机器学习对急性胰腺炎死亡风险的预测价值:系统性审查和元分析
Xiaoming Xu1, Hualei Chen1, Guobin Wang2
1Department of Gastroenterology.
Journal of clinical gastroenterology
|December 15, 2025
概括
机器学习 (ML) 模型准确预测急性胰腺炎 (AP) 患者的死亡率. 这些先进的工具优于传统方法,有助于临床决策,以获得更好的患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 预测分析是一种预测分析.
背景情况:
- 急性胰腺炎 (AP) 是一个重大的临床挑战,严重病例的死亡率高.
- 准确的早期死亡率预测对于有效的临床管理和个性化治疗策略至关重要.
- 机器学习 (ML) 在提高AP死亡率预测的准确性方面表现有前途.
研究的目的:
- 评估和比较ML模型在预测AP患者早期死亡率方面的表现.
- 提供支持开发AP死亡率改善的临床预测工具的证据.
- 引导临床医生利用ML来改善患者分层和治疗规划.
主要方法:
- 在主要数据库 (PubMed,Web of Science,Embase,Cochrane Library) 进行了系统的文献搜索,查找2012年至2025年间发表的研究.
- 使用随机效应模型合成效应大小,以考虑异质性.
- 进行了子组分析,以调查模型特征对预测准确性的影响.
主要成果:
- 包括24项研究,证明了ML模型对AP死亡风险的高预测性能.
- 对于ML模型的聚合C指数在训练集中为0.84和在外部验证集中为0.84.
- ML模型的表现明显优于传统的评分系统 (合并C指数:0.754),主要预测因素包括年龄,BUN,白血素,WBC和呼吸率.
结论:
- 机器学习模型在预测急性胰腺炎死亡率方面表现出极好的准确性.
- 这些发现支持使用ML来更新现有的或开发新的,简单的临床预测工具.
- 基于ML的预测为提高AP管理中的临床决策提供了有价值的参考.
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