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作为PLWH中活跃结核病的关键预测生物标志物的IFN-γ和IL-6:四种机器学习方法的结果

Juan Wan¹, Virasakdi Chongsuvivatwong², Pei Zhang¹

¹Department of Public Laboratory, The Third People's Hospital of Kunming /Infectious Disease Clinical Medical Center of Yunnan Province, Kunming, Yunnan, People's Republic of China.

International journal of general medicine

|February 20, 2026

概括

在携带艾滋病毒 (PLWH) 的人群中,结核病 (TB) 诊断具有挑战性. 使用IFN-γ和IL-6的机器学习模型显示出有希望的结果,但在预测活跃结核病的概括性方面存在困难.

科学领域:

传染性疾病传染性疾病
免疫学免疫学免疫学
生物医学数据科学生物医学数据科学

背景情况:

结核病 (TB) 是艾滋病毒感染者 (PLWH) 的主要死亡原因.
在PLWH中对活跃结核病的早期和准确诊断至关重要,但仍然是一个重大挑战.
需要生物标志物发现和先进的计算方法来提高诊断能力.

研究的目的:

确定生物标志物的新组合,以预测PLWH中的活跃结核病.
开发和评估用于主动结核病预测的机器学习 (ML) 模型.
在随机和时间分割数据集上评估模型性能.

主要方法:

招募了760名肺部症状的PLWH,收集了人口统计,临床和细胞因子数据.
开发了四个ML模型,使用10倍交叉验证,特征选择和超参数优化.
评估模型使用ROC-AUC,灵敏度,特异性和随机和时间数据分割的变量重要性.

主要成果:

干扰素- (IFN-γ) 和干扰素-6 (IL-6) 水平在活跃的结核病患者中显著升高.
使用IFN-γ和IL-6的梯度增强机 (GBM) 模型在随机数据集 (0.96训练,0.73测试) 上实现了高AUC.
GBM 模型的概括性有限,在按时间顺序排列的测试组中,AUC (0.66) 较低.

结论:

IFN-γ和IL-6的升高可能表明PLWH中的结核病激活.
当前的ML模型虽然在随机分割上有效,但在预测按时间顺序排序的现实数据中的活跃TB方面存在局限性.
需要进行进一步的研究,以提高基于ML的结核病诊断工具在PLWH中的通用性.

关键词:

在IFN-γ中使用IFN.在IL-6的研究中,IL-6被认为是IL-6的基因.携带艾滋病毒的人生活在艾滋病毒中.预测建模预测建模结核病是一种肺结核病.

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