Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes

TL;DR

This paper introduces a deep generative model with semi-supervised learning to interpret complex disease trajectories, enabling personalized predictions, patient clustering, and discovery of new medical insights.

Contribution

It presents a novel semi-supervised deep generative approach that disentangles latent disease processes using medical concepts for better interpretability and analysis.

Findings

Effective modeling of systemic sclerosis disease trajectories.

Ability to cluster patients and identify disease sub-types.

Supports personalized monitoring and uncertainty quantification.

Abstract

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.