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一个可解释的机器学习模型用于入住重症监护室的骨折患者的并发症风险分层:一个多中心研究

Xuelong Liang¹, Weijie Zhao², Weigui Liufu³

¹Trauma Orthopedics Department, Maoming People's Hospital, Maoming, Guangdong 525000, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China.

Archives of gerontology and geriatrics

|November 18, 2025

概括

临床医生现在可以使用可解释的机器学习模型预测ICU骨折患者的严重并发症负担. 这种工具有助于早期识别高风险个体,以提供个性化护理.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
关键护理医学关键护理医学

背景情况:

患有慢性伴随疾病的ICU中创伤性骨折患者的恢复速度较慢,死亡率更高.
根据年龄调整的查尔森并发症指数 (aCCI) 量化了并发症负担.
缺乏预测高aCCI在ICU入院骨折患者的工具.

研究的目的:

开发和外部验证可解释的机器学习模型,以预测ICU骨折患者的严重并发症负担 (高aCCI).
为临床医生提供一种工具,以便在ICU入院时进行早期风险分层.

主要方法:

使用MIMIC-IV的3763例成年骨折病例开发了一种机器学习模型,确定了9个关键预测因子.
在评估了11个具有超参数调整和交叉验证的候选模型后,选择了XGBoost算法.
使用SHAP实现了模型解释性,并在两个中国中心进行了外部验证.

主要成果:

在XGBoost模型中,强烈的内部区分 (AUROC = 0.84) 和校准.
外部验证显示了强大的性能,AUROC值为0.88和0.83.
开发了一个交互式网络计算器,提供实时,患者特定的风险解释.

结论:

一个可解释的XGBoost模型准确地预测了ICU骨折患者的高aCCI,并在机构中概括.
开发的网络工具有助于早期识别高风险患者.
该工具支持个性化的管理和高效的资源分配在重症监护机构.

关键词:

这就是MIMIC-IV.这就是 SHAP SHAP 的意思.在XGBoost中使用.年龄调整后的查尔森并发症指数.伴随性疾病的负担.通过外部验证.重症监护病房的重症监护病房是一个重症监护病房.机器学习是机器学习.预测模型是一个预测模型.创伤性骨折是一个创伤性骨折.

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