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

## TL;DR

This paper introduces a novel non-negative matrix factorisation approach for analyzing the evolution of multimorbidity patterns over time using large-scale electronic health records, providing new insights and metrics.

## Contribution

The study presents a new temporal phenotyping method using NMF, along with evaluation metrics and insights into disease network evolution from extensive EHR data.

## Key findings

- Effective identification of disease clusters and their trajectories over time
- Quantitative metrics for evaluating multimorbidity patterns
- Insights into the emergence and evolution of multimorbidity networks

## Abstract

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.