预测心理健康或自我伤害诊断的紧急诊断后的自杀死亡
Gregory E Simon1, Eric Johnson1, Susan M Shortreed1
1Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America.
General hospital psychiatry
|January 26, 2024
概括
使用健康记录数据,可以预测急诊室访问后的自杀死亡. 机器学习模型可以识别需要进一步的后续护理的高风险患者.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
背景情况:
- 自杀仍然是一个重大的公共卫生问题,为心理健康或自我伤害诊断的急诊室访问代表了干预的关键时刻.
- 准确预测此类访问后的自杀风险对于及时有效的患者管理至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测自杀死亡,在急诊室访问后90天内死亡.
- 从电子健康记录和保险索赔数据中识别自杀死亡的关键预测因素.
主要方法:
- 在紧急诊所访问期间,利用了来自七个医疗系统的电子健康记录和保险索赔,用于11岁以上的精神健康或自我伤害诊断的个人.
- 提取了大约2500个潜在预测因素,包括人口,历史和临床特征.
- 使用拉索和随机森林模型应用后勤回归来预测90天内自伤死亡.
主要成果:
- 该研究分析了2,069,170次符合条件的访问,其中899次导致90天内自杀死亡.
- 使用拉索模型的最佳后勤回归实现了接收器操作曲线下的面积为0.823,表明预测性能中等.
- 在预测风险的前5%中的访问捕获了后续自杀死亡的34.8%,自杀死亡率为0.303%.
结论:
- 使用编码健康记录数据的机器学习模型在预测自杀死亡和急诊室访问后的预测中表现出中等的有效性.
- 这些模型可以帮助识别需要更系统的随访护理的患者,以减轻自杀风险.
- 模型的性能在各种人口学子组中保持一致,这表明其广泛适用性.
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