机器学习对急性胰腺炎严重程度的预测价值:系统性审查和元分析
Rui Qian1, Jiamei Zhuang2, Jianjun Xie1
1Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China.
Heliyon
|April 24, 2024
概括
机器学习模型在预测急性胰腺炎 (AP) 严重程度方面显示出高准确性. 这些先进的工具为早期临床决策提供了传统评分系统的有希望的替代方案.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 临床预测模型临床预测模型
背景情况:
- 急性胰腺炎 (AP) 严重程度的早期预测在临床上具有挑战性.
- 现有的临床评分工具具有不确定的预测性能.
- 机器学习 (ML) 方法越来越多地被用于AP严重性预测.
研究的目的:
- 系统地审查和元分析ML模型对AP严重性的预测准确性.
- 提供基于证据的评估,对ML在AP管理中的作用.
主要方法:
- 在PubMed,EMBASE,Cochrane图书馆和科学网络中进行系统搜索,直到2023年12月5日.
- 使用预测模型偏差风险评估工具 (PROBAST) 进行偏差风险评估.
- 根据ML类型的子组分析和主流评分工具准确性的总结.
主要成果:
- 包括33项研究;汇总的c指数为0.87 (培训) 和0.88 (验证).
- 聚合的灵敏度:0.81 (培训),0.79 (验证); 具体性:0.84 (培训),0.90 (验证).
- 机器学习模型 (神经网络,随机森林,xgboost) 的表现优于逻辑回归和传统分数 (APACHE II,BISAP,Ranson).
结论:
- 机器学习证明了对AP严重性的出色的预测准确性.
- 机器学习模型可以作为开发改进的临床预测工具的基础.
- 这一审查支持将ML纳入AP管理的临床实践.
相关概念视频
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