在中年和老年人中,探索两组金属与潜在的对立性功能和功能之间的关联:来自可解释机器学习方法的证据
Haoran Chen1, Min Wang1, Jiao Li1
1Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China.
Ecotoxicology and environmental safety
|December 13, 2023
概括
本研究引入了一种可解释的机器学习 (EML) 模型,以评估金属混合物如何影响功能. EML揭示了金属之间的复杂相互作用,并确定了慢性病 (CKD) 风险的临界暴露水平.
科学领域:
- 环境健康 环境健康
- 毒理学 毒理学 毒理学
- 机器学习 机器学习
背景情况:
- 机器学习模型有效地捕捉了复杂的暴露-结果关系.
- 了解金属混合物对功能的影响对于公共卫生至关重要.
研究的目的:
- 引入一个可解释的机器学习 (EML) 模型来评估金属混合物与功能之间的关联.
- 调查中年和老年人中慢性病 (CKD) 风险,这些人暴露在具有潜在对抗效应的金属混合物中.
主要方法:
- 利用来自45岁以上参与者国家健康和营养检查调查 (NHANES) 的数据.
- 开发了一个EML模型,将优化机器学习与Shapley增量扩展 (SHAP) 集成在一起.
- 将EML结果与多重逻辑回归 (MLR) 和贝叶斯内核机器回归 (BKMR) 进行比较.
主要成果:
- EML确定了,,和作为与CKD风险相关的关键因素.
- 检测到金属之间的显著相互作用,包括-和-.
- EML确切地确定了关键的"触发点",其中CKD风险显著增加.
结论:
- 同时接触不同毒性金属会通过复杂的关节关联影响功能.
- 可解释机器学习 (EML) 是分析复杂暴露混合物及其对健康影响的强大工具.
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