监督机器学习包括环境因素,用于预测急性心力衰竭患者的医院治疗结果
Benjamin Sibilia1,2,3, Solenn Toupin1,2,3, Nabil Bouali4,5
1Service de Cardiologie, Université Paris Cité, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, 2 rue Ambroise Paré, Paris 75010, France.
European heart journal. Digital health
|March 20, 2025
概括
机器学习准确地预测急性心力衰竭患者的主要不良事件. 这种结合环境因素的模型,在改善患者治疗结果方面,优于传统的评分.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 对于急性心力衰竭 (AHF) 的传统风险评分有限.
- 机器学习 (ML) 在AHF风险分层中的潜力尚未得到充分证实.
研究的目的:
- 评估监督ML模型的可行性和准确性,以预测AHF患者在医院内发生的重大不良事件 (MAE).
- 评估将环境因素纳入用于AHF风险预测的ML模型中的情况.
主要方法:
- 一项前性多中心研究包括459名AHF患者.
- 使用随机森林 (RF) 和最小绝对收缩和选择运算符 (LASSO) 进行特征选择,开发了一个监督的ML模型.
- 住院MAE被定义为死亡,复活心脏骤停或需要协助的心脏性休克.
主要成果:
- 该ML模型确定了MAE的七个关键预测因素,包括平均动脉压和娱乐性药物使用.
- 射频模型实现了接收器运行曲线 (AUROC) 下的面积为0.82.
- 与ACUTE HF得分相比,ML模型的预测性能优于ACUTE HF得分 (AUROC 0.82与0.57).
结论:
- 开发的ML模型结合了环境变量,有效预测AHF患者的住院结果.
- 这种ML方法显示出比AHF风险分层的传统统计方法更好的性能.
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