  In this work, we consider the medical concept normalization problem, i.e.,
the problem of mapping a disease mention in free-form text to a concept in a
controlled vocabulary, usually to the standard thesaurus in the Unified Medical
Language System (UMLS). This task is challenging since medical terminology is
very different when coming from health care professionals or from the general
public in the form of social media texts. We approach it as a sequence learning
problem, with recurrent neural networks trained to obtain semantic
representations of one- and multi-word expressions. We develop end-to-end
neural architectures tailored specifically to medical concept normalization,
including bidirectional LSTM and GRU with an attention mechanism and additional
semantic similarity features based on UMLS. Our evaluation over a standard
benchmark shows that our model improves over a state of the art baseline for
classification based on CNNs.
