  The impact of machine learning models on healthcare will depend on the degree
of trust that healthcare professionals place in the predictions made by these
models. In this paper, we present a method to provide people with clinical
expertise with domain-relevant evidence about why a prediction should be
trusted. We first design a probabilistic model that relates meaningful latent
concepts to prediction targets and observed data. Inference of latent variables
in this model corresponds to both making a prediction and providing supporting
evidence for that prediction. We present a two-step process to efficiently
approximate inference: (i) estimating model parameters using variational
learning, and (ii) approximating maximum a posteriori estimation of latent
variables in the model using a neural network, trained with an objective
derived from the probabilistic model. We demonstrate the method on the task of
predicting mortality risk for patients with cardiovascular disease.
Specifically, using electrocardiogram and tabular data as input, we show that
our approach provides appropriate domain-relevant supporting evidence for
accurate predictions.
