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

Depression and Mood Disorders; Suicide Prevention (1392) Clinical Information and Decision Making (1434) Machine Learning (1501) Decision Support for Health Professionals (1210) New Methods (963) mHealth for Screening (221) Artificial Intelligence (1421) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1565)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52045, first published .
Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation

Authors of this article:

Eduardo Maekawa1, 2 Author Orcid Image ;   Eoin Martino Grua1, 2 Author Orcid Image ;   Carina Akemi Nakamura3 Author Orcid Image ;   Marcia Scazufca3, 4 Author Orcid Image ;   Ricardo Araya5 Author Orcid Image ;   Tim Peters6 Author Orcid Image ;   Pepijn van de Ven1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Maekawa E, de Sá Martins M, Nakamura C, Araya R, Peters T, Van de Ven P, Scazufca M. Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach. Applied Sciences 2024;14(16):7258 View