Identifying Growth-Patterns in Children by Applying Cluster analysis to Electronic Medical Records
This work addresses obesity risk prediction for children and healthcare providers, but it is incremental as it applies existing clustering techniques to medical data.
The paper tackled the problem of early obesity risk detection in children by identifying distinct growth patterns using clustering methods on electronic medical records, resulting in the ability to separate children into heaviest, middle, or lightest clusters based on early growth measurements.
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.