ArticleCell reports. Medicine2026
Integrating speech biomarkers and large language models for adolescent suicide risk detection with mobile application for real-world evaluation.
Article in Cell reports. Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Adolescent suicide is a significant public health issue, highlighting the need for efficient methods to detect suicide risk. Here, we develop and validate a speech-based suicide risk detection framework grounded in large language models (LLMs). Two independent cohorts of adolescents aged 10-18 years are analyzed: a development cohort (n = 1,223), with voice recordings collected in structured interview settings for model training and internal evaluation, and an external validation cohort (n = 460), collected through a mobile application to assess feasibility in naturalistic settings. An integrated model combining a speech encoder and an LLMs-based text-processing branch achieves its best performance on the self-introduction task. The model yields an accuracy of 0.808 and a macro-F1 score of 0.807 for suicide risk detection and remains effective under naturalistic mobile assessment. These findings support integrating LLMs with speech-derived markers for scalable adolescent suicide risk detection.
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