  Clinical notes in electronic health records contain highly heterogeneous
writing styles, including non-standard terminology or abbreviations. Using
these notes in predictive modeling has traditionally required preprocessing
(e.g. taking frequent terms or topic modeling) that removes much of the
richness of the source data. We propose a pretrained hierarchical recurrent
neural network model that parses minimally processed clinical notes in an
intuitive fashion, and show that it improves performance for discharge
diagnosis classification tasks on the Medical Information Mart for Intensive
Care III (MIMIC-III) dataset, compared to models that treat the notes as an
unordered collection of terms or that conduct no pretraining. We also apply an
attribution technique to examples to identify the words that the model uses to
make its prediction, and show the importance of the words' nearby context.
