机器学习方法是否有效地检测未被诊断的心房动在初级保健机构使用电子健康记录? 一个系统的审查
Mhd Diaa Chalati1,2, Chetan Shirvankar2,3, Genevieve Gore4
1Department of Family Medicine, McGill University, Montreal, Quebec, Canada.
PLOS digital health
|October 14, 2025
概括
使用电子健康记录 (EHR) 的机器学习 (ML) 模型显示出在初级保健中检测心房动 (AF) 的前景. 然而,有限的验证和高偏差限制了它们目前的临床使用.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 心房动 (AF) 显著增加中风,心力衰竭和死亡风险.
- 目前的AF查方法往往是无效的,在初级保健机构的适用性有限.
- 电子健康记录 (EHR) 提供了使用机器学习 (ML) 进行自动化AF检测的潜力,但其现实世界的有效性需要澄清.
研究的目的:
- 系统地审查基于EHR的ML模型在初级保健中检测AF的有效性,质量和适用性.
- 将ML模型的性能与用于识别未诊断的AF的标准护理进行比较.
- 评估这些模型对患者,医疗保健提供者和医疗保健系统的影响.
主要方法:
- 系统审查遵循乔安娜·布里格斯研究所和PRISMA指南,搜索七个数据库,截至2023年5月.
- 包括比较基于EHR的ML模型与初级保健机构的标准护理进行成人AF检测的研究.
- 使用CHARMS评估数据,通过PROBAST和MI-CLAIM检查清单评估偏差风险和适用性.
主要成果:
- 包括16项研究,主要是回顾性队列 (87%). 随机森林是最常见的ML模型 (43%).
- 只有25%的模型经过了外部验证,53%的模型具有高偏差风险.
- 模型歧视 (AUROC) 从0.71到0.948不等,50%的表现优于对照组;将ML与临床工具相结合提高了表现.
结论:
- 基于EHR的ML模型显示了与标准方法相比,在初级保健中增强AF检测的潜力.
- 目前临床适用性受到外部验证不足,显著偏差和不一致的表现的阻碍.
- 未来的研究必须专注于严格的外部验证,临床试验和整合常见的初级保健预测因素.
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