深度序列神经网络模型改善了美国退伍军人自杀企图风险的分层
Carianne Martinez1,2, Drew Levin1, Jessica Jones1
1Sandia National Laboratories, Albuquerque, NM 87185, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 28, 2023
概括
深度神经网络使用电子健康记录准确地预测退伍军人的自杀企图风险. 可解释性方法识别高风险个体和针对性干预的关键风险因素.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心理健康研究 心理健康研究
背景情况:
- 自杀企图对退伍军人人口构成重大风险.
- 现有的自杀风险预测模型需要改进,以提高准确性和可操作的见解.
研究的目的:
- 开发和验证深度神经网络 (DNN) 以使用纵向电子健康记录 (EHR) 数据预测美国退伍军人的自杀企图风险.
- 利用本地可解释性技术来理解DNN预测,并确定自杀企图的关键风险因素.
主要方法:
- 经过训练和测试,DNN模型使用了大约50万名美国退伍军人的EHR数据.
- 数据包括人口信息,诊断代码,处方数据和程序代码.
- 沙普利增量解释 (SHAP) 值是为了模型的解释性而计算的.
主要成果:
- 与逻辑和线性回归模型相比,DNN在预测自杀企图方面表现优越.
- 一个卷积神经网络 (CNN) 模型实现了0.54的正预测值 (PPV),用于预测顶部0.1%风险级别的12个月内自杀企图.
- 可解释性方法确定了高风险退伍军人的有意义的子组和自杀企图风险的关键决定因素.
结论:
- 深度学习方法为增强退伍军人自杀风险预测模型提供了巨大的潜力.
- 可解释性方法可以指导制定有针对性的干预策略,并为临床决策提供信息.
- 该研究强调了人工智能在识别高风险个体和了解导致退伍军人自杀企图的因素方面的实用性.
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