Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.

ABSTRACT: Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children's data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health

SUBMITTER: Gupta M 

PROVIDER: S-EPMC9221869 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature