most illness classifications. Although it cannot be determined from these data whether specific treatments for mental illness contribute to the observed associations, elevated risk across different diagnoses suggests that some aspects of mental illness itself may confer risk. Copyright © 2016 Elsevier Inc. All rights reserved.

MO-DE-207A-02: A Feature-Preserving Image Reconstruction Method for Improved Pancreaticlesion Classification in Diagnostic CT Imaging

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

Xu, J; Tsui, B; Noo, F

Purpose: To develop a feature-preserving model based image reconstruction (MBIR) method that improves performance in pancreatic lesion classification at equal or reduced radiation dose. Methods: A set of pancreatic lesion models was created with both benign and premalignant lesion types. These two classes of lesions are distinguished by their fine internal structures; their delineation is therefore crucial to the task of pancreatic lesion classification. To reduce image noise while preserving the features of the lesions, we developed a MBIR method with curvature-based regularization. The novel regularization encourages formation of smooth surfaces that model both the exterior shape and the internalmore » features of pancreatic lesions. Given that the curvature depends on the unknown image, image reconstruction or denoising becomes a non-convex optimization problem; to address this issue an iterative-reweighting scheme was used to calculate and update the curvature using the image from the previous iteration. Evaluation was carried out with insertion of the lesion models into the pancreas of a patient CT image. Results: Visual inspection was used to compare conventional TV regularization with our curvature-based regularization. Several penalty-strengths were considered for TV regularization, all of which resulted in erasing portions of the septation (thin partition) in a premalignant lesion. At matched noise variance (50% noise reduction in the patient stomach region), the connectivity of the septation was well preserved using the proposed curvature-based method. Conclusion: The curvature-based regularization is able to reduce image noise while simultaneously preserving the lesion features. This method could potentially improve task performance for pancreatic lesion classification at equal or reduced radiation dose. The result is of high significance for longitudinal surveillance studies of patients with pancreatic cysts, which may develop into pancreatic cancer. The Senior Author receives financial support from Siemens GmbH Healthcare.« less

Development of an intelligent diagnostic system for reusable rocket engine control

NASA Technical Reports Server (NTRS)

Anex, R. P.; Russell, J. R.; Guo, T.-H.

1991-01-01

A description of an intelligent diagnostic system for the Space Shuttle Main Engines (SSME) is presented. This system is suitable for incorporation in an intelligent controller which implements accommodating closed-loop control to extend engine life and maximize available performance. The diagnostic system architecture is a modular, hierarchical, blackboard system which is particularly well suited for real-time implementation of a system which must be repeatedly updated and extended. The diagnostic problem is formulated as a hierarchical classification problem in which the failure hypotheses are represented in terms of predefined data patterns. The diagnostic expert system incorporates techniques for priority-based diagnostics, the combination of analytical and heuristic knowledge for diagnosis, integration of different AI systems, and the implementation of hierarchical distributed systems. A prototype reusable rocket engine diagnostic system (ReREDS) has been implemented. The prototype user interface and diagnostic performance using SSME test data are described.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework.

PubMed

Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan

2016-02-01

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other dataset. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice. Copyright © 2015 Elsevier Inc. All rights reserved.

Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

PubMed Central

Xi, Jinxiang; Zhao, Weizhong; Yuan, Jiayao Eddie; Kim, JongWon; Si, Xiuhua; Xu, Xiaowei

2015-01-01

Background Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases. Objective and Methods In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions. Findings By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations. Conclusion For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases. PMID:26422016

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

PubMed

Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

2015-01-01

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

The Value of Ensari’s Proposal in Evaluating the Mucosal Pathology of Childhood Celiac Disease: Old Classification versus New Version

PubMed Central

Güreşci, Servet; Hızlı, Şamil; Şimşek, Gülçin Güler

2012-01-01

Objective: Small intestinal biopsy remains the gold standard in diagnosing celiac disease (CD); however, the wide spectrum of histopathological states and differential diagnosis of CD is still a diagnostic problem for pathologists. Recently, Ensari reviewed the literature and proposed an update of the histopathological diagnosis and classification for CD. Materials and Methods: In this study, the histopathological materials of 54 children in whom CD was diagnosed at our hospital were reviewed to compare the previous Marsh and Modified Marsh-Oberhuber classifications with this new proposal. Results: In this study, we show that the Ensari classification is as accurate as the Marsh and Modified Marsh classifications in describing the consecutive states of mucosal damage seen in CD. Conclusions: Ensari’s classification is simple, practical and facilitative in diagnosing and subtyping of mucosal pathology of CD. PMID:25207015

Classification versus inference learning contrasted with real-world categories.

