使用机器学习识别新疆潜在结核病感染的风险因素
YanJie Wang1,2, Zhen Luo2, XiaoTong Wen2
1Epidemiology and Statistics, School of Public Health, Xinjiang Medical University, Urumqi, 830017, China.
BMC public health
|December 6, 2025
概括
机器学习确定了潜在结核病感染 (LTBI) 的关键风险因素. 一个开发的名录图有助于有针对性的查,建议对吸烟和暴露于尘埃的干预措施来减少结核病负担.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 潜伏性结核病感染 (LTBI) 是活跃结核病 (TB) 的一个主要储存库.
- 识别LTBI风险因素对于有效的预防策略至关重要.
- 机器学习 (ML) 可以揭示复杂的风险因素-疾病关系.
研究的目的:
- 使用机器学习识别潜在结核感染 (LTBI) 的关键风险因素.
- 开发一个用于LTBI风险评估的预测模型.
- 评估针对性结核病查的风险名谱的临床实用性.
主要方法:
- 一项病例控制研究将669名LTBI患者与669名活跃结核病患者进行了比较.
- 倾向性得分匹配 (1:1) 用于参与者匹配.
- 四个ML模型 (随机森林,XGBoost,SVM,神经网络) 和后勤回归确定了风险因素. SHAP分析提供了解释. 构建和验证了一个风险名录图.
主要成果:
- XGBoost显示出高性能 (AUC=0.898) 的结果.
- 年龄组,医疗保险,收入和教育水平是SHAP发现的关键预测因素.
- 后勤回归确定了11个重要的风险因素,包括年龄,BMI,吸烟,职业尘埃暴露,糖尿病和结核病接触史. 该名录显示了良好的区别 (AUC=0.839).
结论:
- 机器学习有效地确定了LTBI的关键风险因素.
- 经过验证的风险名录为有针对性的LTBI查提供了一个实用的工具,特别是在资源有限的环境中.
- 建议采取针对可修改风险因素的干预措施,如吸烟和职业尘埃暴露,以减少结核病负担.
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