在国家人口中开发一种可解释的机器学习喘预测模型,使用血清制阻燃剂
Xin Pan1,2, Qiong Wang1,3, Che Li4,5
1Graduate School, Beijing University of Chinese Medicine, Beijing, China.
Clinical and experimental medicine
|October 25, 2025
概括
血清化阻燃剂 (BFR) 代谢物与成人喘风险有关. 机器学习模型,特别是XGBoost,使用BFR概况有效预测喘,有助于早期预防和管理.
科学领域:
- 环境健康 环境健康
- 毒理学 毒理学 毒理学
- 流行病学 流行病学
背景情况:
- 制阻燃剂 (BFR) 是广泛使用的化学品,具有潜在的健康影响.
- 暴露于环境污染物如BFRs被假设为导致呼吸系统疾病如喘.
- 了解与喘风险相关的特定BFR及其代谢物概况对于公共卫生至关重要.
研究的目的:
- 调查血清BFR代谢物水平和混合物概况与美国成年人喘风险之间的关联.
- 开发和验证一种可解释的机器学习模型,以基于BFR暴露来预测喘.
- 通过使用先进的分析方法,识别导致喘风险的关键BFR.
主要方法:
- 利用了国家健康和营养检查调查 (NHANES) 的数据,涵盖1999-2023年,包括9948名美国成年人.
- 采用了四种机器学习算法 (光梯度增强机,XGBoost,随机森林,神经网络) 与夏普利增量扩展 (SHAP) 和后勤回归.
- 确定了喘的显著预测因素,包括人口统计,生活方式和血清BFR代谢物数据.
主要成果:
- XGBoost模型实现了最高的预测性能,曲线下的面积 (AUC) 为0.814.
- 包括家族史,BMI和特定BFR (PBDE47,PBDE28,PBDE154,PBDE153, PBB153) 在内的16个特征被确定为重要的预测因素.
- 五个关键的BFR (PBDE47,PBDE28,PBDE154,PBDE153, PBB153) 被SHAP分析强调为喘风险的主要贡献者.
结论:
- 基于血清BFR代谢物概况,XGBoost机器学习模型有效地预测成人喘.
- 这种方法为早期喘预防,风险分层和临床管理提供了有前途的工具.
- 对BFR暴露和喘病原体的进一步研究是有必要的.
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