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Accurate prediction of disease trajectories is critical for earlyidentification and timely treatment of patients at risk. Conventional methodsin survival analysis are often constrained by strong parametric assumptions andlimited in their ability to learn from high-dimensional data, while existingneural network models are not readily-adapted to the longitudinal setting. Thispaper develops a novel convolutional approach that addresses these drawbacks.We present MATCH-Net: a Missingness-Aware Temporal Convolutional Hitting-timeNetwork, designed to capture temporal dependencies and heterogeneousinteractions in covariate trajectories and patterns of missingness. To the bestof our knowledge, this is the first investigation of temporal convolutions inthe context of dynamic prediction for personalized risk prognosis. Usingreal-world data from the Alzheimer's Disease Neuroimaging Initiative, wedemonstrate state-of-the-art performance without making any assumptionsregarding underlying longitudinal or time-to-event processes attesting to themodel's potential utility in clinical decision support.

MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks