# Genetics-Driven Personalized Disease Progression Model

arXiv: https://arxiv.org/abs/2503.00028 · HTML: https://mlsanity.com/paper/2503.00028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 81 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Structured Inference Networks for Nonlinear State Space Models](https://mlsanity.com/paper/1609.09869/) (2016)
- [Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression](https://mlsanity.com/paper/2106.02875/) (2021)
- [A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves](https://mlsanity.com/paper/1608.05182/) (2016)
- [Localized Lasso for High-Dimensional Regression](https://mlsanity.com/paper/1603.06743/) (2016)
- [Computational Drug Repositioning Using Continuous Self-controlled Case Series](https://mlsanity.com/paper/1604.05976/) (2016)

## Abstract (arXiv)

Modeling disease progression through multiple stages is critical for clinical decision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney diseases, and so on. Existing approaches often model the disease progression as a uniform trajectory pattern at the population level. However, chronic diseases are highly heterogeneous and often have multiple progression patterns depending on a patient's individual genetics and environmental effects due to lifestyles. We propose a personalized disease progression model to jointly learn the heterogeneous progression patterns and groups of genetic profiles. In particular, an end-to-end pipeline is designed to simultaneously infer the characteristics of patients from genetic markers using a variational autoencoder and how it drives the disease progressions using an RNN-based state-space model based on clinical observations. Our proposed model shows improvement on real-world and synthetic clinical data.