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通过可解释的机器学习算法对患有败血症结合肺癌的患者进行个性化的ICU死亡率评估:基于人口的研究和外部验证队列研究

Hongjie Tang¹, Hairong Hao², Yue Han¹

¹Department of Critical Care Medicine, Xuzhou Central Hospital, Xuzhou, Jiangsu, China.

Frontiers in oncology

|October 17, 2025

概括

机器学习模型准确地预测了肺癌血症患者的ICU死亡率. 这有助于为这个高风险群体提供个性化的预后.

科学领域:

在瘤学瘤学.
关键护理医学关键护理医学
数据科学数据科学数据科学

背景情况:

败血症是导致死亡的主要原因,特别是在免疫功能低下的肺癌患者中.
准确预测重症监护室 (ICU) 死亡率对于管理这些复杂病例至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测患有败血症和肺癌的ICU患者的死亡率.
确定影响死亡率预测的关键临床变量.

主要方法:

使用密集护理IV (MIMIC IV) 数据库的医疗信息商城来获取患者数据.
使用6ML算法对变量选择和递归特征消除 (RFE) 进行后勤分析.
使用13ML算法构建预测模型,通过AUC,准确性和SHAP值等指标评估性能.

主要成果:

CatBoost模型表现出卓越的性能,在训练 (0.931),内部验证 (0.698) 和外部验证 (0.794) 中实现了最高的曲线下面面积 (AUC).
牛津急性严重性疾病评分 (OASIS) 被确定为通过SHAP分析预测ICU死亡率的最有影响的因素.
总共有1096名来自MIMIC IV的患者和251名来自外部验证组的患者被分析.

结论:

开发了准确可靠的ML模型,用于预测患有败血症的肺癌患者的ICU死亡率.
这些模型为更精确,个性化的预后评估提供了基础.

关键词:

ICU 死亡率 ICU 死亡率这就是MIMIV-IV.肺癌是一种肺癌.机器学习是机器学习.这是一种血症.

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