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Clinical notes in electronic health records contain highly heterogeneouswriting styles, including non-standard terminology or abbreviations. Usingthese notes in predictive modeling has traditionally required preprocessing(e.g. taking frequent terms or topic modeling) that removes much of therichness of the source data. We propose a pretrained hierarchical recurrentneural network model that parses minimally processed clinical notes in anintuitive fashion, and show that it improves performance for dischargediagnosis classification tasks on the Medical Information Mart for IntensiveCare III (MIMIC-III) dataset, compared to models that treat the notes as anunordered collection of terms or that conduct no pretraining. We also apply anattribution technique to examples to identify the words that the model uses tomake its prediction, and show the importance of the words' nearby context.

Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes