Identifying growth-patterns in children by applying cluster analysis to electronic medical records

Moumita Bhattacharya; Deborah Ehrenthal; Hagit Shatkay

doi:10.1109/BIBM.2014.6999183

Identifying growth-patterns in children by applying cluster analysis to electronic medical records

Moumita Bhattacharya
, Deborah Ehrenthal
, Hagit Shatkay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
Editors	Huiru Zheng, Xiaohua Tony Hu, Daniel Berrar, Yadong Wang, Werner Dubitzky, Jin-Kao Hao, Kwang-Hyun Cho, David Gilbert
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	348-351
Number of pages	4
ISBN (Electronic)	9781479956692
DOIs	https://doi.org/10.1109/BIBM.2014.6999183
State	Published - Dec 29 2014
Event	2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 - Belfast, United Kingdom Duration: Nov 2 2014 → Nov 5 2014

Publication series

Name	Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014

Other

Other	2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
Country/Territory	United Kingdom
City	Belfast
Period	11/2/14 → 11/5/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBM.2014.6999183

Cite this

Bhattacharya, M., Ehrenthal, D., & Shatkay, H. (2014). Identifying growth-patterns in children by applying cluster analysis to electronic medical records. In H. Zheng, X. T. Hu, D. Berrar, Y. Wang, W. Dubitzky, J.-K. Hao, K.-H. Cho, & D. Gilbert (Eds.), Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 (pp. 348-351). Article 6999183 (Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2014.6999183

Bhattacharya, Moumita ; Ehrenthal, Deborah ; Shatkay, Hagit. / Identifying growth-patterns in children by applying cluster analysis to electronic medical records. Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014. editor / Huiru Zheng ; Xiaohua Tony Hu ; Daniel Berrar ; Yadong Wang ; Werner Dubitzky ; Jin-Kao Hao ; Kwang-Hyun Cho ; David Gilbert. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 348-351 (Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014).

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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",

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Bhattacharya, M, Ehrenthal, D & Shatkay, H 2014, Identifying growth-patterns in children by applying cluster analysis to electronic medical records. in H Zheng, XT Hu, D Berrar, Y Wang, W Dubitzky, J-K Hao, K-H Cho & D Gilbert (eds), Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014., 6999183, Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014, Institute of Electrical and Electronics Engineers Inc., pp. 348-351, 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014, Belfast, United Kingdom, 11/2/14. https://doi.org/10.1109/BIBM.2014.6999183

Identifying growth-patterns in children by applying cluster analysis to electronic medical records. / Bhattacharya, Moumita; Ehrenthal, Deborah; Shatkay, Hagit.
Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014. ed. / Huiru Zheng; Xiaohua Tony Hu; Daniel Berrar; Yadong Wang; Werner Dubitzky; Jin-Kao Hao; Kwang-Hyun Cho; David Gilbert. Institute of Electrical and Electronics Engineers Inc., 2014. p. 348-351 6999183 (Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Identifying growth-patterns in children by applying cluster analysis to electronic medical records

AU - Bhattacharya, Moumita

AU - Ehrenthal, Deborah

AU - Shatkay, Hagit

PY - 2014/12/29

Y1 - 2014/12/29

N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014

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A2 - Wang, Yadong

A2 - Dubitzky, Werner

A2 - Hao, Jin-Kao

A2 - Cho, Kwang-Hyun

A2 - Gilbert, David

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014

Y2 - 2 November 2014 through 5 November 2014

ER -

Bhattacharya M, Ehrenthal D, Shatkay H. Identifying growth-patterns in children by applying cluster analysis to electronic medical records. In Zheng H, Hu XT, Berrar D, Wang Y, Dubitzky W, Hao JK, Cho KH, Gilbert D, editors, Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 348-351. 6999183. (Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014). doi: 10.1109/BIBM.2014.6999183

Identifying growth-patterns in children by applying cluster analysis to electronic medical records

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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