PubMed

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

NASA Astrophysics Data System (ADS)

Ding, Hao; Cao, Ming; DuPont, Andrew W.; Scott, Larry D.; Guha, Sushovan; Singhal, Shashideep; Younes, Mamoun; Pence, Isaac; Herline, Alan; Schwartz, David; Xu, Hua; Mahadevan-Jansen, Anita; Bi, Xiaohong

2016-03-01

Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

Values and assumptions in the development of DSM-III and DSM-III-R: an insider's perspective and a belated response to Sadler, Hulgus, and Agich's "On values in recent American psychiatric classification".

PubMed

Spitzer, R L

2001-06-01

It is widely acknowledged that the approach taken in the development of a classification of mental disorders is guided by various values and assumptions. The author, who played a central role in the development of DSM-III (American Psychiatric Association [1980] Diagnostic and statistical manual of mental disorders, 3rd ed. Washington, DC:Author) and DSM-III-R (American Psychiatric Association [1987] Diagnostic and statistical manual of mental disorders, 3rd ed, rev. Washington, DC:Author) will explicate the basic values and assumptions that guided the development of these two diagnostic manuals. In so doing, the author will respond to the critique of DSM-III and DSM-III-R made by Sadler et al. in their 1994 paper (Sadler JZ, Hulgus YF, Agich GJ [1994] On values in recent American psychiatric classification. JMed Phil 19:261-277). The author will attempt to demonstrate that the stated goals of DSM-III and DSM-III-R are not inherently in conflict and are easily explicated by appealing to widely held values and assumptions, most of which appeared in the literature during the development of the manuals. Furthermore, we will demonstrate that it is not true that DSM-III places greater emphasis on reliability over validity and is covertly committed to a biological approach to explaining psychiatric disturbance.

Characterization and classification of lupus patients based on plasma thermograms

PubMed Central

Chaires, Jonathan B.; Mekmaysy, Chongkham S.; DeLeeuw, Lynn; Sivils, Kathy L.; Harley, John B.; Rovin, Brad H.; Kulasekera, K. B.; Jarjour, Wael N.

2017-01-01

Objective Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria. Methods Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers. Results Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia. Conclusion Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting. PMID:29149219

Usefulness of combining gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging and contrast-enhanced ultrasound for diagnosing the macroscopic classification of small hepatocellular carcinoma.

PubMed

Kobayashi, Tomoki; Aikata, Hiroshi; Hatooka, Masahiro; Morio, Kei; Morio, Reona; Kan, Hiromi; Fujino, Hatsue; Fukuhara, Takayuki; Masaki, Keiichi; Ohno, Atsushi; Naeshiro, Noriaki; Nakahara, Takashi; Honda, Yohji; Murakami, Eisuke; Kawaoka, Tomokazu; Tsuge, Masataka; Hiramatsu, Akira; Imamura, Michio; Kawakami, Yoshiiku; Hyogo, Hideyuki; Takahashi, Shoichi; Chayama, Kazuaki

