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

Machine Learning (2260) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2291) Depression and Mood Disorders; Suicide Prevention (1772) Mental Health Surveillance and Epidemiology (105) Clinical Mental Health Informatics (74) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (105)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66907, first published .
Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application

Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application

Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application

Authors of this article:

Ya-Han Hu1, 2 Author Orcid Image ;   Ruei-Yan Wu1, 3 Author Orcid Image ;   Min-Yi Su1 Author Orcid Image ;   I-Li Lin4 Author Orcid Image ;   Cheng-Che Shen5, 6 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Ryumina E, Axyonov A, Dolgushin M, Ryumin D, Karpov A. DEPART: Multi-Task Interpretable Depression and Parkinson’s Disease Detection from In-the-Wild Video Data. Big Data and Cognitive Computing 2026;10(3):89 View
  2. Shao D, Shao L, Kou Z. A data-driven analysis of artificial intelligence applications in depression research: 2020–2025. Asian Journal of Psychiatry 2026;120:104978 View