# Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation

arXiv: https://arxiv.org/abs/1907.08577 · HTML: https://mlsanity.com/paper/1907.08577/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study](https://mlsanity.com/paper/2105.09632/) (2021)
- [Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions](https://mlsanity.com/paper/2209.00322/) (2022)
- [sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion](https://mlsanity.com/paper/2211.17121/) (2022)
- [Towards a Fairer Non-negative Matrix Factorization](https://mlsanity.com/paper/2411.09847/) (2024)
- [Transfer Learning in Electronic Health Records through Clinical Concept Embedding](https://mlsanity.com/paper/2107.12919/) (2021)
- [Interpreting deep embeddings for disease progression clustering](https://mlsanity.com/paper/2307.06060/) (2023)
- [Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records](https://mlsanity.com/paper/2610.20502/) (2026)

## Abstract (arXiv)

Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population, both in absolute and relative terms. However, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that employed advanced models, usually lack appropriate benchmarking and validations. In this study, we (1) introduce a novel approach for using Non-negative Matrix Factorisation (NMF) for temporal phenotyping (i.e., simultaneously mining disease clusters and their trajectories); (2) provide quantitative metrics for the evaluation of disease clusters from such studies; and (3) demonstrate how the temporal characteristics of the disease clusters that result from our model can help mine multimorbidity networks and generate new hypotheses for the emergence of various multimorbidity patterns over time. We trained and evaluated our models on one of the world's largest electronic health records (EHR), with 7 million patients, from which over 2 million where relevant to this study.