Keyword search (4,163 papers available)

"classification" Keyword-tagged Publications:

Title Authors PubMed ID
1 Attention-Fusion-Based Two-Stream Vision Transformer for Heart Sound Classification Ranipa K; Zhu WP; Swamy MNS; 41155032
ENCS
2 Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module Khademi S; Heidarian S; Afshar P; Mohammadi A; Sidiqi A; Nguyen ET; Ganeshan B; Oikonomou A; 41150036
ENCS
3 An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure Lahmiri S; Bekiros S; 40218199
JMSB
4 CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning Elmekki H; Alagha A; Sami H; Spilkin A; Zanuttini AM; Zakeri E; Bentahar J; Kadem L; Xie WF; Pibarot P; Mizouni R; Otrok H; Singh S; Mourad A; 40107020
ENCS
5 Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klimes P; Frauscher B; 39608298
SOH
6 CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets Islam M; Zunair H; Mohammed N; 38492455
ENCS
7 Fractals in Neuroimaging Lahmiri S; Boukadoum M; Di Ieva A; 38468046
JMSB
8 Bayesian workflow for the investigation of hierarchical classification models from tau-PET and structural MRI data across the Alzheimer's disease spectrum Belasso CJ; Cai Z; Bezgin G; Pascoal T; Stevenson J; Rahmouni N; Tissot C; Lussier F; Rosa-Neto P; Soucy JP; Rivaz H; Benali H; 37920382
PERFORM
9 Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data Thölke P; Mantilla-Ramos YJ; Abdelhedi H; Maschke C; Dehgan A; Harel Y; Kemtur A; Mekki Berrada L; Sahraoui M; Young T; Bellemare Pépin A; El Khantour C; Landry M; Pascarella A; Hadid V; Combrisson E; O' Byrne J; Jerbi K; 37385392
IMAGING
10 Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data Shamshiri MA; Krzyzak A; Kowal M; Korbicz J; 36758326
ENCS
11 Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications Luo Z; Amayri M; Fan W; Bouguila N; 36685642
ENCS
12 Quantifying imbalanced classification methods for leukemia detection Depto DS; Rizvee MM; Rahman A; Zunair H; Rahman MS; Mahdy MRC; 36516574
ENCS
13 Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging Shaikh MS; Jaferzadeh K; Thörnberg B; 35270968
ENCS
14 Voice characteristics from isolated rapid eye movement sleep behavior disorder to early Parkinson's disease Laetitia Jeancolas 35063866
PERFORM
15 Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images Bourouis S; Alharbi A; Bouguila N; 34460578
ENCS
16 Coding Public Health Interventions for Health Technology Assessments: A Pilot Experience With WHO's International Classification of Health Interventions (ICHI) Wübbeler M; Geis S; Stojanovic J; Elliott L; Gutierrez-Ibarluzea I; Lenoir-Wijnkoop I; 34222165
HKAP
17 COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A; 34113843
ENCS
18 A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects. Khannouz M; Glatard T; 33202905
ENCS
19 Probability of Major Depression Classification Based on the SCID, CIDI, and MINI Diagnostic Interviews: A Synthesis of Three Individual Participant Data Meta-Analyses Wu Y; Levis B; Ioannidis JPA; Benedetti A; Thombs BD; 32814337
LIBRARY
20 Diversity, evolution, and classification of virophages uncovered through global metagenomics. Paez-Espino D, Zhou J, Roux S, Nayfach S, Pavlopoulos GA, Schulz F, McMahon KD, Walsh D, Woyke T, Ivanova NN, Eloe-Fadrosh EA, Tringe SG, Kyrpides NC 31823797
BIOLOGY
21 A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors. Dehghani A, Sarbishei O, Glatard T, Shihab E 31752158
ENCS
22 Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Lee K, Khoo HM, Fourcade C, Gotman J, Grova C 30695721
PERFORM

 

Title:Probability of Major Depression Classification Based on the SCID, CIDI, and MINI Diagnostic Interviews: A Synthesis of Three Individual Participant Data Meta-Analyses
Authors:Wu YLevis BIoannidis JPABenedetti AThombs BD
Link:https://pubmed.ncbi.nlm.nih.gov/32814337/
DOI:10.1159/000509283
Publication:Psychotherapy and psychosomatics
Keywords:ClassificationDepressive disordersDiagnostic interviewsIndividual participant data meta-analysisMajor depression
PMID:32814337 Category: Date Added:2020-10-20
Dept Affiliation: LIBRARY
1 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.
2 Department of Psychiatry, McGill University, Montreal, Québec, Canada.
3 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada.
4 Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, United Kingdom.
5 Departments of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics, Stanford University, Stanford, California, USA.
6 Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Québec, Canada.
7 Department of Medicine, McGill University, Montreal, Québec, Canada.
8 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada, brett.thombs@mcgill.ca.
9 Department of Psychiatry, McGill University, Montreal, Québec, Canada, brett.thombs@mcgill.ca.
10 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada, brett.thombs@mcgill.ca.
11 Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Québec, Canada, brett.thombs@mcgill.ca.
12 Department of Medicine, McGill University, Montreal, Québec, Canada, brett.thombs@mcgill.ca.
13 Department of Psychology, McGill University, Montreal, Québec, Canada, brett.thombs@mcgill.ca.
14 Department of Educational and Counselling Psychology, McGill University, Montreal, Québec, Canada, brett.thombs@mcgill.ca.
15 Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
16 Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montréal, Québec, Canada.
17 Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, the Netherlands.
18 Hull York Medical School and the Department of Health Sciences, University of York, Heslington, York, UK.
19 Library, Concordia University, Montréal, Québec, Canada.
20 Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
21 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
22 International Union for Health Promotion and Health Education, École de santé publique de l'Université de Montréal, Montréal, Québec, Canada.
23 Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
24 Department of Medicine, University of Calgary, Calgary, Alberta, Canada.
25 Women's College Hospital and Research Institute, University of Toronto, Toronto, Ontario, Canada.
26 Hotchkiss Brain Institute and O'Brien Institute for Public Health, Calgary, Alberta, Canada.
27 Department of Psychiatry, Makerere University College of Health Sciences, Kampala, Uganda.
28 Department of Behavioural Medicine, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Oman.
29 King Abdulaziz University, Abdullah Sulayman, Jeddah, Makkah, Saudi Arabia.
30 School of Public Health, Faculty of Medicine, Universidad de Chile, Santiago, Chile.
31 Laboratorio de Investigación Biomédica, Facultad de Medicina y Nutrición, Avenida Universidad, Dgo, Mexico.
32 Department of Rehabilitation Med

Description:

Introduction: Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results.

Objective: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI.

Methods: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis.

Results: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80).

Conclusions: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics.





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