机器学习算法及其对自杀和自我伤害的预测准确性:系统审查和元分析
Matthew J Spittal1, Xianglin Aneta Guo1, Laurant Kang2
1Centre for Mental Health and Community Wellbeing, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
PLoS medicine
|September 11, 2025
概括
机器学习算法在预测自杀行为和医院治疗的自我伤害方面显示出较低的准确性. 目前的方法不足以查或优先考虑干预措施,需要使用替代的临床方法.
科学领域:
- 计算精神病学是一种计算精神病学.
- 临床信息学是一种临床信息学.
- 公共卫生研究 公共卫生研究
背景情况:
- 机器学习 (ML) 在预测自杀行为方面取得了快速进展.
- 需要系统地评估ML算法对自杀和医院治疗自我伤害预测的准确性.
研究的目的:
- 对预测自杀和医院治疗自伤的ML算法进行系统审查和元分析.
- 评估这些ML算法的诊断准确性.
主要方法:
- 在多个数据库 (PubMed,PsycINFO,Scopus等) 中进行系统搜索. 在2025年4月之前.
- 包括关于自杀或在医院治疗的自伤的病例控制,病例队列或队列研究.
- 排除具有自我报告结果或其他设计的研究.
- 使用用于诊断准确性研究的方法对准确性的统计评估.
主要成果:
- 53项研究符合纳入标准.
- 在ROC曲线下的面积从0.69到0.93.9不等.
- 敏感度:45%-82%;特异性:91%-95%;正概率比:6.5-9.9.
- 积极的预测值与人口患病率 (0.1%至66%) 有显著差异.
结论:
- 预测自杀行为的ML算法准确性目前太低,无法进行有效的查或干预优先级.
- 对于在医院接受治疗的自伤行为,管理应专注于基于需求的评估,解决可修改的风险因素和有效的后续护理.
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