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

Clinical Information and Decision Making (1915) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2111) Bipolar Disorders (74) Depression and Mood Disorders; Suicide Prevention (1691) Diagnostic Tools in Mental Health (326)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46165, first published .
Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

Authors of this article:

Dong Yun Lee1 Author Orcid Image ;   Byungjin Choi1 Author Orcid Image ;   Chungsoo Kim2 Author Orcid Image ;   Egill Fridgeirsson3 Author Orcid Image ;   Jenna Reps4 Author Orcid Image ;   Myoungsuk Kim5 Author Orcid Image ;   Jihyeong Kim5 Author Orcid Image ;   Jae-Won Jang6 Author Orcid Image ;   Sang Youl Rhee7, 8 Author Orcid Image ;   Won-Woo Seo9 Author Orcid Image ;   Seunghoon Lee10 Author Orcid Image ;   Sang Joon Son11 Author Orcid Image ;   Rae Woong Park1, 2 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

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  8. Liu L, Tang L, Dai M, Ding X, Wu L, Ke X, Luo J, Liu N. Transcultural prediction model for late-life depression based on multi-cohort machine learning and explainable AI. Journal of Affective Disorders 2026;392:120169 View
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