先进的机器学习算法的性能超逻辑回归在预测医院再入院:一个元分析
Ashna Talwar1, Maria A Lopez-Olivo2, Yinan Huang3
1College of Pharmacy, University of Houston, Houston, TX, USA.
Exploratory research in clinical and social pharmacy
|September 4, 2023
概括
机器学习 (ML) 模型显示,与物流回归 (LR) 相比,对30天再入院的预测有所改善. 深度学习方法在本次对美国患者数据的元分析中表现最高.
科学领域:
- 医疗信息学 医疗信息学
- 医疗人工智能 医疗人工智能
- 预测分析在医疗保健中的应用
背景情况:
- 医院再接收在医疗保健系统中构成了重大挑战.
- 准确预测30天的再入院对于患者护理和资源管理至关重要.
- 机器学习 (ML) 算法越来越多地被探索,因为它们在预测患者结果方面的潜力.
研究的目的:
- 进行元分析,评估后勤回归 (LR) 和各种机器学习 (ML) 模型的性能.
- 为了比较ML模型与LR的预测准确性,在美国患者中对30天的再入院进行预测.
- 为了确定哪些ML技术为再接收预测提供了最佳的性能.
主要方法:
- 对2015年1月至2019年12月期间发表的研究进行了电子数据库 (Medline,PubMed,Embase) 的系统搜索.
- 包括9项符合选择标准的研究,其质量使用预后研究质量 (QUIPS) 工具进行评估.
- 使用曲线下的面积 (AUC) 评估模型性能,并使用STATA 16进行随机效应元分析.
主要成果:
- 与后勤回归相比,机器学习模型在预测30天所有原因的住院再入院方面表现出了统计学上显著的改善 (AUC的平均差异:0.03).
- 小组分析显示,深度学习方法 (MD:0.06) 和神经网络 (MD:0.03) 的表现优于后勤回归.
- 此外,ML模型在预测心力衰竭相关再入院方面也表现出卓越的表现 (MD in AUC:0.04).
结论:
- 与传统的物流回归相比,机器学习为30天的医院再入院提供了增强的预测能力.
- 深度学习方法代表了预测医院再入院的最有效的ML方法.
- 这些发现支持将先进的ML技术纳入临床实践,以积极主动地管理患者.
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