机器学习用于预测心力衰竭后的风险,使用行政卫生数据访问急诊室或入院
Nowell M Fine1, Sunil V Kalmady2,3, Weijie Sun2,4
1Division of Cardiology, Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary Alberta.
PLOS digital health
|October 25, 2024
概括
机器学习准确地预测了心力衰竭患者在急诊室访问或住院后的心脏衰竭患者的不良结果. 这种先进的风险分层改善了患者的护理,并减少了再入院.
科学领域:
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 患有心力衰竭 (HF) 的患者在急诊室 (ED) 访问或住院后面临高风险的不良事件.
- 对这些患者进行准确的风险分层仍然是一个重大的临床挑战.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于识别在急救诊所或HF住院后出现不良结果的高风险患者.
- 将ML方法的性能与用于HF风险预测的传统物流回归进行比较.
主要方法:
- 利用来自加拿大阿尔伯塔省 (2002-2016) 的大型区域行政医疗保健数据库.
- 应用深度特征合成,从各种健康数据源中提取特征.
- 训练并评估了一个梯度增强算法 (CatBoost) 与后勤回归相对应.
- 使用接收器操作特征曲线 (AUC-ROC) 下面的面积来评估模型性能.
主要成果:
- 在30天和1年的随访中,CatBoost模型显示了相对于综合终点 (HF ED访问,HF再入院或死亡) 的后勤回归的优异预测性能.
- 在30天后,CatBoost实现了74.16%的AUC-ROC,而后勤回归的AUC-ROC为62.25%.
- 一年后,CatBoost的AUC-ROC达到76.80%,而后勤回归的AUC-ROC为69.52%. 仅仅针对所有原因的死亡率,CatBoost的AUC-ROC值达到83.21% (30天) 和85.73% (1年).
结论:
- 基于机器学习的建模,通过深度特征合成来增强,为心力衰竭患者提供了卓越的风险分层.
- 这种方法有效地识别了ED访问或住院后面临不良结果风险的患者.
- 这些发现凸显了ML在改善大型医疗保健系统内心力衰竭患者护理方面的潜力.
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