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Leveraging deep neural networks and semantic similarity measures for medical concept normalisation in user reviews

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dc.date.accessioned 2019-01-22T20:57:45Z
dc.date.available 2019-01-22T20:57:45Z
dc.date.issued 2018
dc.identifier.issn 2221-7932
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/149635
dc.description.abstract © 2018 Rossiiskii Gosudarstvennyi Gumanitarnyi Universitet.All Rights Reserved. Nowadays a new yet powerful tool for drug repurposing and hypothesis generation emerged. Text mining of different domains like scientific libraries or social media has proven to be reliable in that application. One particular task in that area is medical concept normalization, i.e. mapping a disease mention to a concept in a controlled vocabulary, like Unified Medical Language System (UMLS). This task is challenging due to the differences in language of health care professionals and social media users. To bridge this gap, we developed end-to-end architectures based on bidirectional Long Short-Term Memory and Gated Recurrent Units. In addition, we combined an attention mechanism with our model. We have done an exploratory study on hyperparameters of proposed architectures and compared them with the effective baseline for classification based on convolutional neural networks. A qualitative examination of the mentions in user reviews dataset collected from popular online health information platforms as well as quantitative one both show improvements in the semantic representation of health-related expressions in user reviews about drugs.
dc.relation.ispartofseries Komp'juternaja Lingvistika i Intellektual'nye Tehnologii
dc.subject Deep learning
dc.subject Information extraction
dc.subject Medical concept mapping
dc.subject Medical concept normalization
dc.subject Recurrent neural networks
dc.subject UMLS
dc.title Leveraging deep neural networks and semantic similarity measures for medical concept normalisation in user reviews
dc.type Conference Paper
dc.relation.ispartofseries-issue 17
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 490
dc.source.id SCOPUS22217932-2018-17-SID85058005600


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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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