开发和验证可计算的社会现象类型,以满足与健康相关的社会需求
Megan E Gregory1, Suranga N Kasthurirathne2, Tanja Magoc3
1Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, United States.
JAMIA open
|January 8, 2025
概括
使用电子健康记录 (EHR) 为与健康相关的社会需求 (HRSN) 开发可计算的表型 (CP) 显示了机器学习 (ML) 的公平表现,但突出了种族差异. 需要进一步的研究来提高准确性和公平性.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据科学 临床数据科学
- 健康的社会决定因素
背景情况:
- 准确测量与健康相关的社会需求 (HRSNs) 是一个挑战.
- 电子健康记录 (EHR) 为识别HRSN提供了一个潜在的数据源.
- 开发可计算的表型 (CP) 可以自动化HRSN识别.
研究的目的:
- 通过使用EHR数据,开发和验证粮食不安全,住房不稳定,金融不安全和交通障碍的CP.
- 为了比较基于规则和机器学习 (ML) 的CP开发方法.
- 评估这些CP的性能和潜在偏差.
主要方法:
- 收集了HRSN调查数据作为参考标准,来自3个卫生系统的1550名患者.
- 开发了人类定义的基于规则的CP和使用78个EHR功能监督的ML (XGBoost) CP.
- 评估CP使用灵敏度,特异性,正预测值和曲线下的面积 (AUC),比较性能指标.
主要成果:
- 63%的患者报告至少有一个HRSN.
- ML CPs表现出公平的表现 (AUC 0.680.75),显著超过基于规则的CP (AUC 0.520.68).
- 根据种族/种族,MLCP的表现不同,非西班牙裔白人患者的AUC更高. 关键特征包括遭遇频率和医疗补助保险.
结论:
- 对HRSN的监督MLCP显示出有希望的患者需要进一步查,但需要改进.
- 现有的ML CPs在种族/种族群体之间表现差异.
- 未来的工作应该整合地理空间数据和自然语言处理,以提高CP的准确性和公平性.
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