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可解释的机器学习模型用于预测重症监护室患者的肌肉损伤

Xiaojiang Liu¹, Guanyang Chen¹, Chenxiao Hao¹

¹Department of Critical Care Medicine, Peking University People's Hospital, Beijing, China.

Science progress

|August 25, 2025

概括

研究人员开发了一种机器学习模型,用于预测重症监护室 (ICU) 的心肌损伤. XGBoost 模型表现最好,确定了早期检测的关键预测因素.

科学领域:

危急护理医学
在医疗保健中的机器学习
心血管研究

背景情况:

在重症监护室 (ICU) 找出心肌损伤的研究不足.
早期发现心肌损伤对于重症监护机构患者的治疗结果至关重要.

研究的目的:

开发和验证机器学习模型,用于预测成年ICU患者的心肌损伤.
在ICU环境中确定与心肌损伤相关的关键临床变量.

主要方法:

追溯的队列研究,涉及7453名成人,非心脏手术患者入院于ICU (2012-2022年).
开发和比较五种机器学习模型:逻辑回归,随机森林,LASSO,支持向量机和XGBoost.
使用SHapley添加式解释 (SHAP) 进行模型解释.

主要成果:

XGBoost模型显示出最高的预测性能,曲线下的面积 (AUC) 为0. 779,准确度为0. 781.
通过XGBoost模型确定的主要预测因素包括最大心率,呼吸率,温度,最小心率,年龄和血输血.
该模型成功地区分了心肌损伤和没有心肌损伤的患者 (29%和71%的患病率).

结论:

一个基于XGBoost的机器学习模型显示了预测ICU患者心肌损伤的巨大潜力.
这种模型可以作为临床决策和早期发现心肌损伤的宝贵工具.
需要进一步的研究和临床验证,以便将这种预测工具纳入常规ICU实践中.

关键词:

重症监护室谢普利添加剂的解释机器学习心肌损伤其他心脏手术

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