在420万名美国退伍军人中,高维度预测自杀风险使用集体转移学习
Sayera Dhaubhadel1, Kumkum Ganguly1, Ruy M Ribeiro1
1Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Scientific reports
|January 20, 2024
概括
这项研究引入了一种集合转移学习方法,用于使用电子医疗记录预测退伍军人的自杀风险. 该模型在预测大量退伍军人的自杀和相关结果方面取得了很高的准确性.
科学领域:
- 计算精神病学是一种计算精神病学.
- 机器学习在医疗保健中的应用
- 退伍军人健康研究 退伍军人健康研究
背景情况:
- 预测自杀风险对于退伍军人精神卫生保健至关重要.
- 电子医疗记录 (EMR) 为风险预测提供了丰富的数据来源.
- 现有的方法可能无法充分利用复杂的纵向EMR数据.
研究的目的:
- 开发和验证一个集体转移学习模型来预测自杀风险.
- 使用退伍军人事务 (VA) 电子医疗记录 (EMR) 进行自杀预测.
- 评估大型潜在退伍军人队列上的模型性能.
主要方法:
- 在7.5年内使用横截面和纵向EMR变量训练了多种基础模型.
- 通过微调七个基本模型,创建组合模型.
- 在培训中采用了追溯嵌套病例控制 (Rcc) 研究设计.
- 在420万退伍军人的前队伍中得到验证.
主要成果:
- 组合模型的c-统计结果为2年自杀风险为0.73和组合结果为0.83.
- 在各种子组 (风险层,年龄,性别,种族,医疗保健利用率) 中表现出良好的校准.
- 从线性Rcc模型中确定了包括心理健康诊断,物质使用和生理指标在内的关键预测因素.
结论:
- 合并转移学习是有效的预测使用EMR数据的退伍军人自杀风险.
- 开发的模型显示出强大的预测性能和通用性.
- 这些发现支持使用先进的机器学习技术在老兵人群中主动提供心理健康护理.
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