2015-11-01

Non-simple nodules in hepatocellular carcinoma (HCC) correlate with poor prognosis. Therefore, we examined the diagnostic ability of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOB-MRI) and contrast-enhanced ultrasound (CEUS) for diagnosing the macroscopic classification of small HCCs. A total of 85 surgically resected nodules (≤30 mm) were analyzed. HCCs were pathologically classified as simple nodular (SN) and non-SN. By evaluating hepatobiliary phase (HBP) of EOB-MRI and Kupffer phase of CEUS, the diagnostic abilities of both modalities to correctly distinguish between SN and non-SN were compared. Forty-six nodules were diagnosed as SN and the remaining 39 nodules as non-SN. The area under the ROC curve (AUROCs, 95% confidence interval) for the diagnosis of non-SN were EOB-MRI, 0.786 (0.682-0.890): CEUS, 0.784 (0.679-0.889), in combination, 0.876 (0.792-0.959). The sensitivity, specificity, and accuracy were 64.1%, 95.7%, and 81.2% in EOB-MRI, 56.4%, 97.8%, and 78.8% in CEUS, and 84.6%, 95.7%, and 90.6% in combination, respectively. High diagnostic ability was obtained when diagnosed in both modalities combined. The sensitivity was especially statistically significant compared to CEUS. Combined diagnosis by EOB-MRI and CEUS can provide high-quality imaging assessment for determining non-SN in small HCCs. • Non-SN has a higher frequency of MVI and intrahepatic metastasis than SN. • Macroscopic classification is useful to choose the treatment strategy for small HCCs. • Diagnostic ability for macroscopic findings of EOB-MRI and CEUS were statistically equal. • The diagnosis of macroscopic findings by individual modality has limitations. • Combined diagnosis of EOB-MRI and CEUS provides high diagnostic ability.

Comparison of the diagnostic ability of blue laser imaging magnification versus pit pattern analysis for colorectal polyps.

PubMed

Nakano, Arihiro; Hirooka, Yoshiki; Yamamura, Takeshi; Watanabe, Osamu; Nakamura, Masanao; Funasaka, Kohei; Ohno, Eizaburo; Kawashima, Hiroki; Miyahara, Ryoji; Goto, Hidemi

2017-04-01

Background and study aims  There have been few evaluations of the diagnostic ability of new narrow band light observation blue laser imaging (BLI). The present prospective study compared the diagnostic ability of BLI magnification and pit pattern analysis for colorectal polyps. Patients and methods  We collected lesions prospectively, and the analysis of images was made by two endoscopists, retrospectively. A total of 799 colorectal polyps were examined by BLI magnification and pit pattern analysis at Nagoya University Hospital. The Hiroshima narrow-band imaging classification was used for BLI. Differentiation of neoplastic from non-neoplastic lesions and diagnosis of deeply invasive submucosal cancer (dSM) were compared between BLI magnification and pit pattern analysis. Type C2 in the Hiroshima classification was evaluated separately, because application of this category as an index of the depth of cancer invasion was considered difficult. Results  We analyzed 748 colorectal polyps, excluding 51 polyps that were inflammatory polyps, sessile serrated adenoma/polyps, serrated adenomas, advanced colorectal cancers, or other lesions. The accuracy of differential diagnosis between neoplastic and non-neoplastic lesions was 98.4 % using BLI magnification and 98.7 % with pit pattern analysis. In addition, the diagnostic accuracy of BLI magnification and pit pattern analysis for dSM for cancer was 89.5 % and 92.1 %, respectively. When type C2 lesions were excluded, the diagnostic accuracy of BLI for dSM was 95.9 %. The 18 type C2 lesions comprised 1 adenoma, 9 intramucosal or slightly invasive submucosal cancers, and 8 dSM. Pit pattern analysis allowed accurate diagnosis of the depth of invasion in 13 lesions (72.2 %). Conclusions  Most colorectal polyps could be diagnosed accurately by BLI magnification without pit pattern analysis, but we should add pit pattern analysis for type C2 lesions in the Hiroshima classification.

Leveraging sequence-based faecal microbial community survey data to identify a composite biomarker for colorectal cancer.

