Identifying Growth-Patterns in Children by Applying Cluster analysis to Electronic Medical Records

TL;DR

This paper uses clustering analysis on electronic medical records to identify distinct childhood growth patterns, aiming to detect early signs of obesity risk by grouping children with similar body measurement trajectories.

Contribution

It introduces a clustering-based approach to classify children's growth patterns from EMRs, facilitating early obesity risk detection.

Findings

Distinct growth-pattern curves correlate with different weight-based clusters.

Clustering can separate children into risk groups based on early measurements.

Growth patterns can predict obesity risk with early data.

Abstract

Obesity is one of the leading health concerns in the United States. Researchers and health care providers are interested in understanding factors affecting obesity and detecting the likelihood of obesity as early as possible. In this paper, we set out to recognize children who have higher risk of obesity by identifying distinct growth patterns in them. This is done by using clustering methods, which group together children who share similar body measurements over a period of time. The measurements characterizing children within the same cluster are plotted as a function of age. We refer to these plots as growthpattern curves. We show that distinct growth-pattern curves are associated with different clusters and thus can be used to separate children into the topmost (heaviest), middle, or bottom-most cluster based on early growth measurements.