整合EHR和ECG数据,用于预测中风患者心脏动
Alireza Vafaei Sadr1, Manvita Mareboina2, Diana Orabueze3
1Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA.
Bioengineering (Basel, Switzerland)
|September 27, 2025
概括
通过结合心电图 (ECG) 和电子健康记录 (EHR) 数据,可以更好地预测阳性心房动 (PAF). 在这项试点研究中,使用三分之二心电图和三分之一电子心电图比率的深度学习模型显示出卓越的准确性.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 由于其短暂的性质,难以预测心房发 (PAF).
- 目前的方法通常只使用心电图 (ECG) 或电子健康记录 (EHR) 数据.
- 整合异质数据源可能会提高预测准确性.
研究的目的:
- 为PAF预测开发和评估一个集成ECG和EHR数据的深度学习模型.
- 为了确定ECG和EHR数据贡献之间的最佳平衡,以提高预测性能.
主要方法:
- 开发了一个基于变压器的深度学习模型.
- 该模型整合了来自189名患者的12个导电图信号和47个结构化EHR变量.
- 使用5倍交叉验证,系统地改变心电图与电子心脏病记录数据输入的比率.
主要成果:
- 最优的模型配置使用了三分之二的心电图到三分之一的电子心脏记录数据比率.
- 这种多式联络方法实现了0.70的精度,0.72的灵敏度和0.87.87的特异性.
- 组合模型的性能优于单模模型,精度比仅使用EHR的方法提高了35%,比仅使用ECG的方法提高了5%.
结论:
- 通过多式联机深度学习方法将心电图和结构化电子心电图数据结合起来,可以提高PAF预测的准确性和灵敏性.
- 这一策略对识别患有PAF风险的患者有前途,特别是在密码性中风群体中.
- 需要在更大的队列中进一步验证以证实这些发现.
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