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

arXiv: https://arxiv.org/abs/2311.08149 · HTML: https://mlsanity.com/paper/2311.08149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Weakly-Supervised Disentanglement Without Compromises](https://mlsanity.com/paper/2002.02886/) (2020)
- [GP-VAE: Deep Probabilistic Time Series Imputation](https://mlsanity.com/paper/1907.04155/) (2019)
- [Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data](https://mlsanity.com/paper/1709.07902/) (2017)
- [Gaussian Process Prior Variational Autoencoders](https://mlsanity.com/paper/1810.11738/) (2018)
- [Simple and Effective VAE Training with Calibrated Decoders](https://mlsanity.com/paper/2006.13202/) (2020)
- [A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation](https://mlsanity.com/paper/2010.14766/) (2020)
- [Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case](https://mlsanity.com/paper/2206.08309/) (2022)
- [Longitudinal Variational Autoencoder](https://mlsanity.com/paper/2006.09763/) (2020)
- [Recursive Estimation for Sparse Gaussian Process Regression](https://mlsanity.com/paper/1905.11711/) (2019)
- [MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis](https://mlsanity.com/paper/1909.12637/) (2019)
- [Correlated Product of Experts for Sparse Gaussian Process Regression](https://mlsanity.com/paper/2112.09519/) (2021)
- [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)
- [Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series](https://mlsanity.com/paper/2311.07744/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Causal Framework for Precision Rehabilitation](https://mlsanity.com/paper/2411.03919/) (2024)
- [Deep Kernel Learning for Stratifying Glaucoma Trajectories](https://mlsanity.com/paper/2605.00708/) (2026)

## Abstract (arXiv)

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.