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Multimorbidity, or the presence of several medical conditions in the sameindividual, has been increasing in the population, both in absolute andrelative terms. However, multimorbidity remains poorly understood, and theevidence from existing research to describe its burden, determinants andconsequences has been limited. Previous studies attempting to understandmultimorbidity patterns are often cross-sectional and do not explicitly accountfor multimorbidity patterns' evolution over time; some of them are based onsmall datasets and/or use arbitrary and narrow age ranges; and those thatemployed advanced models, usually lack appropriate benchmarking andvalidations. In this study, we (1) introduce a novel approach for usingNon-negative Matrix Factorisation (NMF) for temporal phenotyping (i.e.,simultaneously mining disease clusters and their trajectories); (2) providequantitative metrics for the evaluation of disease clusters from such studies;and (3) demonstrate how the temporal characteristics of the disease clustersthat result from our model can help mine multimorbidity networks and generatenew hypotheses for the emergence of various multimorbidity patterns over time.We trained and evaluated our models on one of the world's largest electronichealth records (EHR), with 7 million patients, from which over 2 million whererelevant to this study.

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