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在流行病环境中分阶段识别发烧患者的CAP:建模和验证
Ziheng Gao1,2, Tengfei Chen2, Yanxiang Ha2
1Bejing University of Chinese Medicine, Beijing, 100029, China.
Scientific reports
|December 18, 2025
概括
我们开发了机器学习模型,使用临床数据预测社区获得性肺炎 (CAP) 风险,改善资源有限的环境中的诊断. 这些模型为识别肺炎和分类亚型提供了可访问的工具.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 肺炎的诊断 肺炎的诊断 肺炎的诊断
背景情况:
- 社区获得性肺炎 (CAP) 的诊断通常需要昂贵的成像,限制资源有限的地区的访问.
- 现有的诊断工具以临床医生为重点,传统中医 (TCM) 缺乏肺炎亚型分类的标准.
- 需要可访问的,数据驱动的方法来评估CAP风险和基于TCM的亚型识别.
研究的目的:
- 开发和验证一种多式机器学习模型,用于评估发烧患者的社区性肺炎 (CAP) 风险.
- 整合临床变量和实验室测试,以改善肺炎概率预测.
- 利用无监督学习在传统中医 (TCM) 框架内对肺炎亚型进行分类.
主要方法:
- 开发集体学习模型,整合临床数据 (基本信息,病历) 和实验室测试.
- 在北京传统中医医院发烧诊所的数据集上训练并验证模型 (N=2,193培训,N=300验证).
- 采用了基于TCM综合征的亚型分类 (冷/热) 的潜在类别分析.
主要成果:
- 使用前访问记录的初始模型 (α) 取得了良好的表现 (AUC内部=0.80,AUC外部=0.80).
- 采用四个实验室指标的增强模型 (β) 显著改善了内部性能 (AUC内部=0.93) 并保持了外部有效性 (AUC外部=0.81).
- 两个预测模型被开发成在线计算器,并确定了TCM子类型.
结论:
- 整合临床和实验室数据的机器学习模型有效评估发烧患者的CAP风险.
- 开发的模型提供了一个有希望的,可访问的方法来确定CAP,特别是在资源有限的环境中.
- 该研究成功地分类了基于TCM的肺炎亚型,将现代诊断与传统医学相结合.
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