机器学习方法在病毒性肺炎中区分冷热综合征 集成传统中医和现代医学:机器学习模型开发和验证
Xiaojie Jin1,2, Yanru Wang1, Jiarui Wang3
1Key Laboratory of Dunhuang Medicine, Ministry of Education, Gansu University of Chinese Medicine, Dingxi East Road, 35th, Lanzhou, 730000, China, 86 13919019578.
JMIR medical informatics
|July 16, 2025
概括
机器学习准确地区分了传统中医 (TCM) 病毒性肺炎中的冷热综合征. 梯度增强机模型整合了TCM和现代功能,提高了诊断准确度,以便做出更好的临床决策.
科学领域:
- 综合医学是一个整体的医学.
- 计算生物学 计算生物学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 传统中医 (TCM) 综合征的分化是诊断和治疗的关键.
- 感冒和热综合征对于病毒性肺炎至关重要,但难以区分.
- 机器学习 (ML) 为改善TCM综合征差异化准确性提供了一种新的方法.
研究的目的:
- 开发一种基于ML的诊断模型,用于区分病毒性肺炎中的冷热综合征.
- 整合TCM和现代医疗功能,以提高诊断性能.
- 用内部和外部患者数据验证模型的有效性.
主要方法:
- 使用了8ML算法,包括梯度提升机 (GBM),后勤回归和随机森林.
- 在1484名病毒性肺炎患者 (2021-2022) 的临床数据上培训和验证模型.
- 仅使用TCM特征,仅使用现代特征和集成特征的模型进行比较.
主要成果:
- 集成TCM和现代功能的GBM模型实现了最高的诊断准确性 (AUC 0.7788).
- 最佳模型确定了13个关键特征,包括温度,C反应蛋白和白细胞计数.
- 外部验证显示了强的性能,AUC为0.8428.8.
结论:
- 一个ML模型有效地区分了病毒性肺炎中TCM冷热综合征.
- 整合TCM和现代功能可提高临床决策的诊断精度.
- 这项研究推动了TCM综合征差异化的科学解释和现代化.
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