描述那些没有被归类为高自杀风险的退伍军人自杀死者
Maxwell Levis1,2, Monica Dimambro1, Joshua Levy3
1White River Junction VA Medical Center, White River Junction, VT, USA.
Psychological medicine
|September 16, 2024
概括
大多数死于自杀的退伍军人并没有被确定为高风险. 这项研究发现,机器学习模型可能不足以代表少数群体,影响退伍军人的自杀预防工作.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 心理健康研究 心理健康研究
背景情况:
- 退伍军人事务部 (VA) 的自杀预防工作主要集中在高风险患者身上.
- 绝大多数自杀的退伍军人不符合针对性服务的高风险标准.
- 这项研究检查了因自杀而死亡但未被归类为高风险的VA患者.
研究的目的:
- 分析因自杀而死亡的非高风险退伍军人的特征.
- 识别风险子组之间的人口,诊断和社会差异.
- 评估机器学习风险预测模型中的潜在偏差.
主要方法:
- 分析2017-2018年国家VA自杀数据.
- 通过机器学习算法识别为高风险患者的排除.
- 主要组件分析将剩余的患者分为中度和低风险组.
- 调查人口统计,服务利用,诊断和健康的社会决定因素的差异.
主要成果:
- 高风险患者是年轻的,白人,未婚,无家可归,有更多的心理健康诊断.
- 中等和低风险患者是年长,已婚,黑人,美洲原住民/太平洋岛民,有更多的身体健康诊断.
- 与高风险组相比,低风险组的患者表现出更多的缺失数据.
结论:
- 这些发现提高了对未经研究的非高风险自杀死者的理解.
- 依赖当前的机器学习模型可能会延续由于种族/民族少数群体代表性不足而导致的偏见.
- 需要完善风险预测模型,以便在VA系统中更公平地预防自杀.
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