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

arXiv: https://arxiv.org/abs/2407.11427 · HTML: https://mlsanity.com/paper/2407.11427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 61 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 25 of 25)

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- [On Disentanglement in Gaussian Process Variational Autoencoders](https://mlsanity.com/paper/2102.05507/) (2021)
- [Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models](https://mlsanity.com/paper/2309.16521/) (2023)
- [SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping](https://mlsanity.com/paper/2209.10623/) (2022)
- [Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration](https://mlsanity.com/paper/2301.04525/) (2023)

## Abstract (arXiv)

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.