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通过机器学习在多样化的人群中改善心肌梗塞的分类

Alicia Chen¹, Chuan Hong², Yuk Lam Ho^1,3

¹Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA.

American journal of epidemiology

|October 7, 2025

概括

像PheCAP这样的机器学习 (ML) 表型管道,与ICD代码相比,在电子健康记录 (EHR) 中改善了心肌梗塞 (MI) 的分类,特别是在多样化的人口中.

科学领域:

医疗信息学医疗信息学
生物医学数据科学生物医学数据科学
临床流行病学临床流行病学

背景情况:

电子健康记录 (EHR) 数据对于基于机器学习 (ML) 的表型分类至关重要.
在不同人群中ML算法的性能,特别是用于分类心肌梗塞 (MI),需要进一步研究.

研究的目的:

为了比较基于ICD的算法的性能与ML表型管道 (PheCAP) 的MI分类.
评估差异化算法性能对复制MI风险因素研究结果的影响.
为了评估一般和自我报告的黑人退伍军人群体的表现.

主要方法:

使用了退伍军人卫生管理局 (VHA) 的2002-2019年 (n=11,523,175) 的EHR数据.
使用基于规则的ICD算法和包含自然语言处理的PheCAP ML管道进行分类MI.
经过训练和验证的算法与403名查表审查的退伍军人的黄金标准相对应,对自我报告的黑人个人进行过量抽样.

主要成果:

ICD算法显示了高正预测值 (PPV),但敏感性低 (所有种族:PPV 0.97,感应 0.17;黑人:PPV 0.94,感应 0.24).
PheCAP获得了良好的PPV,灵敏度明显更高 (所有种族:PPV0.90,感觉0.66;黑人:PPV0.81,感觉0.79).
与ICD算法相比,PheCAP增强了复制MI风险因素研究结果的能力.

结论:

与传统的基于ICD的方法相比,ML表型管道如PheCAP在EHR数据中为MI分类提供了更好的灵敏度.
准确的MI表型对于了解疾病风险因素和改善临床研究至关重要,特别是在多种不同患者群体中.
PheCAP显示了使用EHR数据进行更强大的流行病学研究的潜力.

关键词:

算法算法是一种算法.电子健康记录是电子健康记录.流行病学流行病学机器学习是机器学习.心肌梗塞的心脏病发作

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