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开发和验证一种机器学习模型,用于预测老年关节骨折患者的手术前深静脉血栓形成

Xiaokang Wei¹, Zehao Yin¹, Shuqi Zhang¹

¹Department of Orthopedics, The First Hospital of Jilin University, Changchun, China.

Frontiers in medicine

|February 23, 2026

概括

使用XGBoost的机器学习模型在手术前在老年骨折患者中有效预测深静脉血栓 (DVT). 这种工具有助于进行手术前风险评估,并改善患者护理途径.

科学领域:

老年医学老年医学
心血管外科心血管外科
医疗保健中的机器学习

背景情况:

老年人部骨折是一个重大的全球健康问题.
深静脉血栓 (DVT) 是一种常见的并发症,增加了手术风险和延迟治疗.
有效的手术前风险评估对于在这个人群中管理DVT至关重要.

研究的目的:

开发和验证一种机器学习模型,用于预测老年关节骨折患者的手术前DVT.
加强手术前评估,并简化这些患者的临床护理途径.

主要方法:

对782名老年关节骨折患者进行了回顾性研究.
使用五种监督机器学习算法 (DT,XGBoost,SVM,LightGBM,LR) 开发预测模型.
模型性能使用歧视,校准和临床适用性指标进行评估,用于可解释性使用SHAP.

主要成果:

在782名患者中,有186名患者 (23.8%) 患有DVT.
XGBoost模型实现了卓越的预测准确性 (AUC 0.829培训,0.808验证).
关键预测因素包括受伤到入院的时间,D-二聚体,血红蛋白,白蛋白和APTT水平.

结论:

XGBoost模型在老年关节骨折患者的手术前DVT预测方面表现出很高的性能.
SHAP分析提高了模型的解释性,而基于网络的工具提高了临床适用性.
这种预测工具可以帮助临床医生进行风险分层和医疗决策.

关键词:

在SHAP中,价值是SHAP值.深静脉血栓形成的原因是深静脉血栓形成.部骨折骨折部骨折骨折机器学习是机器学习.预测模型是一个预测模型.

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