机器学习与免疫相关的实验室指标可以区分肺结核和肺外结核
Shuanglin Gu1, Yichen Guo2, Han Jiang3
1Department of Clinical Research Center, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China.
Frontiers in medicine
|January 1, 2026
概括
机器学习模型可以帮助早期诊断肺外结核病 (EPTB). 一个使用常规血液标记物,如血红蛋白 (HB) 和CD4+T细胞的K-Nearest Neighbors模型显示出对EPTB的强烈预测有效性.
科学领域:
- 传染性疾病 传染性疾病
- 医学诊断 医学诊断 医学诊断
- 机器学习 机器学习
背景情况:
- 肺外结核 (EPTB) 是一个重大的全球卫生挑战.
- 早期和准确的EPTB诊断仍然是一个主要的临床障碍.
- EPTB可以通过血液或淋巴系统从肺病变传播到其他器官.
研究的目的:
- 开发和评估用于早期识别EPTB的机器学习模型.
- 使用常规实验室数据评估各种算法的预测性能.
- 为了确定EPTB检测的关键生物标志物.
主要方法:
- 分析了1580名患者的数据.
- 包括七个常规的血液标志物 (例如,血红蛋白 (HB),平均血小板体积 (MPV),一个细胞因子 (INTERLEUKIN-6 (IL-6)),和三个淋巴细胞相关的指数 (例如,CD4+T细胞).
- 使用九种不同的机器学习算法构建预测模型.
主要成果:
- K-最近邻居 (KNN) 模型表现出最高的预测性能,曲线下的面积 (AUC) 为0.846.
- 该KNN模型实现了0.769的灵敏度和0.786.6的特异性.
- CD4+T细胞,HB和MPV被确定为EPTB最重要的预测因素.
结论:
- 开发了一个可解释的机器学习模型来识别EPTB.
- 该模型利用例行实验室测试的数据,有助于早期诊断.
- 这种方法为EPTB的临床检测提供了宝贵的帮助.
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