PubMed

Shah, Manasi S; DeSantis, Todd Z; Weinmaier, Thomas; McMurdie, Paul J; Cope, Julia L; Altrichter, Adam; Yamal, Jose-Miguel; Hollister, Emily B

2018-05-01

Colorectal cancer (CRC) is the second leading cause of cancer-associated mortality in the USA. The faecal microbiome may provide non-invasive biomarkers of CRC and indicate transition in the adenoma-carcinoma sequence. Re-analysing raw sequence and metadata from several studies uniformly, we sought to identify a composite and generalisable microbial marker for CRC. Raw 16S rRNA gene sequence data sets from nine studies were processed with two pipelines, (1) QIIME closed reference (QIIME-CR) or (2) a strain-specific method herein termed SS-UP (Strain Select, UPARSE bioinformatics pipeline). A total of 509 samples (79 colorectal adenoma, 195 CRC and 235 controls) were analysed. Differential abundance, meta-analysis random effects regression and machine learning analyses were carried out to determine the consistency and diagnostic capabilities of potential microbial biomarkers. Definitive taxa, including Parvimonas micra ATCC 33270, Streptococcus anginosus and yet-to-be-cultured members of Proteobacteria, were frequently and significantly increased in stools from patients with CRC compared with controls across studies and had high discriminatory capacity in diagnostic classification. Microbiome-based CRC versus control classification produced an area under receiver operator characteristic (AUROC) curve of 76.6% in QIIME-CR and 80.3% in SS-UP. Combining clinical and microbiome markers gave a diagnostic AUROC of 83.3% for QIIME-CR and 91.3% for SS-UP. Despite technological differences across studies and methods, key microbial markers emerged as important in classifying CRC cases and such could be used in a universal diagnostic for the disease. The choice of bioinformatics pipeline influenced accuracy of classification. Strain-resolved microbial markers might prove crucial in providing a microbial diagnostic for CRC. 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/.

Exploring the utility of narrative analysis in diagnostic decision making: picture-bound reference, elaboration, and fetal alcohol spectrum disorders.

PubMed

Thorne, John C; Coggins, Truman E; Carmichael Olson, Heather; Astley, Susan J

2007-04-01

To evaluate classification accuracy and clinical feasibility of a narrative analysis tool for identifying children with a fetal alcohol spectrum disorder (FASD). Picture-elicited narratives generated by 16 age-matched pairs of school-aged children (FASD vs. typical development [TD]) were coded for semantic elaboration and reference strategy by judges who were unaware of age, gender, and group membership of the participants. Receiver operating characteristic (ROC) curves were used to examine the classification accuracy of the resulting set of narrative measures for making 2 classifications: (a) for the 16 children diagnosed with FASD, low performance (n = 7) versus average performance (n = 9) on a standardized expressive language task and (b) FASD (n = 16) versus TD (n = 16). Combining the rates of semantic elaboration and pragmatically inappropriate reference perfectly matched a classification based on performance on the standardized language task. More importantly, the rate of ambiguous nominal reference was highly accurate in classifying children with an FASD regardless of their performance on the standardized language task (area under the ROC curve = .863, confidence interval = .736-.991). Results support further study of the diagnostic utility of narrative analysis using discourse level measures of elaboration and children's strategic use of reference.

Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images.

PubMed

Ni Ni, Soe; Tian, J; Marziliano, Pina; Wong, Hong-Tym

2014-01-01

Optical coherence tomography is a high resolution, rapid, and noninvasive diagnostic tool for angle closure glaucoma. In this paper, we present a new strategy for the classification of the angle closure glaucoma using morphological shape analysis of the iridocorneal angle. The angle structure configuration is quantified by the following six features: (1) mean of the continuous measurement of the angle opening distance; (2) area of the trapezoidal profile of the iridocorneal angle centered at Schwalbe's line; (3) mean of the iris curvature from the extracted iris image; (4) complex shape descriptor, fractal dimension, to quantify the complexity, or changes of iridocorneal angle; (5) ellipticity moment shape descriptor; and (6) triangularity moment shape descriptor. Then, the fuzzy k nearest neighbor (fkNN) classifier is utilized for classification of angle closure glaucoma. Two hundred and sixty-four swept source optical coherence tomography (SS-OCT) images from 148 patients were analyzed in this study. From the experimental results, the fkNN reveals the best classification accuracy (99.11 ± 0.76%) and AUC (0.98 ± 0.012) with the combination of fractal dimension and biometric parameters. It showed that the proposed approach has promising potential to become a computer aided diagnostic tool for angle closure glaucoma (ACG) disease.

