评估用于预测心力衰竭死亡率和再接收的机器学习方法:元分析
Hamed Hajishah1, Danial Kazemi2, Ehsan Safaee3
1Student Research Committee, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran.
BMC cardiovascular disorders
|April 6, 2025
概括
机器学习模型显示出预测心力衰竭 (HF) 死亡率和再入院的强大潜力,改善了患者护理. 对于广泛的临床使用,解释性和整合性仍然存在挑战.
科学领域:
- 心脏病学 心脏病学
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
背景情况:
- 心力衰竭 (HF) 影响着数以百万计的人,患病率不断上升,医疗保健成本显著.
- 预测模型,特别是机器学习 (ML),为识别高风险患者提供了一个有希望的方法.
- 准确预测诸如死亡率和再入院等不良结果,对于有效的高温管理至关重要.
研究的目的:
- 系统地审查和评估ML模型在预测心力衰竭患者的死亡率和再录取方面的有效性.
- 评估ML在提高高频率患者护理和临床决策方面的潜力.
- 为了确定HF结果预测的最佳性能ML算法.
主要方法:
- 按照PRISMA 2020指南,在PubMed,Scopus和Web of Science进行系统的文献搜索.
- 对64项研究 (943,941名患者) 的元分析,评估了346个ML模型,用于HF死亡率和再接收预测.
- 使用PROBAST工具评估偏差风险,使用Egger和Begg测试评估出版偏差.
主要成果:
- 神经网络模型在死亡率预测方面获得了最高的AUC (0.808),而支持向量机则在再接收预测方面表现出色 (AUC 0.733).
- 随机森林,后勤回归和梯度提升是最常用的ML算法.
- 发现存在显著的偏差风险,主要是由于追溯数据和样本大小证明不足.
结论:
- ML模型显示了预测心力衰竭再入院和死亡率的巨大潜力,提高了预后准确性.
- 通过更好的预测,可以改善个性化患者护理,但解释性,概括性和临床整合方面的挑战需要解决.
- 精细的ML技术和强有力的监管框架对于成功的临床整合和改善HF结果至关重要.
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