使用机器学习模型和特征选择模型预测急性胰腺炎中感染性胰腺死
Li Xin1, Ding Yixuan1, Huang Bohan1
1Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Scientific reports
|March 1, 2026
概括
一个可解释的随机森林模型准确地预测了急性胰腺炎 (AP) 患者感染胰腺亡 (IPN) 的风险. 关键预测因素包括纤维素,APACHE II评分和D-二次体,使得早期风险分层成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
- 临界护理医学 临界护理医学
背景情况:
- 感染性胰腺亡 (IPN) 是急性胰腺炎 (AP) 的严重并发症,具有显著的死亡风险.
- 早期和准确的IPN预测仍然是一个关键的临床挑战.
- 现有的预测方法往往缺乏解释性,阻碍了临床的信任和应用.
研究的目的:
- 开发和外部验证可解释的机器学习模型,用于预测个性化IPN风险.
- 加强急性胰腺炎患者早期风险分层.
- 确定与IPN发展相关的关键临床预测因素.
主要方法:
- 对728名AP患者的回顾性分析,对166名患者进行外部验证.
- 使用嵌入式特征选择 (规则化的线性和基于树的算法) 开发可解释的机器学习模型.
- 使用嵌套交叉验证,AUC,精度回忆,Brier分数和SHAP进行模型性能评估.
主要成果:
- 随机森林模型表现出卓越的性能,外部AUC为0.764.
- 增加IPN风险的关键预测因素包括纤维素,APACHE II评分,D-二次体,IL-6和C反应蛋白.
- 较高的淋巴细胞计数和血红素被确定为对IPN的保护因素.
结论:
- 一个可解释的随机森林模型为IPN预测提供了强大的歧视和校准.
- 该模型为AP患者早期IPN风险分层提供了一个透明的,数据驱动的框架.
- 建议在临床实施之前进行未来的多中心验证.
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