The role of amino acid PET in the light of the new WHO classification 2016 for brain tumors.

PubMed

Suchorska, Bogdana; Albert, Nathalie L; Bauer, Elena K; Tonn, Jörg-Christian; Galldiks, Norbert

2018-04-26

Since its introduction in 2016, the revision of the World Health Organization (WHO) classification of central nervous system tumours has already changed the diagnostic and therapeutic approach in glial tumors. Blurring the lines between entities formerly labelled as "high-grade" or "low-grade", molecular markers define distinct biological subtypes with different clinical course. This new classification raises the demand for non-invasive imaging methods focussing on depicting metabolic processes. We performed a review of current literature on the use of amino acid PET (AA-PET) for obtaining diagnostic or prognostic information on glioma in the setting of the current WHO 2016 classification. So far, only a few studies have focussed on combining molecular genetic information and metabolic imaging using AA-PET. The current review summarizes the information available on "molecular grading" as well as prognostic information obtained from AA-PET and delivers an insight into a possible interrelation between metabolic imaging and glioma genetics. Within the framework of molecular characterization of gliomas, metabolic imaging using AA-PET is a promising tool for non-invasive characterisation of molecular features and to provide additional prognostic information. Further studies incorporating molecular and metabolic features are necessary to improve the explanatory power of AA-PET in glial tumors.

A new species of Leiobunum from Arizona, U. S. A. highlights the limits of typological classification in harvestmen (Opiliones: Sclerosomatidae: Leiobuninae).

PubMed

Shultz, Jeffrey W

2018-01-09

A new species of leiobunine harvestman from the Chiricahua Mountains of Arizona is described. The species lacks pro- and retrolateral submarginal rows of coxal denticles, a feature often considered diagnostic for the polyphyletic Nelima, and has greatly reduced ventral dentition on the palpal claw, as in the monotypic Leuronychus. In most other respects, the species is uniquely similar to members of a clade from central and western Mexico currently in the poly- and/or paraphyletic Leiobunum. These traits include a supracheliceral lamina with a wide transverse plate and a canaliculate ocularium, with an anterior surface that slopes dorsoposteriorly and a posterior surface that bulges rearward and is constricted at its base.  There is thus a conflict between classification using traditional diagnostic characters and classification using unique similarity of non-traditional characters. The problem is exacerbated by the problematic status of each candidate genus. Here the species is placed in Leiobunum as L. silum sp. nov., a decision that gives weight to probable phylogenetic affinity with species currently placed in that genus. Leiobunum silum provides an excellent example of the limits of traditional typological classification and the need for a broad-scale morphological and molecular revision of sclerosomatid harvestmen.

Does Diagnostic Classification of Early-Onset Psychosis Change over Follow-Up?

ERIC Educational Resources Information Center

Fraguas, David; de Castro, Maria J.; Medina, Oscar; Parellada, Mara; Moreno, Dolores; Graell, Montserrat; Merchan-Naranjo, Jessica; Arango, Celso

2008-01-01

Objective: To examine the diagnostic stability and the functional outcome of patients with early-onset psychosis (EOP) over a 2-year follow-up period. Methods: A total of 24 patients (18 males (75%) and 6 females (25%), mean age [plus or minus] SD: 15.7 [plus or minus] 1.6 years) with a first episode of EOP formed the sample. Psychotic symptoms…

The Screening Accuracy of the Parent and Teacher-Reported Social Responsiveness Scale (SRS): Comparison with the 3Di and ADOS

ERIC Educational Resources Information Center

Duvekot, Jorieke; van der Ende, Jan; Verhulst, Frank C.; Greaves-Lord, Kirstin

2015-01-01

The screening accuracy of the parent and teacher-reported Social Responsiveness Scale (SRS) was compared with an autism spectrum disorder (ASD) classification according to (1) the Developmental, Dimensional, and Diagnostic Interview (3Di), (2) the Autism Diagnostic Observation Schedule (ADOS), (3) both the 3Di and ADOS, in 186 children referred to…

