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arXiv Machine Learning By Tianle Hu, Chen Peng, Yi-Hsin Tsai, Takshing Andy Tung, Bingyang Sun, Yenjou Wang

Beyond Risk Prediction: Evidence Grounding and Psychosocial Factor Verification for Explainable Suicide Risk Assessment

Read the original on arXiv Machine Learning →

The paper introduces a framework for explainable suicide risk assessment from social networking service posts, comprising Risk Assessment, Evidence Grounding, and Factor Identification. Risk Assessment uses length-based routing to handle varying post lengths, while Evidence Grounding highlights supporting phrases and enforces consistency with risk predictions. Factor Identification employs two verifiers—Taxonomy Verifier and Evidence-Aware Verifier—to predict psychosocial factors, achieving weighted F1 scores of 0.8088, 0.7605, and 0.5562 for the three tasks respectively.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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