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使用基于SHAP的XGBoost模型改善急性胰腺炎严重程度的早期预测:超越传统的评分系统

Ana Cisnal¹, María Lourdes Ruiz Rebollo², César Flórez-Pardo²

¹Institute of Advanced Production Technologies, School of Industrial Engineering, University of Valladolid, Paseo Prado de la Magdalena 3-5, 47011, Valladolid Spain.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

|November 8, 2025

概括

机器学习模型可以准确地预测严重的急性胰腺炎. 通过SHAP增强的XGBoost模型的表现优于传统的得分,改善了患者管理.

科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
临床预测模型临床预测模型

背景情况:

急性胰腺炎 (AP) 的发病率和死亡率很高,其中20%的病例进展到严重的形式.
传统的评分系统 (Ranson,BISAP) 的准确性有限,AP严重性预测的适用性延迟.

研究的目的:

开发和验证机器学习 (ML) 模型,用于早期预测中度严重和严重急性胰腺炎 (MSAP-SAP).
将ML模型的性能与传统的评分系统进行比较.

主要方法:

用入院 (24小时) 和早期 (48小时) 数据对816名患者 (2014-2023) 的回顾性分析.
开发和评估ML模型 (XGBoost) 与SHapley添加式解释 (SHAP) 进行特征选择和可解释性.
训练/测试的模型使用80:20分割,通过ROC-AUC进行评估.

主要成果:

用SHAP增强的XGBoost (XGB^SH) 模型实现了高预测性能:ROC-AUC为0.89 (24小时) 和0.94 (48小时).
关键预测因素包括SIRS,BUN,CRP,肌素和多流液.
XGB^SH模型的表现明显优于兰森和BISAP的得分 (ROC-AUC = 0.72).

结论:

用SHAP增强的XGBoost模型为早期AP严重性预测提供了可靠和可解释的工具.
这种ML方法改善了AP的临床决策和患者管理.
早期准确预测AP严重程度对于及时干预至关重要.

关键词:

在急性胰腺炎中,临床决策支持临床决策支持机器学习是机器学习.严重程度预测的预测.

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