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开发和验证一种可解释的机器学习模型,用于预测感染胰腺缩患者的死亡率
Caihong Ning1,2,3,4,5, Hui Ouyang1,2, Jie Xiao6
1Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan Province 410008, China.
EClinicalMedicine
|February 6, 2025
概括
一种可解释的机器学习模型准确地预测了感染胰腺亡 (IPN) 患者的死亡率. 这种工具有助于临床决策,并改善了这种严重疾病的结果.
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
- 临床决策支持 临床决策支持
背景情况:
- 感染性胰腺亡 (IPN) 是急性胰腺炎的严重并发症,死亡率高 (15-35%).
- 现有的IPN死亡率预测工具缺乏足够的灵敏度和特异性.
- 需要改进的工具来预测IPN患者的死亡.
研究的目的:
- 开发和验证一种可解释的机器学习 (ML) 模型,用于预测IPN患者的90天死亡率.
- 确定IPN中死亡率的关键预测因素.
- 为临床应用创建可访问的工具.
主要方法:
- 对344名IPN患者进行前性队列研究;对132名患者进行外部验证.
- 开发并对10个ML模型进行了基准测试,选择了表现最佳的模型.
- 使用顺序向前选择来优化特征子集,并使用SHAP来实现模型可解释性.
- 开发了基于Web的交互式Shiny应用程序,用于临床使用.
主要成果:
- 随机生存森林 (RSF) 模型显示出优异的预测性能 (C指数为0.863内部,0.857外部).
- 关键的死亡预测因素包括多器官衰竭,高的APACHE II评分,长期器官衰竭,血液感染和年龄≥50岁.
- SHAP分析显示,预测因素和死亡率之间存在显著的非线性相互作用.
结论:
- 一个可解释的ML模型是可行的和有效的预测IPN患者的死亡率.
- 开发的模型和网络工具可以指导临床管理并改善患者的治疗结果.
- 这种方法为增强IPN病例的护理提供了更好的潜力.
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