  This paper introduces a novel non-parametric deep model for estimating
time-to-event (survival analysis) in presence of censored data and competing
risks. The model is designed based on the sequence-to-sequence (Seq2Seq)
architecture, therefore we name it Survival Seq2Seq. The first recurrent neural
network (RNN) layer of the encoder of our model is made up of Gated Recurrent
Unit with Decay (GRU-D) cells. These cells have the ability to effectively
impute not-missing-at-random values of longitudinal datasets with very high
missing rates, such as electronic health records (EHRs). The decoder of
Survival Seq2Seq generates a probability distribution function (PDF) for each
competing risk without assuming any prior distribution for the risks. Taking
advantage of RNN cells, the decoder is able to generate smooth and virtually
spike-free PDFs. This is beyond the capability of existing non-parametric deep
models for survival analysis. Training results on synthetic and medical
datasets prove that Survival Seq2Seq surpasses other existing deep survival
models in terms of the accuracy of predictions and the quality of generated
PDFs.
