在一般人群中,基于机器学习的突发心脏病死亡预测,使用电子健康记录数据
Younès Youssfi1,2,3, Richard Chocron1,2,4, Thomas Laurenceau1,2,4
1Université Paris Cité, Inserm, PARCC, F-75015 Paris, France.
European journal of preventive cardiology
|March 6, 2026
概括
一个新的机器学习模型使用电子健康记录预测突然心脏死亡 (SCD) 风险. 这种人工智能工具可以识别一般人群中的高风险个体,用于针对性的预防策略.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 突然心脏死亡 (SCD) 通常发生在没有已知的危险因素的个体中,这使得预测具有挑战性.
- 目前的查方法不足以在一般人群中识别有风险的个人.
研究的目的:
- 开发和验证用于预测SCD风险的机器学习模型.
- 在一般人群中识别高风险SCD的个体.
主要方法:
- 通过使用电子健康记录 (EHR) 数据进行机器学习模型的训练和验证.
- 包括超过1700万份处方和160万份诊断的药物处方和医院诊断数据.
- 在巴黎和西雅图的两个外部队伍上验证了模型.
主要成果:
- 该模型在验证队列中达到0.81的AUC,在地理队列中达到0.66.
- 该预测模型分别在巴黎和西雅图的最高十分位数中确定了26%和33%的SCD病例.
- 非心血管因素也为SCD预测做出了贡献,该模型是特定于SCD,而不是心肌梗塞.
结论:
- 一个人工智能驱动的预测模型可以在一般人群中识别高风险的SCD个体.
- 这种与EHR集成的模型可以帮助风险分层,并优先考虑预防性干预.
- 该工具支持更有针对性地分配心血管健康资源.
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