Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning

TL;DR

This paper introduces a deep learning approach to joint modeling for Alzheimer's disease, improving scalability and performance over traditional methods by leveraging neural networks.

Contribution

It presents a novel deep learning framework for joint modeling of longitudinal and time-to-event data in Alzheimer's disease, overcoming fixed model limitations.

Findings

Enhanced prediction accuracy over traditional models

Improved scalability to large datasets

Demonstrated effectiveness on ADNI data

Abstract

Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. Despite the many advantages of joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to large datasets. We adopt a deep learning approach to address these limitations, enhancing existing methods with the flexibility and scalability of deep neural networks while retaining the benefits of joint modeling. Using data from the Alzheimer's Disease Neuroimaging Institute, we show improvements in performance and scalability compared to traditional methods.