Deep Neural Models for Medical Concept Normalization in User-Generated Texts
This addresses the challenge of standardizing medical terminology from social media for applications like health monitoring, though it is incremental as it applies known neural methods to this domain.
The paper tackles medical concept normalization in user-generated texts by mapping health-related mentions to UMLS concepts using neural networks, achieving significant performance improvements over existing state-of-the-art models on three benchmarks.
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.