Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning
This work addresses forecasting disease progression for Alzheimer's patients, but it is incremental as it builds on existing joint modeling approaches.
The authors tackled the problem of forecasting Alzheimer's disease trajectories by using deep learning to enhance joint models, showing improvements in performance and scalability compared to traditional methods.
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.