# Subtyping patients with chronic disease using longitudinal BMI patterns

arXiv: https://arxiv.org/abs/2111.05385 · HTML: https://mlsanity.com/paper/2111.05385/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 86 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [Temporal-Clustering Invariance in Irregular Healthcare Time Series](https://mlsanity.com/paper/1904.12206/) (2019)

## Built on by (most cited first, 4 of 4)

- [An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation](https://mlsanity.com/paper/2412.10454/) (2024)
- [HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning](https://mlsanity.com/paper/2509.24118/) (2025)
- [Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks](https://mlsanity.com/paper/2406.02510/) (2024)
- [Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture](https://mlsanity.com/paper/2606.28623/) (2026)

## Abstract (arXiv)

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.