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机器学习用于预测重症监护室患者的死亡率:预测性能的系统审查和元分析

Hu Sun¹, Meijuan Kang², Huayu Zhang²

¹Neurology Intensive Care Unit, The Second People's Hospital of Dingxi City, Dingxi, China.

Nursing in critical care

|October 11, 2025

概括

机器学习模型显示出预测重症监护室 (ICU) 死亡率的前景,实现了0.83.3的AUC. 然而,需要进一步的前性研究来提高模型可靠性和临床整合.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
关键护理医学关键护理医学

背景情况:

预测重症监护室 (ICU) 患者的预后是具有挑战性的,但早期死亡率预测对于及时干预至关重要.
缺少用于ICU死亡率预测的机器学习 (ML) 模型的全面综合.

研究的目的:

系统地审查有关ML模型的文献,以预测ICU患者的死亡率.
进行元分析,总结这些模型的聚合性能估计.

主要方法:

2014年1月1日至2024年12月10日期间发表的研究的系统审查和元分析.
在PubMed,科克伦图书馆,科学网和Embase进行的搜索.
两变混合效应模型元分析用于从40项纳入研究中合成预测性绩效指标.

主要成果:

分析包括了40项研究,包括317,028名患者和123个ML模型.
通常使用的ML方法包括后勤回归,随机森林和XGBoost.
接收器操作特征曲线 (AUC) 下的聚合面积为0.83 (95% CI:0.80-0.86),聚合灵敏度为0.72和特异性为0.81.

结论:

ML模型显示了预测ICU死亡率的巨大潜力.
大多数研究缺乏外部验证,依赖追溯数据增加了偏差风险.
多中心前性研究对于验证和提高基于ML的临床使用预测模型的可靠性至关重要.

关键词:

重症监护病房的重症监护病房是重症监护病房.机器学习是机器学习.这是一个元分析.死亡率死亡率预测预测预测预测系统性审查系统性审查

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