Disease-Atlas: Navigating Disease Trajectories with Deep Learning

TL;DR

This paper introduces Disease-Atlas, a deep learning framework that improves disease trajectory modeling by overcoming traditional joint model limitations, offering enhanced flexibility, scalability, and robustness with real-world medical data.

Contribution

The paper presents a novel deep learning approach that extends joint modeling for longitudinal and time-to-event data, addressing fixed model limitations and computational challenges.

Findings

Improved performance over traditional joint models

Enhanced scalability to high-dimensional data

Robustness to irregular sampling in medical datasets

Abstract

Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification, and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks, while retaining the benefits of joint modeling. Using longitudinal data from a real-world medical dataset, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.