Diagnostic of Horndeski theories

NASA Astrophysics Data System (ADS)

Perenon, Louis; Marinoni, Christian; Piazza, Federico

2017-01-01

We study the effects of Horndeski models of dark energy on the observables of the large-scale structure in the late time universe. A novel classification into Late dark energy, Early dark energy and Early modified gravity scenarios is proposed, according to whether such models predict deviations from the standard paradigm persistent at early time in the matter domination epoch. We discuss the physical imprints left by each specific class of models on the effective Newton constant μ, the gravitational slip parameter η, the light deflection parameter Σ and the growth function fσ8 and demonstrate that a convenient way to dress a complete portrait of the viability of the Horndeski accelerating mechanism is via two, redshift-dependent, diagnostics: the μ(z) - Σ(z) and the fσ8(z) - Σ(z) planes. If future, model-independent, measurements point to either Σ - 1 < 0 at redshift zero or μ - 1 < 0 with Σ - 1 > 0 at high redshifts or μ - 1 > 0 with Σ - 1 < 0 at high redshifts, Horndeski theories are effectively ruled out. If fσ8 is measured to be larger than expected in a ΛCDM model at z > 1.5 then Early dark energy models are definitely ruled out. On the opposite case, Late dark energy models are rejected by data if Σ < 1, while, if Σ > 1, only Early modifications of gravity provide a viable framework to interpret data.

Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM)

NASA Astrophysics Data System (ADS)

Díaz-Ayil, G.; Amouroux, M.; Blondel, W. C. P. M.; Bourg-Heckly, G.; Leroux, A.; Guillemin, F.; Granjon, Y.

2009-07-01

This paper deals with the development and application of in vivo spatially-resolved bimodal spectroscopy (AutoFluorescence AF and Diffuse Reflectance DR), to discriminate various stages of skin precancer in a preclinical model (UV-irradiated mouse): Compensatory Hyperplasia CH, Atypical Hyperplasia AH and Dysplasia D. A programmable instrumentation was developed for acquiring AF emission spectra using 7 excitation wavelengths: 360, 368, 390, 400, 410, 420 and 430 nm, and DR spectra in the 390-720 nm wavelength range. After various steps of intensity spectra preprocessing (filtering, spectral correction and intensity normalization), several sets of spectral characteristics were extracted and selected based on their discrimination power statistically tested for every pair-wise comparison of histological classes. Data reduction with Principal Components Analysis (PCA) was performed and 3 classification methods were implemented (k-NN, LDA and SVM), in order to compare diagnostic performance of each method. Diagnostic performance was studied and assessed in terms of sensitivity (Se) and specificity (Sp) as a function of the selected features, of the combinations of 3 different inter-fibers distances and of the numbers of principal components, such that: Se and Sp ≈ 100% when discriminating CH vs. others; Sp ≈ 100% and Se > 95% when discriminating Healthy vs. AH or D; Sp ≈ 74% and Se ≈ 63%for AH vs. D.

Inter- and intraspecific diversity in Cistus L. (Cistaceae) seeds, analysed with computer vision techniques.

PubMed

Lo Bianco, M; Grillo, O; Cañadas, E; Venora, G; Bacchetta, G

2017-03-01

This work aims to discriminate among different species of the genus Cistus, using seed parameters and following the scientific plant names included as accepted in The Plant List. Also, the intraspecific phenotypic differentiation of C. creticus, through comparison with three subspecies (C. creticus subsp. creticus, C. c. subsp. eriocephalus and C. c. subsp. corsicus), as well as the interpopulation variability among five C. creticus subsp. eriocephalus populations was evaluated. Seed mean weight and 137 morphocolorimetric quantitative variables, describing shape, size, colour and textural seed traits, were measured using image analysis techniques. Measured data were analysed applying step-wise linear discriminant analysis. An overall cross-validated classification performance of 80.6% was recorded at species level. With regard to C. creticus, as case study, percentages of correct discrimination of 96.7% and 99.6% were achieved at intraspecific and interpopulation levels, respectively. In this classification model, the relevance of the colorimetric and textural descriptive features was highlighted, as well as the seed mean weight, which was the most discriminant feature at specific and intraspecific level. These achievements proved the ability of the image analysis system as highly diagnostic for systematic purposes and confirm that seeds in the genus Cistus have important diagnostic value. © 2016 German Botanical Society and The Royal Botanical Society of the Netherlands.

Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

Multidimensional Ultrasound Imaging of the Wrist: Changes of Shape and Displacement of the Median Nerve and Tendons in Carpal Tunnel Syndrome

PubMed Central

Filius, Anika; Scheltens, Marjan; Bosch, Hans G.; van Doorn, Pieter A.; Stam, Henk J.; Hovius, Steven E.R.; Amadio, Peter C.; Selles, Ruud W.

2015-01-01

Dynamics of structures within the carpal tunnel may alter in carpal tunnel syndrome (CTS) due to fibrotic changes and increased carpal tunnel pressure. Ultrasound can visualize these potential changes, making ultrasound potentially an accurate diagnostic tool. To study this, we imaged the carpal tunnel of 113 patients and 42 controls. CTS severity was classified according to validated clinical and nerve conduction study (NCS) classifications. Transversal and longitudinal displacement and shape (changes) were calculated for the median nerve, tendons and surrounding tissue. To predict diagnostic value binary logistic regression modeling was applied. Reduced longitudinal nerve displacement (p≤0.019), increased nerve cross-sectional area (p≤0.006) and perimeter (p≤0.007), and a trend of relatively changed tendon displacements were seen in patients. Changes were more convincing when CTS was classified as more severe. Binary logistic modeling to diagnose CTS using ultrasound showed a sensitivity of 70-71% and specificity of 80-84%. In conclusion, CTS patients have altered dynamics of structures within the carpal tunnel. PMID:25865180

Machine learning techniques for medical diagnosis of diabetes using iris images.

PubMed

Samant, Piyush; Agarwal, Ravinder

2018-04-01

Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods. Investigation was performed over a close group of total 338 subjects (180 diabetic and 158 non-diabetic). Infra-red images of both the eyes were captured simultaneously. The region of interest from the iris image was cropped as zone corresponds to the position of pancreas organ according to the iridology chart. Statistical, texture and discrete wavelength transformation features were extracted from the region of interest. The results show best classification accuracy of 89.63% calculated from RF classifier. Maximum specificity and sensitivity were absorbed as 0.9687 and 0.988, respectively. Results have revealed the effectiveness and diagnostic significance of proposed model for non-invasive and automatic diabetes diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Studies in knowledge-based diagnosis of failures in robotic assembly

NASA Technical Reports Server (NTRS)

Lam, Raymond K.; Pollard, Nancy S.; Desai, Rajiv S.

1990-01-01

The telerobot diagnostic system (TDS) is a knowledge-based system that is being developed for identification and diagnosis of failures in the space robotic domain. The system is able to isolate the symptoms of the failure, generate failure hypotheses based on these symptoms, and test their validity at various levels by interpreting or simulating the effects of the hypotheses on results of plan execution. The implementation of the TDS is outlined. The classification of failures and the types of system models used by the TDS are discussed. A detailed example of the TDS approach to failure diagnosis is provided.

Psychiatric Disorders: Diagnosis to Therapy

PubMed Central

Krystal, John H.; State, Matthew W.

2014-01-01

Recent findings in a range of scientific disciplines are challenging the conventional wisdom regarding the etiology, classification and treatment of psychiatric disorders. This review focuses on the current state of the psychiatric diagnostic nosology and recent progress in three areas: genomics, neuroimaging, and therapeutics development. The accelerating pace of novel and unexpected findings is transforming the understanding of mental illness and represents a hopeful sign that the approaches and models that have sustained the field for the past 40 years are yielding to a flood of new data and presaging the emergence of a new and more powerful scientific paradigm. PMID:24679536

Neurogenetics in Child Neurology: Redefining a Discipline in the Twenty-first Century.

