在欧洲成年人中,基于机器学习的高血压环境风险评分
Jean-Baptiste Guimbaud1, Emilie Calabre2, Rafael de Cid3
1ISGlobal, Barcelona, Spain; University of Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622 Villeurbanne, France; Meersens, Lyon, France.
Artificial intelligence in medicine
|May 1, 2025
概括
SEANN是一种新的神经网络方法,通过整合聚合效应大小来增强高血压风险因素分析,从而提高科学有效性,而不是纯粹基于数据的方法. 这种方法更好地解开了环境暴露对健康的影响.
科学领域:
- 环境健康 环境健康
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 暴露框架旨在了解累积环境暴露对健康的影响.
- 现有的方法面临着诸如多线性,非线性和混等挑战.
- 为了解决这些局限性,引入了SEANN (总结效果调整的神经网络).
研究的目的:
- 开发一种新的方法,将领域知识与神经网络整合起来,用于分析高血压风险因素.
- 改进环境暴露对高血压的影响的分析和解释.
- 将SEANN信息模型与不可知论深度神经网络模型进行比较.
主要方法:
- 利用了GCAT队列中的18337名成年人 (40-65岁) 的数据,分析了53个环境因素.
- 计算了两种基于深度神经网络的高血压患病率环境风险评分:一种由SEANN提供信息和聚合效应大小,另一种是无神论对应物.
- 采用沙普利值来提取和比较两种模型学到的暴露结果关系.
主要成果:
- 两个不可知性的NN和SEANN模型都实现了类似的预测性能 (AUC 0.7).
- SEANN在学习关系的科学有效性方面取得了实质性的改进,更好地与现有文献保持一致.
- 直接由SEANN提供信息的变量更接近文献发现,非信息变量显示调整后的关联与以前的研究更一致;平均三角形SHAP距离是SEANN的6倍.
结论:
- 在环境健康研究中,SEANN比传统的,纯粹基于数据的机器学习方法提供了附加值.
- 通过结合基于文献的效果大小,SEANN增强了对高血压复杂暴露效应的解.
- 该研究强调了SEANN对暴露组数据进行更准确,更可解释的分析的潜力.
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