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Medical concept normalization in social media posts with recurrent neural networks

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dc.date.accessioned 2019-01-22T20:46:14Z
dc.date.available 2019-01-22T20:46:14Z
dc.date.issued 2018
dc.identifier.issn 1532-0464
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/148681
dc.description.abstract © 2018 Elsevier Inc. Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language System (UMLS), is known as medical concept normalization. This task is challenging due to the differences in the use of medical terminology between health care professionals and social media texts coming from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks and semantic representation of one- or multi-word expressions: we develop end-to-end architectures directly tailored to the task, including bidirectional Long Short-Term Memory, Gated Recurrent Units with an attention mechanism, and additional semantic similarity features based on UMLS. Our evaluation against a standard benchmark shows that recurrent neural networks improve results over an effective baseline for classification based on convolutional neural networks. A qualitative examination of mentions discovered in a dataset of user reviews collected from popular online health information platforms as well as a quantitative evaluation both show improvements in the semantic representation of health-related expressions in social media.
dc.relation.ispartofseries Journal of Biomedical Informatics
dc.subject Information extraction
dc.subject Medical concept normalization
dc.subject Natural language processing
dc.subject Recurrent neural networks
dc.subject Social media
dc.subject User reviews
dc.title Medical concept normalization in social media posts with recurrent neural networks
dc.type Article
dc.relation.ispartofseries-volume 84
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 93
dc.source.id SCOPUS15320464-2018-84-SID85049314992


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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