Genetics-Driven Personalized Disease Progression Model

• URL: http://arxiv.org/abs/2503.00028v1
• Date: Mon, 24 Feb 2025 21:45:14 GMT
• Title: Genetics-Driven Personalized Disease Progression Model
• Authors: Haoyu Yang, Sanjoy Dey, Pablo Meyer,
• Abstract summary: Existing approaches often model the disease progression as a uniform trajectory pattern at the population level.<n>We propose a personalized disease progression model to jointly learn the heterogeneous progression patterns and groups of genetic profiles.<n>Our proposed model shows improvement on real-world and synthetic clinical data.
• Score: 3.7962423524107396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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