  One major impediment to the wider use of deep learning for clinical decision
making is the difficulty of assigning a level of confidence to model
predictions. Currently, deep Bayesian neural networks and sparse Gaussian
processes are the main two scalable uncertainty estimation methods. However,
deep Bayesian neural network suffers from lack of expressiveness, and more
expressive models such as deep kernel learning, which is an extension of sparse
Gaussian process, captures only the uncertainty from the higher level latent
space. Therefore, the deep learning model under it lacks interpretability and
ignores uncertainty from the raw data. In this paper, we merge features of the
deep Bayesian learning framework with deep kernel learning to leverage the
strengths of both methods for more comprehensive uncertainty estimation.
Through a series of experiments on predicting the first incidence of heart
failure, diabetes and depression applied to large-scale electronic medical
records, we demonstrate that our method is better at capturing uncertainty than
both Gaussian processes and deep Bayesian neural networks in terms of
indicating data insufficiency and distinguishing true positive and false
positive predictions, with a comparable generalisation performance.
Furthermore, by assessing the accuracy and area under the receiver operating
characteristic curve over the predictive probability, we show that our method
is less susceptible to making overconfident predictions, especially for the
minority class in imbalanced datasets. Finally, we demonstrate how uncertainty
information derived by the model can inform risk factor analysis towards model
interpretability.
