Genetics-Driven Personalized Disease Progression Model
This addresses the need for personalized clinical decision-making in chronic diseases like cancer and diabetes, though it appears incremental as it builds on existing methods like variational autoencoders and RNNs.
The paper tackled the problem of modeling heterogeneous disease progression patterns in chronic diseases by proposing a personalized model that jointly learns progression patterns and genetic profiles, showing improvement on real-world and synthetic clinical data.
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.