  In this work, we consider the medical concept normalization problem, i.e.,
the problem of mapping a health-related entity mention in a free-form text to a
concept in a controlled vocabulary, usually to the standard thesaurus in the
Unified Medical Language System (UMLS). This is a challenging task 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 powerful neural networks such
as recurrent neural networks and contextualized word representation models
trained to obtain semantic representations of social media expressions. Our
experimental evaluation over three different benchmarks shows that neural
architectures leverage the semantic meaning of the entity mention and
significantly outperform an existing state of the art models.
