在自杀风险预测中的极端阶级不平衡的导航
Christopher Kitchen1, Anas Belouali2, Paul S Nestadt3,4
1Center for Population Health Information Technology (IT), Johns Hopkins School of Public Health (JHSPH), Baltimore, MD, United States.
Frontiers in psychiatry
|January 28, 2026
概括
这项研究发现,使自杀风险模型在阶级不平衡和时间地平线方面更加现实,可以提高他们的表现. 特定的患者队列,比如那些有社会需求的患者,显示出更好的精度和回忆力,但不是整体准确度.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 临床信息学 临床信息学
背景情况:
- 自杀风险模型面临实施挑战,原因是发展和现实世界条件之间的差异.
- 任意分类值限制了预测模型及其性能统计数据的可解释性.
- 了解阶级不平衡如何影响预测模型的性能对于准确的自杀风险评估至关重要.
研究的目的:
- 探索不同阶级不平衡比率对基于回归的自杀风险预测模型的表现的影响.
- 调查训练样本组成,时间地平线和患者特征如何影响模型性能.
- 提高自杀风险预测模型的可解释性和可靠性.
主要方法:
- 利用来自马里兰州自杀数据库 (MSDW) 的1649577名患者的大数据集,涵盖2016-2020年.
- 使用交叉验证框架来评估模型性能,按数据源分层 (MHCC,HSCRC).
- 分析了阶级不平衡,时间地平线,患者特征 (年龄,护理利用率,社会需求) 和模型绩效指标 (AUROC,AUPRC) 之间的关联.
主要成果:
- 接收器运行特征曲线下的面积 (AUROC) 与训练样本不平衡或时间地平线不一致而变.
- 精度回忆曲线下的面积 (AUPRC) 与样本不平衡和时间地平线有直接关联,随着不平衡的加大而增加.
- 分层分析显示,AUPRC对特定患者队列的显著改善,例如在急诊室,住院环境或有记录的社会需求的患者,而18岁以下的患者的表现恶化.
结论:
- 自杀风险预测的回归模型表明,当AUPRC在更现实的条件下开发时,包括阶级不平衡和不那么严格的时间范围时,AUPRC得到了改进.
- 针对特定临床群体 (按年龄,护理利用率,社会需求定义) 的培训模型可以导致显著不同的精度和回忆估计,但不是AUROC.
- 根据真实世界的数据特征调整模型开发参数对于可靠和可解释的自杀风险预测至关重要.
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