测试正则表达式搜索和机器学习模型,以从初级保健电子病历数据中确定住房不稳定性和低收入状态,在安大略省多伦多,安大略省
Stephanie Garies1,2,3, Christopher Meaney4, Karen Weyman5,4
1Department of Family Medicine, University of Calgary, Calgary, AB, Canada. sgaries@ucalgary.ca.
BMC public health
|October 1, 2025
概括
机器学习和正则表达式搜索在识别患者的准确性较差.
科学领域:
- 医疗信息学 医疗信息学
- 健康的社会决定因素
- 医疗保健中的机器学习
背景情况:
- 健康的社会决定因素 (SDoH),包括住房和收入,对于公平的健康规划至关重要.
- 初级保健提供者往往缺乏这些SDoH的患者数据.
- 电子医疗记录 (EMR) 数据为这些信息提供了一个潜在的来源.
研究的目的:
- 评估机器学习 (ML) 和正则表达式 (REGEX) 搜索方法,从EMR数据中提取住房不稳定性和低收入状态.
- 将ML和REGEX方法的性能与患者报告的数据进行比较.
主要方法:
- 利用来自加拿大学术家庭健康团队的非识别EMR数据.
- 开发了一个REGEX分类器和一个监督ML模型 (XGBoost).
- 根据健康公平问卷进行验证,作为参考标准.
主要成果:
- 无论是ML方法还是REGEX方法在识别住房不稳定和低收入方面都表现出低灵敏度.
- 通过ML方法,对住房 (83.3%) 和收入 (72.9%) 均显示出令人满意的正预测值 (PPV).
- REGEX对住房 (29.0%) 的敏感度更高,但对收入 (17.6%) 的PPV更低.
结论:
- 目前使用EMR数据的ML和REGEX方法在临床识别需要住房或经济支持的患者方面存在局限性.
- 虽然ML与PPV显示出希望,但整体性能不足以提供临床实用性.
- 继续依赖患者报告的SDoH数据仍然至关重要.
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