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用回归和机器学习模型评估来自环境辐射暴露,PM_2.5和其他暴露的县级肺癌发病率

Heechan Lee^1,2, Heidi A Hanson², Jeremy Logan³

¹Nuclear and Radiological Engineering and Medical Physics Programs, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 770 State Street, Atlanta, GA, 30332, USA.

Environmental geochemistry and health

|February 17, 2024

概括

了解人类暴露体是疾病病因学的关键. 这项研究发现,结合和PM2.5暴露,使用机器学习,更好地预测肺癌发病率.

科学领域:

环境健康环境健康
流行病学流行病学
毒理学毒理学毒理学

背景情况:

肺癌风险受多因素暴露的影响,包括环境,社会经济和生活方式因素.
对暴露的流行病学研究往往忽略了其他环境因素的同时影响.
颗粒物 (PM2.5) 可能会影响子后代的传输到肺组织,可能会改变健康风险.

研究的目的:

同时调查多个低剂量辐射源 (子,总α活性) 和PM2.5对美国肺癌发病率的影响.
评估暴露与肺癌发病率之间的关联,同时控制县级因素.
将传统回归模型的性能与机器学习 (ML) 方法进行比较,以分析复杂的环境暴露和健康结果.

主要方法:

使用国家癌症研究所的监测,流行病学和最终结果数据 (2013-2017) 的生态分析.
普森回归和随机森林模型被用来评估关联.
在分析中控制了县级的环境,社会人口统计和生活方式因素.

主要成果:

机器学习模型,特别是Poisson随机森林回归,在预测肺癌发病率 (较低的MAPE和RMSE) 中明显优于传统的Poisson回归.
发现PM2.5对肺癌发病率的影响随着环境度较高的子增加而增加.
证实了和PM2.5暴露对肺癌风险的协同作用.

结论:

在评估肺癌风险时,有必要考虑多种环境暴露,如和PM2.5,这强调了暴露学框架的重要性.
机器学习模型有效地捕捉了环境暴露和健康结果之间的复杂相互作用,例如室内暴露和肺癌发病率.
该研究强调了先进分析方法的有用性,以更全面地了解复杂疾病的环境决定因素.

关键词:

暴露组体是指暴露组体.电离辐射辐射的电离辐射.肺癌是一种肺癌.对于PM2.5来说,这是一个很好的例子.拉顿是一种.

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