@inproceedings{d7871ad100e048feaedd2eba461db25e,
title = "Identifying growth-patterns in children by applying cluster analysis to electronic medical records",
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 growth-pattern 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.",
author = "Moumita Bhattacharya and Deborah Ehrenthal and Hagit Shatkay",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 ; Conference date: 02-11-2014 Through 05-11-2014",
year = "2014",
month = dec,
day = "29",
doi = "10.1109/BIBM.2014.6999183",
language = "English (US)",
series = "Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "348--351",
editor = "Huiru Zheng and Hu, \{Xiaohua Tony\} and Daniel Berrar and Yadong Wang and Werner Dubitzky and Jin-Kao Hao and Kwang-Hyun Cho and David Gilbert",
booktitle = "Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014",
address = "United States",
}
