  In realistic scenarios, multivariate timeseries evolve over case-by-case
time-scales. This is particularly clear in medicine, where the rate of clinical
events varies by ward, patient, and application. Increasingly complex models
have been shown to effectively predict patient outcomes, but have failed to
adapt granularity to these inherent temporal resolutions. As such, we introduce
a novel, more realistic, approach to generating patient outcome predictions at
an adaptive rate based on uncertainty accumulation in Bayesian recurrent
models. We use a Recurrent Neural Network (RNN) and a Bayesian embedding layer
with a new aggregation method to demonstrate adaptive prediction timing. Our
model predicts more frequently when events are dense or the model is certain of
event latent representations, and less frequently when readings are sparse or
the model is uncertain. At 48 hours after patient admission, our model achieves
equal performance compared to its static-windowed counterparts, while
generating patient- and event-specific prediction timings that lead to improved
predictive performance over the crucial first 12 hours of the patient stay.
