构建手术部位感染预测模型的最佳机器学习算法:系统性审查和网络元分析
Jiao Shan1, Xiaoyuan Bao2, Bin Wang3
1Department of Hospital-Acquired Infection Control, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Computers in biology and medicine
|May 1, 2025
概括
使用结构化和文本数据的机器学习模型显示了预测手术部位感染 (SSI) 的最佳性能. 增强分类器似乎是SSI预测中最有效的算法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 对感染的预测建模.
背景情况:
- 手术部位感染 (SSIs) 构成了重大的临床挑战.
- 许多机器学习 (ML) 算法用于SSI预测,但它们的比较性能尚不清楚.
研究的目的:
- 进行网络元分析,比较SSI各种ML算法的预测性能.
- 为了确定SSI预测的最佳ML算法.
主要方法:
- 在多个数据库 (MEDLINE,EMBASE,CINAHL,Web of Science,Cochrane Library) 进行系统搜索,截至2023年11月.
- 包括使用ML用于SSI预测模型的诊断准确性试验.
- 使用相对诊断几率比率 (RDOR) 和优势指数 (SI) 的性能评估.
主要成果:
- 采用外科类型的模型表现优于没有外科类型的模型 (RDOR 2.71).
- 使用混合结构数据和文本数据的模型优于仅使用结构数据的模型 (RDOR 8.70).
- 增强分类器在混合数据模型 (SI 6.17) 中显示出最好的总体预测,而支持矢量机则在结构化数据 (SI 4.70) 中表现出色.
结论:
- 利用结构化和文本数据的ML算法为SSI预测提供了最佳的性能.
- 增强分类器成为预测手术部位感染的领先算法.
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