  Recurrent neural network based solutions are increasingly being used in the
analysis of longitudinal Electronic Health Record data. However, most works
focus on prediction accuracy and neglect prediction uncertainty. We propose
Deep Kernel Accelerated Failure Time models for the time-to-event prediction
task, enabling uncertainty-awareness of the prediction by a pipeline of a
recurrent neural network and a sparse Gaussian Process. Furthermore, a deep
metric learning based pre-training step is adapted to enhance the proposed
model. Our model shows better point estimate performance than recurrent neural
network based baselines in experiments on two real-world datasets. More
importantly, the predictive variance from our model can be used to quantify the
uncertainty estimates of the time-to-event prediction: Our model delivers
better performance when it is more confident in its prediction. Compared to
related methods, such as Monte Carlo Dropout, our model offers better
uncertainty estimates by leveraging an analytical solution and is more
computationally efficient.
