评估机器学习在自杀风险估计中的实际实用性的考虑:成本和公平性的作用
Christopher Kitchen1, Anas Belouali2, Paul S Nestadt2
1Johns Hopkins School of Public Health.
Research square
|January 8, 2026
概括
机器学习模型可以改善自杀死亡预测,提供比传统方法更好的精度. 尽管存在数据挑战,XGBoost表现强,有助于临床决策支持.
科学领域:
- 计算精神病学是一种计算精神病学.
- 医疗信息学 医疗信息学
- 机器学习在医疗保健中的应用
背景情况:
- 自杀死亡模型存在不准确性,限制了临床实用性.
- 现有的机器学习 (ML) 技术可能无法完全解决自杀风险预测中的精确召回权衡.
研究的目的:
- 评估和比较各种ML技术的性能,以预测自杀风险.
- 优化ML模型的精度回忆权衡,并评估它们在临床决策支持中的实用性.
主要方法:
- 使用马里兰州自杀数据库 (MSDW) 数据 (2017-2020) 的回顾性队列分析.
- 使用精度回忆曲线下的区域 (AUPRC) 优化对ML技术 (XGBoost,随机森林,MLP) 的评估.
- 对敏感度和精度偏好的F-Beta统计数据的分析.
主要成果:
- 优化AUPRC的设置显著改善了对逻辑回归的预测.
- 在医院出院 (AUPRC:0.667) 和索赔记录 (AUPRC:0.558) 两方面,XGBoost表现出很高的表现.
- 当精度优先时,XGBoost表现出色,而随机森林和MLP在灵敏度方面更好.
结论:
- 这项研究是第一个使用AUPRC-maxima优化来基于ML的自杀死亡预测的研究.
- 机器学习模型,特别是XGBoost,有望改善自杀风险评估和临床决策支持.
- 解决阶级不平衡和完善绩效评估对于有效的自杀风险建模至关重要.
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