可解释自杀风险评估框架研究
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2026年10月8日，arXiv Computation and Language发布一手研究《超越风险预测：面向可解释自杀风险评估的证据定位与心理社会因素验证》。该研究基于 IEEE BigData 2026 Explainable Suicide Risk Detection Challenge，提出一个由 Risk Assessment（风险评估）、Evidence Grounding（证据定位）与 Factor Identification（因素识别）组成的框架，旨在将自杀风险评估从单纯预测扩展到可解释的证据定位与心理社会因素验证。目前报道仅介绍框架构成与任务背景，未披露实验数据或性能结果。
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- arXiv · Computation and Language超越风险预测：面向可解释自杀风险评估的证据定位与心理社会因素验证
该研究基于 IEEE BigData 2026 Explainable Suicide Risk Detection Challenge，提出由 Risk Assessment、Evidence Grounding 与 Factor Identification 组成的框架。
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