预测美国陆军士兵过渡期无家可归的情况
Jack Tsai1, Dorota Szymkowiak2, Dina Hooshyar3
1National Center on Homelessness among Veterans, VA Homeless Programs Office, Washington, District of Columbia; School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas.
American journal of preventive medicine
|February 4, 2024
概括
这项研究使用机器学习来识别高风险无家可归的军队过渡服务成员 (TSM). 这些模型有效地针对干预措施,旨在防止服务后的无家可归.
科学领域:
- 公共卫生 公共卫生
- 军人卫生保健 军人卫生保健
- 数据科学数据科学数据科学
背景情况:
- 无家可归是过渡服务成员 (TSM) 的一个重大风险.
- 有效识别有风险的TSM对于预防性干预至关重要.
- 现有的方法可能无法充分识别所有高风险的TSM.
研究的目的:
- 开发和验证一种基于机器学习的实用方法,用于对无家可归的最高风险的陆军TSM进行分类.
- 针对最有可能在服务后经历无家可归的TSM进行预防性干预.
- 提高军人无家可归预防计划的效率和有效性.
主要方法:
- 利用了来自4790名士兵的数据,参与了在服务人员长度研究 (STARRS-LS) 中评估风险和弹性研究.
- 训练了两个机器学习模型:第一阶段 (行政/地理空间数据) 和第二阶段 (自报调查数据).
- 结果变量是过渡后12个月内无家可归.
主要成果:
- 过渡后12个月的无家可归患病率为5.0%.
- 第1阶段的模型确定了30%的TSM,占无家可归者的52%.
- 第二阶段的模型确定了所有TSM的10% (第一阶段高风险组的33%),占所有无家可归者的35%.
结论:
- 机器学习模型可以有效地识别高风险无家可归的TSM.
- 这种方法可以为预防性干预提供有针对性的宣传和评估.
- 开发的方法为减少TSM无家可归提供了切实可行的解决方案.
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