  Real-world clinical time series data sets exhibit a high prevalence of
missing values. Hence, there is an increasing interest in missing data
imputation. Traditional statistical approaches impose constraints on the
data-generating process and decouple imputation from prediction. Recent works
propose recurrent neural network based approaches for missing data imputation
and prediction with time series data. However, they generate deterministic
outputs and neglect the inherent uncertainty. In this work, we introduce a
unified Bayesian recurrent framework for simultaneous imputation and prediction
on time series data sets. We evaluate our approach on two real-world mortality
prediction tasks using the MIMIC-III and PhysioNet benchmark datasets. We
demonstrate strong performance gains over state-of-the-art (SOTA) methods, and
provide strategies to use the resulting probability distributions to better
assess reliability of the imputations and predictions.
