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在ICU中基于AI的评分系统的外部验证:系统性审查和元分析

Patrick Rockenschaub^1,2,3, Ela Marie Akay¹, Benjamin Gregory Carlisle⁴

¹CLAIM - Charité Lab for AI in Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.

BMC medical informatics and decision making

|January 6, 2025

概括

对重症监护室 (ICU) 患者的机器学习 (ML) 风险评分的外部验证并不常见,在新数据集中,性能通常会下降. 这凸显了人们对临床实践中当前ML模型可靠性的担忧.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
临床预测模型临床预测模型

背景情况:

机器学习 (ML) 模型越来越多地用于预测重症监护室 (ICU) 中的临床恶化.
然而,由于过度装配,这些模型在新的医院环境中往往表现不佳.
在临床实施之前,外部验证对于评估基于ML的风险评分可靠性至关重要.

研究的目的:

系统地审查基于ML的风险评分在ICU患者恶化预测中的外部验证的频率.
评估外部验证对这些ML模型性能的影响.

主要方法:

在MEDLINE,Web of Science和arXiv进行了系统的文献搜索,寻找使用ML预测ICU患者病情恶化的研究.
包括的研究是在2023年12月之前以英语出版的.
总结了外部验证的频率,并使用线性混合效应模型分析了性能变化 (接收器操作特征曲线下的区域 - AUROC).

主要成果:

在包含的572项研究中,只有14.7%接受了外部验证,到2023年将增加到23.9%.
在使用开源数据的研究中,外部验证更常见,特别是MIMIC和eICU数据集.
平均而言,AUROC在外部数据中下降了-0.037,近一半的研究显示性能下降大于0.05.

结论:

在ICU中对ML风险得分的外部验证很少发生,尽管它的趋势越来越强.
在外部数据集中观察到的性能下降引起了人们对许多拟议的ML分数的概括性和可靠性的担忧.
过度依赖有限的数据集和独家使用AUROC阻碍了可靠的解释和验证.

关键词:

急性恶化的情况.电子健康记录是电子健康记录.外部验证的验证方法在重症监护病房的重症监护病房.机器学习是机器学习.

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