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机器学习用于预测老年人中的突然心脏病死亡,使用基于社区的电子健康记录

Yue Li^1,2,3,4, Zihan Mei^1,2,3, Guolei Sun⁵

¹School of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.

BMC public health

|October 17, 2025

概括

机器学习模型有效地预测使用电子健康记录 (EHR) 的老年人突然心脏死亡 (SCD) 风险. 随机生存森林模型表现出卓越的性能,识别了针对性干预的高风险个体.

科学领域:

心脏病学心脏病学
生物统计学生物统计学
医疗信息学医疗信息学

背景情况:

机器学习 (ML) 模型在心血管疾病风险预测方面表现有前途.
使用长期电子健康记录 (EHR) 来预测突然心脏死亡 (SCD) 风险的ML的实用性需要进一步阐明.
这项研究重点是开发和验证ML模型,用于在社区居住的老年人中预测SCD风险.

研究的目的:

开发和验证用于预测突然心脏死亡 (SCD) 风险的机器学习模型.
利用来自老年人的6年初级保健电子健康记录 (EHR) 数据.
识别高风险个体进行有针对性的干预和个性化护理.

主要方法:

使用了济南研究队列 (30,535名年龄≥65岁的个人).
采用Cox比例危险和随机生存森林 (RSF) 来进行特征选择和模型装配.
模型通过5倍交叉验证进行了优化,并在单独的测试数据集上进行了外部验证.

主要成果:

RSF模型的C指数为0.820 (培训) 和0.757 (测试),表现优于其他模型.
校准图表显示,大多数SCD事件发生在预测风险分数最高的个体中.
通过使用总结图表,提高了RSF模型的可解释性.

结论:

机器学习模型通过使用基于社区的EHR显著改善了SCD风险预测.
开发的风险模型可以识别高风险的老年人进行有针对性的干预.
未来的研究应该将这种模型整合到初级保健工作流程中,以进行现实世界的评估.

关键词:

欧洲共同体欧洲共同体欧洲共同体年长的老人.电子健康记录是电子健康记录.机器学习机器学习风险预测风险预测突然的心脏病死亡.

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