  We propose a deep generative approach using latent temporal processes for
modeling and holistically analyzing complex disease trajectories, with a
particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent
representations of the underlying generative process that explain the observed
patient disease trajectories in an interpretable and comprehensive way. To
enhance the interpretability of these latent temporal processes, we develop a
semi-supervised approach for disentangling the latent space using established
medical knowledge. By combining the generative approach with medical
definitions of different characteristics of SSc, we facilitate the discovery of
new aspects of the disease. We show that the learned temporal latent processes
can be utilized for further data analysis and clinical hypothesis testing,
including finding similar patients and clustering SSc patient trajectories into
novel sub-types. Moreover, our method enables personalized online monitoring
and prediction of multivariate time series with uncertainty quantification.
