  Accurate prediction of disease trajectories is critical for early
identification and timely treatment of patients at risk. Conventional methods
in survival analysis are often constrained by strong parametric assumptions and
limited in their ability to learn from high-dimensional data, while existing
neural network models are not readily-adapted to the longitudinal setting. This
paper develops a novel convolutional approach that addresses these drawbacks.
We present MATCH-Net: a Missingness-Aware Temporal Convolutional Hitting-time
Network, designed to capture temporal dependencies and heterogeneous
interactions in covariate trajectories and patterns of missingness. To the best
of our knowledge, this is the first investigation of temporal convolutions in
the context of dynamic prediction for personalized risk prognosis. Using
real-world data from the Alzheimer's Disease Neuroimaging Initiative, we
demonstrate state-of-the-art performance without making any assumptions
regarding underlying longitudinal or time-to-event processes attesting to the
model's potential utility in clinical decision support.
