基于机器学习的回收和心力衰竭患者的死亡率预测
Maziar Sabouri1,2, Ahmad Bitarafan Rajabi2,3,4, Ghasem Hajianfar2
1Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran.
Scientific reports
|November 1, 2023
概括
机器学习模型使用常规特征准确预测心力衰竭患者的住院死亡率和30天住院再入院率. 需要进一步的数据来改善预测3个月再录取和6个月死亡率.
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) 对医疗保健系统构成重大负担,需要准确预测患者的结果.
- 预测住院死亡率,6个月死亡率和30天/90天的住院再入院对于患者管理和资源分配至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测心力衰竭患者的住院和6个月死亡率以及30天和90天的住院再入院.
- 通过使用常规患者数据来确定这些预测任务的最有效的ML方法和特征选择方法.
主要方法:
- 在737名心力衰竭患者的队列中,使用34个常规特征进行了分析.
- 数据被分为训练 (70%) 和测试 (30%) 组,并使用Z-score方法进行正常化.
- 使用Boruta,递归特征消除 (RFE) 和最小冗余最大相关性 (MRMR) 方法进行了特征选择.
- 训练了8个ML模型,通过十倍交叉验证和网格搜索优化了超参数,并使用AUC,精度,特异性和灵敏度进行了评估.
主要成果:
- RFE-LR和Boruta-LR模型在医院死亡率预测方面表现良好 (AUC分别为0.91和0.90).
- 对于30天的再住院治疗,Boruta-SVM和MRMR-LR模型显示出最好的结果 (AUC分别为0.73和0.71).
- 3个月再住院和6个月死亡率的表现不那么强大,这表明需要额外的数据.
结论:
- 使用常规特征开发了可靠的ML模型,用于预测住院死亡率和30天再住院.
- 这些模型可以帮助个性化治疗,改善决策和优化医疗资源分配.
- 建议进行进一步的研究,包括额外的数据,以提高预测准确度,以提高长期结果,如3个月再录取和6个月死亡率.
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