mmVAE: multimorbidity clustering using Relaxed Bernoulli $β$-Variational Autoencoders
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:88-102, 2022.
The prevalence of chronic disease multimorbidity is a significant and increasing challenge for health systems. In many cases, the occurrence of one chronic disease leads to the development of one or more other chronic conditions. This exerts a significant challenge in improving patient outcomes and is a growing challenge globally as average population age increases. Using electronic health record information to identify patterns of co-occurring conditions is seen as an unbiased means of understanding multimorbidity but most studies have adopted off-the-shelf algorithmic techniques that are not tailored for the application. We present a novel bespoke approach for multimorbidity clustering based on a highly customised version of a $\beta$-variational autoencoder. We incorporate the use of minimum entropy clustering to identify sparse, low-dimensional factored representations that link at a feature-level to the observed patient-level multimorbidity profiles. We demonstrate how the approach can be used to explore complex structure in a population-scale health data sets by examining data from a UK population of nearly 300,000 women in pregnancy suffering from multimorbidity.