  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.
