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

Depression and Mood Disorders; Suicide Prevention (1392) Research Instruments, Questionnaires, and Tools (589) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1565) Diagnostic Tools in Mental Health (248) Development and Evaluation of Research Methods, Instruments and Tools (511) Decision Support for Health Professionals (1210) Machine Learning (1501)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48444, first published .
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

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 (1) Tweetations (4) Metrics

Journals

  1. Wu Y, Ye Z, Wang Z, Duan S, Zhu J, Fang Y. Examining individual and contextual predictors of disability in Chinese older adults: A machine learning approach. International Journal of Medical Informatics 2024;191:105552 View