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通过血清PFAS度来开发一种可解释的机器学习慢性阻塞性肺病预测模型

Xiaomei Shao¹, Ling Zhang², Yuting Wang¹

¹Nanjing Jiangbei Hospital, Affiliated Nanjing Jiangbei Hospital of Xinglin College, Nantong University, Jiangsu, China.

Frontiers in public health

|July 25, 2025

概括

某些像PFOS和PFUA这样的per-和多基物质 (PFAS) 可能会防止慢性阻塞性肺病 (COPD),而其他像PFOA这样的物质则会增加风险. 机器学习模型可以根据PFAS暴露来预测COPD风险.

科学领域:

环境健康环境健康
毒理学毒理学毒理学
数据科学数据科学数据科学

背景情况:

慢性阻塞性肺病 (COPD) 构成了严重的全球健康负担,早期检测方法有限.
关于和多基基物质 (PFAS) 与COPD之间的联系的研究已经存在,但很少有研究使用可解释的机器学习 (ML).

研究的目的:

调查各种PFAS暴露与患COPD的风险之间的关联.
开发和验证一种可解释的ML模型,以基于PFAS暴露水平来预测COPD风险.

主要方法:

利用了来自4,450名国家健康和营养检查调查 (NHANES) 参与者的数据 (2013-2018).
应用数据清理,K-近邻 (KNN) 归算,并开发了9个ML模型,包括CatBoost.
评估模型使用准确度,AUC,灵敏度和特异性;进一步分析了使用PDP和SHAP的最佳模型.
部署了最终的模型作为一个公共的基于Web的风险计算器.

主要成果:

CatBoost模型实现了84%的准确性,0.89 AUC,81%的灵敏度和84%的特异性.
较高水平的 perfluorooctane 硫酸 (PFOS) 和 perfluoroundecanoic 酸 (PFUA) 与降低 COPD 风险有关.
perfluorooctanoic 酸 (PFOA) 和 2-(N-Methyl-perfluorooctane sulfonamido) 乙酸 (MPAH) 显示出与增加COPD风险的积极关联.
其他PFAS如PFNA,PFDE和PFHxS表现出混合或非线性效应.

结论:

PFOS和PFUA作为防治COPD的保护因素,而PFOA和MPAH会增加COPD的风险.
可解释的ML,特别是CatBoost,有效地澄清了复杂的PFAS-COPD关系.
调查结果支持需要更严格的PFAS法规,并强调ML在公共卫生预防策略中的作用.

关键词:

谢普利添加剂的解释慢性阻塞性肺病慢性阻塞性肺病环境污染环境污染机器学习是机器学习.部分依赖图表部分依赖图表

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