  Information extraction from textual documents such as hospital records and
healthrelated user discussions has become a topic of intense interest. The task
of medical concept coding is to map a variable length text to medical concepts
and corresponding classification codes in some external system or ontology. In
this work, we utilize recurrent neural networks to automatically assign ICD-10
codes to fragments of death certificates written in English. We develop
end-to-end neural architectures directly tailored to the task, including basic
encoder-decoder architecture for statistical translation. In order to
incorporate prior knowledge, we concatenate cosine similarities vector among
the text and dictionary entry to the encoded state. Being applied to a standard
benchmark from CLEF eHealth 2017 challenge, our model achieved F-measure of
85.01% on a full test set with significant improvement as compared to the
average score of 62.2% for all official participants approaches.
