  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.
