  We have three contributions in this work: 1. We explore the utility of a
stacked denoising autoencoder and a paragraph vector model to learn
task-independent dense patient representations directly from clinical notes. To
analyze if these representations are transferable across tasks, we evaluate
them in multiple supervised setups to predict patient mortality, primary
diagnostic and procedural category, and gender. We compare their performance
with sparse representations obtained from a bag-of-words model. We observe that
the learned generalized representations significantly outperform the sparse
representations when we have few positive instances to learn from, and there is
an absence of strong lexical features. 2. We compare the model performance of
the feature set constructed from a bag of words to that obtained from medical
concepts. In the latter case, concepts represent problems, treatments, and
tests. We find that concept identification does not improve the classification
performance. 3. We propose novel techniques to facilitate model
interpretability. To understand and interpret the representations, we explore
the best encoded features within the patient representations obtained from the
autoencoder model. Further, we calculate feature sensitivity across two
networks to identify the most significant input features for different
classification tasks when we use these pretrained representations as the
supervised input. We successfully extract the most influential features for the
pipeline using this technique.
