机器学习模型对心力衰竭再入院的预测性表现:系统性审查
Nader Alnomasy1, Petelyne Pangket2, Romeo Mostoles3
1Medical Surgical Department, College of Nursing, University of Hail, Ha'il 81451, Saudi Arabia.
Biomedicines
|September 27, 2025
概括
机器学习有效地预测心力衰竭 (HF) 再入院. 监督的ML模型表现出强的性能,有助于减少患者住院和医疗保健成本.
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:
- 心力衰竭 (HF) 患者面临高回收率,增加医疗保健支出.
- 预测型 HF 再入院的建模对于成本控制和改善患者管理至关重要.
研究的目的:
- 系统地审查机器学习 (ML) 应用程序,以预测心力衰竭的再入院.
- 评估HF再接收预测中使用的ML模型的性能和特征.
主要方法:
- 在多个医疗数据库中按照PRISMA指南进行了系统审查.
- 数据提取和选由三个审查员独立进行.
- 包括使用机器学习算法用于预测心力衰竭再入院的研究.
主要成果:
- 分析了来自六个国家的22项研究,检查了各种再接收时间框架 (30天至3年).
- 监督学习算法显示出高预测准确度,曲线下的面积 (AUC) 值在0.70到0.99.9之间.
- 没有监督的算法实现了0.69和0.72.7之间的AUC.
结论:
- 机器学习模型可以准确地预测心力衰竭在不同时间间隔内的住院情况.
- 由临床知识增强的监督的ML方法显示出改善预测模型性能的希望.
- 医疗保健提供者和数据科学家之间的加强合作对于开发优秀的预测模型来改善患者的结果和降低成本至关重要.
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