使用机器学习构建严重的高甘油三血症相关急性胰腺炎的预测模型
Shichuan Feng1, Guangzhu Wang1, Ying Zhou1
1Department of General Practice, Hanzhong Central Hospital Hanzhong 723000, Shaanxi, China.
American journal of translational research
|October 20, 2025
概括
机器学习模型有效地识别了严重的急性胰腺炎-高糖血 (SAP-HTG) 风险因素. 与传统的评分系统相比,XGBoost在早期诊断和风险分层方面表现优越.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 严重的高甘油三血症相关的急性胰腺炎 (SAP-HTG) 需要准确的风险分层.
- 区分SAP-HTG与非严重的形式对于及时干预至关重要.
- 传统的评分系统在预测SAP-HTG严重程度方面可能存在局限性.
研究的目的:
- 使用机器学习识别SAP-HTG的关键风险因素.
- 开发和比较预测模型 (逻辑回归,随机森林,SVM,XGBoost) 与传统的评分系统 (APACHE II,Ranson) 相比.
- 加强SAP-HTG的早期诊断和风险分层.
主要方法:
- 对514名急性胰腺炎患者的回顾性分析 (90名SAP-HTG,424名N-SAP-HTG).
- 利用LASSO和多变量逻辑回归来识别预测因素.
- 使用AUC,ROC曲线和SHAP分析开发和评估了四种机器学习模型 (逻辑回归,RF,SVM,XGBoost).
主要成果:
- 确定了九个预测因素:年龄,糖尿病史,禁食血糖 (FBG),TyG指数,粉酶 (AMY),甘油三 (TG),总胆固醇 (TC),C反应蛋白 (CRP) 和 (Ca2+).
- CRP和TG是显著的风险因素;Ca2+和年龄是保护性的.
- XGBoost (AUC=0.959) 和随机森林 (AUC=0.955) 模型的性能优于后勤回归,SVM和传统的评分系统.
结论:
- 机器学习模型有效地识别了SAP-HTG的风险因素.
- 在SAP-HTG风险分层方面,XGBoost表现出卓越的性能.
- 这些模型为SAP-HTG的早期临床诊断和管理提供了有价值的工具.
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