在潜伏感染的个体中,人工智能驱动的肺结核再激活的风险预测
Mohammed Eltahir Abdelhag1, Husham E Homeida2, Ogail Yousif Dawod3
1Department of Computer Science, College of Engineering and Computer Science, Jazan University, Saudi Arabia.
The Indian journal of tuberculosis
|December 16, 2025
概括
人工智能 (AI) 模型可以通过分析临床,免疫和遗传数据来预测潜在结核病 (TB) 复发. 这种方法有助于识别高风险人群,以便在结核病控制计划中进行有针对性的干预和资源分配.
科学领域:
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 潜伏性结核病感染 (LTBI) 影响全球四分之一的人口,造成严重的重新激活风险.
- 目前的诊断方法,如TST和干扰素-释放试验,无法预测结核病的重新激活.
- 需要整合多式联运数据的预测模型来有效管理TB.
研究的目的:
- 研究人工智能 (AI) 和机器学习 (ML) 在预测潜伏结核病复发中的实用性.
- 开发和验证预测模型,以识别从LTBI发展为活跃结核病的高风险个体.
主要方法:
- 利用了一组数据集,包括来自LTBI个体的临床,免疫 (细胞因子概况) 和转录组数据.
- 训练并评估机器学习算法:随机森林 (RF),支持矢量机 (SVM) 和梯度提升 (XGBoost).
- 用于特征选择的递归特征消除 (RFE) 和性能评估的分层k倍交叉验证 (AUC-ROC,准确性,精度,回忆).
主要成果:
- XGBoost模型表现出卓越的性能,AUC-ROC为0.93,准确率为88.7%,回忆率为86.2%.
- 关键预测因素包括高IL-6和IFN-γ水平,先前的结核病史,HIV联合感染和与免疫调节相关的基因表达.
- 该模型在各个年龄和感染子组中表现一致.
结论:
- 人工智能驱动的模型显示,基于结核病复发风险对LTBI个体进行分层是有希望的.
- 将预测工具集成到临床实践中可以增强早期干预,并优化对结核病控制的资源配置.
- 建议进行进一步的前性研究和多站点验证,以便在现实世界中实施.
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