一个多变量模型的验证,以预测心理健康摄入样本中的自杀企图
Santiago Papini1,2, Honor Hsin3, Patricia Kipnis1
1Division of Research, Kaiser Permanente Division of Research, Oakland, California.
JAMA psychiatry
|March 27, 2024
概括
一个机器学习模型可以预测开始接受心理健康护理的人的自杀企图,高风险病例的前10%占所有企图的近一半. 需要进一步的研究才能有效地实施这种自杀预测工具.
科学领域:
- 精神病学是一个精神病学.
- 公共卫生 公共卫生
- 医疗保健中的机器学习
背景情况:
- 自杀率的上升和对心理健康服务的高需求需要有针对性的预防策略.
- 在最初的心理健康门诊服务启动期间,识别高自杀风险的个人至关重要.
- 之前的自杀预测模型并没有专门评估摄入预约期间的表现.
研究的目的:
- 为了评估机器学习模型在预测患者中自杀企图的有效性,启动门诊心理健康护理.
- 在临界摄入阶段,评估模型在现实临床环境中的性能.
主要方法:
- 一项使用先前开发的机器学习模型预测90天内自杀企图的预测研究.
- 包括2012年1月1日至2022年4月1日期间来自北加州凯泽永久医院的所有心理健康摄入预约.
- 从电子健康记录中提取数据,包括诊断码和政府数据库,以确定结果.
主要成果:
- 这项研究分析了来自835,616名独特患者的1,623,232次预约.
- 机器学习模型证明了接收器操作特征曲线下的面积为0.77.
- 该模型认为前10%的约会具有高风险,占后续自杀企图的48.8%.
结论:
- 之前开发用于预测自杀企图的机器学习模型在心理健康摄入预约的背景下表现良好.
- 需要实施研究来确定临床使用的适当门和干预措施.
- 该模型显示了在摄入期间针对高风险个体的承诺,等待进一步研究患者和临床医生的可接受性.
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