PubMed

Kaufmann, Walter E

2016-12-01

Increasing knowledge on genetic etiology of pediatric neurologic disorders is affecting the practice of the specialty. I reviewed here the history of pediatric neurologic disorder classification and the role of genetics in the process. I also discussed the concept of clinical neurogenetics, with its role in clinical practice, education, and research. Finally, I propose a flexible model for clinical neurogenetics in child neurology in the twenty-first century. In combination with disorder-specific clinical programs, clinical neurogenetics can become a home for complex clinical issues, repository of genetic diagnostic advances, educational resource, and research engine in child neurology.

Computer-assisted diagnosis of melanoma.

PubMed

Fuller, Collin; Cellura, A Paul; Hibler, Brian P; Burris, Katy

2016-03-01

The computer-assisted diagnosis of melanoma is an exciting area of research where imaging techniques are combined with diagnostic algorithms in an attempt to improve detection and outcomes for patients with skin lesions suspicious for malignancy. Once an image has been acquired, it undergoes a processing pathway which includes preprocessing, enhancement, segmentation, feature extraction, feature selection, change detection, and ultimately classification. Practicality for everyday clinical use remains a vital question. A successful model must obtain results that are on par or outperform experienced dermatologists, keep costs at a minimum, be user-friendly, and be time efficient with high sensitivity and specificity. ©2015 Frontline Medical Communications.

Nursing Care Hour Standards Study. Part 1. Section B. Patient Classification System Model Development

DTIC Science & Technology

1981-09-01

Measurement/Adult El FI~ [El 1:11] II PIES Form 326e (OT) 1 Novembeti 1978 A-3 sfti= 237 Saline IrrigatioD (Gastric)W uli il 111 Enema: Cleansing Mi...U lIED Dilatation and -Curettage/Assisting Physician ii) El Do~ EI M.1 L or Evacuation Vaginal /Pelvic Examinrations/Assisting Physician ElU L. IE l2...ADMINISTRATION: Oral -QB E ED EE ElE Suppositories Rectal/ Vaginal 1E El El El I ElD Topical MI~II iE Sublingual III ol El El El DIAGNOSTIC TESTS: Bone

Primary progressive aphasia: classification of variants in 100 consecutive Brazilian cases

PubMed Central

Senaha, Mirna Lie Hosogi; Caramelli, Paulo; Brucki, Sonia M.D.; Smid, Jerusa; Takada, Leonel T.; Porto, Claudia S.; César, Karolina G.; Matioli, Maria Niures P.; Soares, Roger T.; Mansur, Letícia L.; Nitrini, Ricardo

2013-01-01

Primary progressive aphasia (PPA) is a neurodegenerative clinical syndrome characterized primarily by progressive language impairment. Recently, consensus diagnostic criteria were published for the diagnosis and classification of variants of PPA. The currently recognized variants are nonfluent/agrammatic (PPA-G), logopenic (PPA-L) and semantic (PPA-S). OBJECTIVE To analyze the demographic data and the clinical classification of 100 PPA cases. METHODS Data from 100 PPA patients who were consecutively evaluated between 1999 and 2012 were analyzed. The patients underwent neurological, cognitive and language evaluation. The cases were classified according to the proposed variants, using predominantly the guidelines proposed in the consensus diagnostic criteria from 2011. RESULTS The sample consisted of 57 women and 43 men, aged at onset 67.2±8.1 years (range of between 53 and 83 years). Thirty-five patients presented PPA-S, 29 PPA-G and 16 PPA-L. It was not possible to classify 20% of the cases into any one of the proposed variants. CONCLUSION It was possible to classify 80% of the sample into one of the three PPA variants proposed. Perhaps the consensus classification requires some adjustments to accommodate cases that do not fit into any of the variants and to avoid overlap where cases fit more than one variant. Nonetheless, the established current guidelines are a useful tool to address the classification and diagnosis of PPA and are also of great value in standardizing terminologies to improve consistency across studies from different research centers. PMID:29213827