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对脑卒中后死亡率的偏差风险评估机器学习预测模型:系统审查

Nicole Maria Radley¹, Ian Soh², Abdelrahman M Saad³

¹Imperial College London, United Kingdom.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

|March 15, 2025

概括

机器学习模型显示出预测中风死亡率的前景,但偏差的高风险限制了临床使用. 未来的研究必须减少偏见,以提高中风预测可靠性.

科学领域:

神经学神经学
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

卒中是全球死亡率和残疾的主要原因之一.
精确预测中风后死亡率对于患者管理至关重要.
机器学习 (ML) 提供了增强中风患者死亡率预测的潜力.

研究的目的:

系统地审查和评估用于中风后死亡率预测的ML模型中的偏差风险.

主要方法:

按照PRISMA和CHARMS指南进行系统审查.
在科克兰图书馆,Scopus,PubMed和科学网上搜索.
包括9项研究,共计669,424名患者.

主要成果:

ML模型实现了高预测性能 (AUC为0.81-0.95).
研究使用了各种数据集 (公共和医院),随访期从7天到18个月.
所有纳入的研究都显示出高整体偏差风险.

结论:

机器学习模型具有预测中风后死亡率的巨大潜力.
偏差的高风险目前阻碍了这些模型的临床实施.
未来的研究应该优先考虑减少偏见,以提高基于ML的中风死亡率预测的可靠性和适用性.

关键词:

在这里,我们可以看到AIAIAI.机器学习是机器学习.死亡率死亡率死亡率偏见的风险偏见的风险一次性中风,中风.

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