Department of Psychology, University of Kansas, 1415 Jayhawk Blvd, Lawrence, KS 66045, USA.
Department of Psychology, University of Tennessee, Austin Peay Building, Knoxville, TN 37996, USA.
Int J Environ Res Public Health. 2022 Aug 2;19(15):9447. doi: 10.3390/ijerph19159447.
Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their ability to search for interactions. This paper shows evidence that an explainable artificial intelligence approach, commonly employed in genomics research, can address this problem. The method entails using random intersection trees to decode interactions learned by random forest models. Here, this approach is used to extract interactions between features of a multi-level environment from random forest models of waist-to-height ratios using 11,112 participants from the Adolescent Brain Cognitive Development study. This study shows that methods used to discover interactions between genes can also discover interacting features of the environment that impact obesity. This new approach to modeling ecosystems may help shine a spotlight on combinations of environmental features that are important to obesity, as well as other health outcomes.
生态理论表明，环境、社会和个体因素相互作用导致肥胖。然而，许多分析技术，如多层次建模，需要手动指定相互作用的因素，这使得它们无法搜索相互作用。本文表明，一种可解释的人工智能方法（常用于基因组学研究）可以解决这个问题。该方法采用随机交集树来解码随机森林模型学到的相互作用。在这里，该方法用于从青少年大脑认知发育研究中使用的 11112 名参与者的腰高比随机森林模型中提取多层次环境特征之间的相互作用。这项研究表明，用于发现基因之间相互作用的方法也可以发现影响肥胖的环境相互作用特征。这种新的生态系统建模方法可能有助于关注对肥胖以及其他健康结果重要的环境特征组合。
