使用电子健康记录元数据来预测退伍军人的住房不稳定性
Rafael Zamora-Resendiz1, David W Oslin2, Dina Hooshyar3
1Applied Mathematics & Computational Research Division, Lawrence Berkeley National Laboratory, US Department of Energy, Berkeley, CA, United States.
机器学习模型可以识别退伍军人的住房不稳定性,改善无家可归风险的人的早期检测. 这种方法提高了当前查工具遗漏的个体识别的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 健康的社会决定因素
背景情况:
- 住房不稳定是与无家可归相关的主要压力因素.
- 早期识别风险人群对于有针对性的护理至关重要.
- 目前的查方法可能会错过一些经历住房不稳定的退伍军人.
研究的目的:
- 开发和评估机器学习模型,用于分类与住房不稳定相关的患者结果.
- 改进对有无家可归风险的退伍军人进行识别.
- 提高VA无家可归者查临床提醒 (HSCR) 的有效性.
主要方法:
- 使用后勤回归和随机森林模型.
- 分析了18个月的患者记录活动.
- 基于住房不稳定性预测风险的分类患者结果.
主要成果:
- 机器学习模型有效地分类住房不稳定事件.
- 在预测风险的前1%中,积极反应的可能性是预测风险的前1%的34倍.
- 在预测风险的前1%内,检测虚假阴性结果的可能性为1/4.
结论:
- 数据驱动的机器学习可以在不依赖专家策划的变量的情况下识别住房不稳定性.
- 这种方法有可能改善退伍军人住房不稳定的检测.
- 这些发现表明,有更准确的方法来识别HSCR遗漏的退伍军人.
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