  Deep learning models have exhibited superior performance in predictive tasks
with the explosively increasing Electronic Health Records (EHR). However, due
to the lack of transparency, behaviors of deep learning models are difficult to
interpret. Without trustworthiness, deep learning models will not be able to
assist in the real-world decision-making process of healthcare issues. We
propose a deep learning model based on Bayesian Neural Networks (BNN) to
predict uncertainty induced by data noise. The uncertainty is introduced to
provide model predictions with an extra level of confidence. Our experiments
verify that instances with high uncertainty are harmful to model performance.
Moreover, by investigating the distributions of model prediction and
uncertainty, we show that it is possible to identify a group of patients for
timely intervention, such that decreasing data noise will benefit more on the
prediction accuracy for these patients.
