Reliable prediction of childhood obesity using only routinely collected EHRs may be possible.

ABSTRACT:

Background

Early identification of children at high risk of obesity can provide clinicians with the information needed to provide targeted lifestyle counseling to high-risk children at a critical time to change the disease course.

Objectives

This study aimed to develop predictive models of childhood obesity, applying advanced machine learning methods to a large unaugmented electronic health record (EHR) dataset. This work improves on other studies that have (i) relied on data not routinely available in EHRs (like prenatal data), (ii) focused on single-age predictions, or (iii) not been rigorously validated.

Methods

A customized sequential deep-learning model to predict the development of obesity was built, using EHR data from 36,191 diverse children aged 0-10 years. T

SUBMITTER: Gupta M 

PROVIDER: S-EPMC11417568 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature