Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis

TL;DR

This paper introduces a semi-supervised deep generative model for analyzing and interpreting complex disease trajectories, specifically for Systemic Sclerosis, enabling patient clustering, disease understanding, and personalized predictions.

Contribution

It presents a novel semi-supervised generative approach that disentangles latent disease features using medical knowledge, improving interpretability and clinical utility.

Findings

Effective patient clustering into novel sub-types

Enhanced disease trajectory interpretability

Accurate personalized predictions with uncertainty quantification

Abstract

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.