整合特征选择,机器学习和SHAP可解释性来预测严重急性胰腺炎
1Department of Emergency Medicine, Gönen State Hospital, 10900 Balıkesir, Türkiye.
Diagnostics (Basel, Switzerland)
|October 16, 2025
概括
机器学习模型准确地预测严重的急性胰腺炎 (SAP) 在急诊室 (ED) 呈现时使用随时可用的数据. 这种方法有助于 SAP 患者的早期风险分层和资源配置.
科学领域:
- 医学信息学 医学信息学
- 临床决策支持 临床决策支持
- 医疗保健中的机器学习
背景情况:
- 严重急性胰腺炎 (SAP) 在早期风险分层中具有显著的发病率和挑战.
- 传统的SAP评分系统需要连续观察,这限制了它们对即时评估的实用性.
- 开发准确的早期预测模型对于SAP在急诊部 (ED) 的演示至关重要.
研究的目的:
- 开发和比较用于早期SAP预测的监督机器学习 (ML) 管道.
- 将特征选择和基于SHAP的可解释性集成到用于SAP预测的ML模型中.
- 评估ML模型的性能和可解释性,使用在ED到达时可用的数据.
主要方法:
- 对急性胰腺炎的成年患者进行了一项回顾性,单中心的队列研究.
- 通过将六种特征选择方法与六种分类器配对,创建了36个ML管道.
- 为了模型的可解释性,使用SHAP (SHapley添加式扩展) 进行了评估,并使用引导来评估性能.
主要成果:
- 性能最好的ML管道在接收器运行特征曲线 (AUROC) 下的面积在0.750和0.826.0之间.
- 顶部管道 (随机森林特征的递归特征消除 + k-最近邻居分类器) 达到0.826.8的AUROC.
- SHAP分析证实,常规可用的变量对SAP预测有了可信的贡献.
结论:
- 将特征选择与ML集成,可以使用ED演示数据对SAP进行准确和可解释的早期预测.
- 开发的ML方法识别了可操作的预测因素,可能支持更早的分拣和资源分配.
- 建议对这些ML模型进行外部验证,以确认它们的通用性.
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