Multimorbidity, the co-occurrence of multiple chronic conditions within the same individual, is increasing globally. This is a challenge for the single patients, as these individuals are subject to a heavy disease and treatment burden, yet evidence on the epidemiology and consequences of multimorbidity remains underexplored. Historically, studies aiming to understand multimorbidity patterns predominantly utilized cross-sectional data, neglecting the essential temporal dynamics which shape multimorbidity progression. Other studies based their analyses on small datasets, or populations only targeting certain sectors of the healthcare system. In this study, we (1) introduce a novel two-step multimodal Variational Autoencoder-based approach for temporal disease-based clustering (i.e. discovering age-aware multimorbidity clusters)
(2) provide quantitative experiments for the robustness of our approach and the extracted temporal clusters
and (3) demonstrate how the temporal disease clusters obtained from our model can provide novel understanding of the development of multiple conditions over time and thus generate new hypotheses for different stages of multimorbidity and their associations. We trained and evaluated our models on a dataset containing the entire adult population of Denmark in the period 1995-2015, focusing on individuals suffering from chronic heart disease, including 766,596 individuals.