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开发和验证基于机器学习的预测模型,用于在重症监护病房内预测心脏骤停的结果

Peifeng Ni^1,2, Sheng Zhang³, Gensheng Zhang⁴

¹Department of Critical Care Medicine, Zhejiang University School of Medicine, No. 866 Yuhangtang Road, Hangzhou, 310000, Zhejiang, China.

Scientific reports

|March 14, 2025

概括

机器学习准确地预测了心脏骤停 (CA) 幸存者的住院死亡率. 自发循环恢复 (ROSC) 后72小时开发的CatBoost模型显示了对患者结局有希望的结果.

科学领域:

临界护理医学临界护理医学
生物医学信息学生物医学信息学
医疗保健中的机器学习

背景情况:

心脏骤停 (CA) 给全球健康带来了巨大的负担,往往预后不利.
预测CA幸存者的住院死亡率对于临床决策和资源分配至关重要.

研究的目的:

开发和验证一种可解释的机器学习 (ML) 模型,用于预测CA患者的住院死亡率,这些患者在事件发生后72小时内存活.
确定这一患者队列中死亡率的关键预测因素.

主要方法:

利用密集护理IV (MIMIC-IV) 数据库的医疗信息中心和四家三级医院的外部数据进行模型开发和验证.
训练并评估了11个ML算法,使用自发循环 (ROSC) 恢复后72小时收集的数据.
采用递归特征消除 (RFE) 来识别重要变量,并构建了一个紧的预测模型. 使用SHAP图表来实现模型的可解释性.

主要成果:

CatBoost算法在预测72小时内住院死亡率方面表现出卓越的表现.
最终的模型在内部验证中达到0.86的曲线下面面积 (AUC),在外部验证中达到0.76.
确定了11个关键变量,有助于一种节且有效的预测工具.

结论:

72小时CatBoost模型显示了预测心脏骤停幸存者的住院死亡率的巨大潜力.
该模型的可解释性,在SHAP分析的帮助下,提高了其临床适用性.
建议进行进一步的优化和验证,以完善模型的预测准确性和临床实践中的实用性.

关键词:

心脏骤停是因为心脏停止了.显而易见的提升.这是一个MIMIC-IV数据库.机器学习是机器学习.死亡率死亡率死亡率沙普利添加剂的解释

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