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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

Similarly, Yang et al [27] used MTL with a BERT-based model to incorporate time-perspective cues for suicidal ideation detection on the CEASE dataset. Despite these advances, key limitations persist. First, most studies rely on English-language data. Furthermore, text-based models are often trained on social media content [25,30,45], while audio models rely on public datasets [26-29,31,44,46] that may lack relevance to real-world clinical scenarios, thereby potentially limiting their applicability.

Ya-Han Hu, Ruei-Yan Wu, Min-Yi Su, I-Li Lin, Cheng-Che Shen

JMIR Med Inform 2025;13:e66907


Digital Health Interventions to Reduce Cancer-Related Fatigue Among Adolescents and Young Adults: Scoping Review

Digital Health Interventions to Reduce Cancer-Related Fatigue Among Adolescents and Young Adults: Scoping Review

Researchers SJ and X Yang conducted a comprehensive search across 6 databases—Pub Med, CINAHL, Psyc INFO, Embase, Cochrane Library, and Web of Science—as well as a manual search of relevant references. The search spanned each database from its inception to August 2024 and was restricted to studies published in English.

Shanshan Jiang, Xiaoyu Yang, Xinying Yu

JMIR Mhealth Uhealth 2025;13:e68834