肥胖
痴呆
糖尿病
社会经济地位
医学
体质指数
疾病
亚型
共病
聚类分析
星团(航天器)
老年学
计算机科学
环境卫生
人工智能
内科学
人口
程序设计语言
内分泌学
作者
Md. Mozaharul Mottalib,Jessica C. Jones‐Smith,Bethany Sheridan,Rahmatollah Beheshti
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-12
被引量:1
标识
DOI:10.1109/jbhi.2023.3237753
摘要
Obesity 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.
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