ArticleIEEE journal of biomedical and health informatics2023
Subtyping Patients With Chronic Disease Using Longitudinal BMI Patterns.
Article in IEEE journal of biomedical and health informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- Mediating effect of serum lipids on the BMI-uric acid association: A cross-sectional study.Medicine · 2026Article
- Identification of Bone Marrow and Peripheral Blood Plasma Extracellular Vesicle Protein Biomarker Signatures for Multiple Myeloma Diagnosis and Staging.International journal of nanomedicine · 2026Article
- Cluster-Based Immunization Patterns in Diabetes Mellitus: Insights for Personalized Preventive Care.Journal of personalized medicine · 2025Article
- A supervised machine learning approach with feature selection for sex-specific biomarker prediction.NPJ systems biology and applications · 2025Article
- Impact of COVID-19 Diagnosis on Weight Trajectories of Children in the US National COVID Cohort Collaborative.Childhood obesity (Print) · 2025Article
- Impact of COVID-19 Diagnosis on Weight Trajectories of Children in the US National COVID Cohort Collaborative.medRxiv : the preprint server for health sciences · 2025Article
- Associations of Longitudinal BMI-Percentile Classification Patterns in Early Childhood with Neighborhood-Level Social Determinants of Health.Childhood obesity (Print) · 2025Article
- An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation.Proceedings of machine learning research · 2024Article
- Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline-A Systematic Review.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.