Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
This work addresses the challenge of analyzing disease progression for Systemic Sclerosis patients, offering incremental improvements through semi-supervised disentanglement of latent spaces.
The authors tackled the problem of modeling complex disease trajectories for Systemic Sclerosis by proposing a semi-supervised deep generative approach, resulting in interpretable latent representations that enabled clustering into novel sub-types and personalized prediction with uncertainty quantification.
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.