Subtyping Patients With Chronic Disease Using Longitudinal BMI Patterns

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Author(s)Mottalib, Md Mozaharul
Author(s)Jones-Smith, Jessica C.
Author(s)Sheridan, Bethany
Author(s)Beheshti, Rahmatollah
Date Accessioned2023-04-05T20:48:57Z
Date Available2023-04-05T20:48:57Z
Publication Date2023-01-17
Description© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This article was originally published in IEEE Journal of Biomedical and Health Informatics. The version of record is available at: https://doi.org/10.1109/JBHI.2023.3237753
AbstractObesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke. While the role of obesity identified by cross-sectional BMI recordings has been heavily studied, the role of BMI trajectories is much less explored. In this study, we use a machine learning approach to subtype individuals' risk of developing 18 major chronic diseases by using their BMI trajectories extracted from a large and geographically diverse EHR dataset capturing the health status of around two million individuals for a period of six years. We define nine new interpretable and evidence-based variables based on the BMI trajectories to cluster the patients into subgroups using the k-means clustering method. We thoroughly review each cluster's characteristics in terms of demographic, socioeconomic, and physiological measurement variables to specify the distinct properties of the patients in the clusters. In our experiments, the direct relationship of obesity with diabetes, hypertension, Alzheimer's, and dementia has been re-established and distinct clusters with specific characteristics for several of the chronic diseases have been found to be conforming or complementary to the existing body of knowledge.
Sponsor10.13039/100006094-University of Delaware (Grant Number 1627409) NIH (Grant Number: 3P20GM103446 and 5P20GM113125) Robert Wood Johnson Foundation's (Grant Number 76778)
CitationM. M. Mottalib, J. C. Jones-Smith, B. Sheridan and R. Beheshti, "Subtyping Patients With Chronic Disease Using Longitudinal BMI Patterns," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 2083-2093, April 2023, doi: 10.1109/JBHI.2023.3237753.
ISSN2168-2208
URLhttps://udspace.udel.edu/handle/19716/32627
Languageen_US
PublisherIEEE Journal of Biomedical and Health Informatics
KeywordsPatient subtyping
Keywordsinterpretable machine learning
KeywordsBMI trajectories
Keywordsobesity
Keywordschronic diseases
Keywordsgood health and well-being
TitleSubtyping Patients With Chronic Disease Using Longitudinal BMI Patterns
TypeArticle
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