Published on in Vol 27 (2025)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/67772, first published
.
Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach
Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach
Journals
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- Hu Y, Wu R, Su M, Lin I, Shen C. Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application. JMIR Medical Informatics 2025;13:e66907 View
- Lyu M, Tan L, Liu F, Lei J, Jiang T, Qi J, Wang X, Yang H, Zhong J, Feng Z. Multiverse analysis of machine learning: classification between groups defined by suicidal ideation screening status using acoustic features in college students. Frontiers in Psychology 2026;17 View
- Ciobanu A, Tebeanu A, Tau V, Chendea A, Franti E, Niculae C, Vasile C, Macarie G, Neagu L, Dascalu M, Stoica C, Moga M, Iorgulescu G. Addressing the Dissimulation Problem in the Assessment of Suicide Risk Using CNN-Based Voice Analysis: A Proof-of-Concept Study. Psychiatry International 2026;7(4):174 View
Conference Proceedings
- Sun Q, Zhang S, Gao Y, Li Y, Li J, Li X. 2025 IEEE International Conference on Data Mining Workshops (ICDMW). Speech Feature Extraction and Selection for Mental Health Analysis: Methods, Tools, and Challenges View