  In this paper, we propose a deep generative time series approach using latent
temporal processes for modeling and holistically analyzing complex disease
trajectories. We aim to find meaningful temporal latent representations of an
underlying generative process that explain the observed 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 concepts. By combining
the generative approach with medical knowledge, we leverage the ability to
discover novel aspects of the disease while integrating medical concepts into
the model. 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 the disease into new sub-types. Moreover, our
method enables personalized online monitoring and prediction of multivariate
time series including uncertainty quantification. We demonstrate the
effectiveness of our approach in modeling systemic sclerosis, showcasing the
potential of our machine learning model to capture complex disease trajectories
and acquire new medical knowledge.
