  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.
