开发和验证可解释的机器学习模型,用于入住重症监护病房的 triage 患者
Zheng Liu1, Wenqi Shu1, Hongyan Liu1
1Department of Emergency, The First Hospital of China Medical University, Shenyang, China.
PloS one
|February 18, 2025
概括
机器学习模型使用患者数据,包括生命体征和病史,准确预测重症监护室 (ICU) 的入院情况. 这些可解释的模型改进了用于紧急分拣决策的传统方法.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 医疗保健服务研究 医疗服务研究
背景情况:
- 预测重症监护室 (ICU) 入院对于优化患者护理和紧急部门的资源配置至关重要.
- 传统的分类方法可能无法完全捕捉患者敏度的复杂性,需要先进的预测工具.
研究的目的:
- 开发和验证可解释的机器学习 (ML) 模型,用于预测 triaged 紧急病人的ICU入院情况.
- 将各种患者数据纳入的ML模型的性能与传统分拣指标进行比较.
主要方法:
- 从密集护理IV (MIMIC-IV) 数据库中对189,167名急诊患者的分析.
- 三种模型的开发:基于紧急严重性指数 (ESI),基于生命体征,以及基于综合数据 (生命体征,人口统计,病史,首席投诉).
- 使用AUC,F1评分,校准曲线和决策曲线进行评估;通过夏普利增量解释 (SHAP) 进行解释.
主要成果:
- 综合性ML模型 (模型3) 与ESI (模型1) 和只有生命体征 (模型2) 的模型相比,显示出更高的预测性能 (AUC).
- 梯度提升和物流回归在模型3中实现了最高的AUC (0.81),超过了天真的贝叶斯和随机森林.
- 可解释的ML模型提供了关于ICU入院的关键预测因素的见解.
结论:
- 一个可解释的ML分拣模型整合了生命体征,人口统计,病史和首席投诉,比传统模型更有效地预测ICU入院.
- 可解释的ML在紧急分拣过程中促进了加强的临床决策.
- 这些发现支持将先进的ML工具集成到临床工作流程中,以改善患者管理.
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