可解释的机器学习用于识别ICU回收风险在具有概率规则的子组中
Lincen Yang1, Siri L van der Meijden2,3, Sesmu M Arbous2,4
1Leiden Institute of Advanced Computer Science, Leiden University, 2333 CC, Leiden, The Netherlands.
Journal of the American Medical Informatics Association : JAMIA
|October 29, 2025
概括
一个新的基于规则的模型,真正无序的规则集 (TURS),识别了重症监护室 (ICU) 患者子组,具有明显的再接收风险. 这有助于临床医生优化出院规划和资源配置,以获得更好的患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的机器学习
背景情况:
- 估计重症监护室 (ICU) 患者再接收风险对于资源管理和预防早出院至关重要.
- 当前的机器学习模型往往缺乏解释性或无法识别具有不同再入院风险的不同患者子组.
研究的目的:
- 引入一种新的基于规则的模型,真正无序的规则集 (TURS),以解决现有方法的局限性.
- 揭示ICU人群中异质的再接收风险和小组级患者特征.
主要方法:
- 从2011年1月到2020年1月在莱登大学医学中心对ICU入院数据进行了TURS模型的训练.
- 分析了患者特征和特征变量对每个确定子组的再接收风险的影响.
主要成果:
- TURS确定了具有异质特征分布和重要性的患者子组,为ICU出院计划提供了可操作的见解.
- 实现了优于其他基于规则的模型的预测性能 (ROC-AUC 70.5%),具有简洁的模型复杂性 (5 条规则,平均长度 2).
结论:
- 亚组分析显示了显著的患者异质性,每个组的特征含义和对再接收风险的影响各不相同.
- 图尔斯模型提供了患者子组的简要概述,支持ICU出院决策并增强知识发现.
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