开发和验证神经网络生存预测模型用于缺血性心脏病
Peter C Holm1, Amalie D Haue1,2, David Westergaard1,3,4
1Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
Cardiovascular diabetology
|January 28, 2026
概括
使用广泛的电子健康记录数据的机器学习模型显著改善了对缺血性心脏病患者冠状动脉血管造影后所有原因死亡率的预测.
科学领域:
- 心脏病学 心脏病学
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 目前的缺血性心脏病风险模型是有限的,使用很少的已知因素.
- 电子健康记录 (EHR) 为改善预后提供了更广泛的功能集.
研究的目的:
- 开发和验证一个机器学习模型,集成全面的EHR数据,用于预测冠状动脉血管学后死亡率.
- 将新模型的性能与现有风险评分进行比较.
主要方法:
- 使用丹麦EHR数据进行开发和冰岛EHR数据进行外部验证的回顾性研究.
- 一个基于神经网络的离散时间生存模型 (PMHnet) 被训练了来自EHR的584个特征.
- 使用依赖时间的AUC (tdAUC) 和Brier分数来评估性能,并与GRACE2.0.0进行基准测试.
主要成果:
- 在5年内,PMHnet表现出强大的预测性能, tdAUC值从0.82到0.88不等.
- 该模型在冰岛的外部验证数据上保持了类似的性能.
- 在歧视方面,PMHnet显著优于GRACE2.0评分和更简单的模型.
结论:
- 复杂,功能丰富的机器学习模型擅长预测缺血性心脏病的全因死亡率.
- 这些先进的模型可以帮助临床医生和患者做出治疗和管理决策.
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