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机器学习模型预测ESKD患者CRRT期间的凝血风险:一种SHAP可解释的方法

Shuang Qiu¹, Shibo Mu², Yongyuan Tao¹

¹Department of Blood Purification, General Hospital of Northern Theatre Command, Shenyang, China.

|October 10, 2025

概括

机器学习模型可以预测持续脏替代疗法的末期脏病患者体外电路凝血风险. 支持矢量机器模型显示出最佳性能,确定低分子量肝素剂量作为关键因素.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
生物医学工程生物医学工程
数据科学数据科学数据科学

背景情况:

持续置换疗法 (CRRT) 对末期病 (ESKD) 患者至关重要.
维持体外循环和防止电路凝结对于CRRT的有效性和安全性至关重要.
对凝血风险的预测模型可以提高患者的治疗结果.

研究的目的:

开发和评估机器学习 (ML) 模型,用于预测接受CRRT的ESKD患者体外电路凝血风险.
确定导致电路凝固的关键因素.
提高CRRT的安全性和有效性.

主要方法:

分析了一组636名接受CRRT的ESKD患者.
使用最少绝对收缩和选择操作员 (LASSO) 算法进行了特征选择.
多个ML算法 (SVM,XGBoost,RF,GBM,决策树,LR) 使用十倍交叉验证进行了训练和验证.
模型的性能是使用接收机操作特征曲线 (AUC) 下的面积和沙普利增量解释 (SHAP) 值来评估的.

主要成果:

在体外循环期间发生血栓的发生率为31.3% (199名患者).
支持矢量机 (SVM) 模型实现了0.864的最高AUC,表明了优异的预测性能.
低分子量肝素 (LMWH) 的初始剂量被确定为凝血的最重要的预测因子.

结论:

机器学习模型,特别是SVM,是预测CRRT患者体外电路凝血风险的有效工具.
SHAP分析有效地阐明了关键的风险因素,使得早期临床干预成为可能.
根据预测模型优化LMWH剂量可能会减少凝血事件并改善CRRT管理.

关键词:

在 ESKD 的意思是 ESKD.持续的置换疗法治疗.身体外电路凝血凝血机器学习是机器学习.预测模型预测模型

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