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This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (2559) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (3431) Research Instruments, Questionnaires, and Tools (1257) Decision Support for Health Professionals (2412) Diagnostic Tools in Mental Health (462) Machine Learning (3402) Development and Evaluation of Research Methods, Instruments and Tools (1487)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48444, first published .
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Patient Health Questionnaire-9 Item Pairing Predictiveness for Prescreening Depressive Symptomatology: Machine Learning Analysis

Patient Health Questionnaire-9 Item Pairing Predictiveness for Prescreening Depressive Symptomatology: Machine Learning Analysis

Authors of this article:

Darragh Glavin1, 2 Author Orcid Image ;   Eoin Martino Grua1, 2 Author Orcid Image ;   Carina Akemi Nakamura3 Author Orcid Image ;   Marcia Scazufca3, 4 Author Orcid Image ;   Edinilza Ribeiro dos Santos5 Author Orcid Image ;   Gloria H Y Wong6, 7 Author Orcid Image ;   William Hollingworth8 Author Orcid Image ;   Tim J Peters9 Author Orcid Image ;   Ricardo Araya10 Author Orcid Image ;   Pepijn Van de Ven1, 2 Author Orcid Image
Article Authors Cited by (12) Tweetations (6) Metrics

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  3. Ward S, Patel S, Singh K. The Mind and Spine Connection. Contemporary Spine Surgery 2025;26(5):1 View
  4. Metri P, Kukreja S. Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights. MethodsX 2025;15:103454 View
  5. Pandya A, Singhal N, Panchal P, Patel J, Patel L, Thesia V, Patel M. Evaluating current trends in stress and depression detection using artificial intelligence and machine learning. Intelligent Hospital 2026;2(1):100047 View
  6. Konishcheva K, Leventhal B, Koyama M, Panda S, Vogelstein J, Milham M, Lindner A, Klein A. Accurate and efficient data-driven psychiatric assessment using machine learning. BMC Medical Informatics and Decision Making 2026;26(1) View
  7. Rahman A, Al Farid F, Mehedi M, Uddin J, Abdul Karim H. DepressNet: an interpretable machine learning framework for depression severity stratification using psychometric data. Frontiers in Computer Science 2026;8 View
  8. Zribi M, Bouanani M, Charfi F, Ouali U, Zoghlami N, Skhiri H. Prevalence of Self-reported Lifetime Physician-diagnosed Depression in Tunisia: Results of a National Study. Clinical Practice & Epidemiology in Mental Health 2026;22(1) View
  9. Shaha T, Al Farid F, Begum M, Al Mamun A, Nazneen T, Uddin J, Abdul Karim H. Partitioned feature selection: locally uniform feature selection with structured feature grouping for explainable predictor identification. Frontiers in Artificial Intelligence 2026;9 View
  10. Alotaibi A, Sas C, Rahmani H. A Large Language Model–Based System for Depression Symptom Identification: Iterative Qualitative Co-Design Studies. JMIR Formative Research 2026;10:e95422 View

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  1. . Novel strategies for identifying and addressing mental health and learning disorders in school-age children. View