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Interactive attention network for adverse drug reaction classification

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dc.date.accessioned 2019-01-22T20:50:48Z
dc.date.available 2019-01-22T20:50:48Z
dc.date.issued 2018
dc.identifier.issn 1865-0929
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/149066
dc.description.abstract © Springer Nature Switzerland AG 2018. Detection of new adverse drug reactions is intended to both improve the quality of medications and drug reprofiling. Social media and electronic clinical reports are becoming increasingly popular as a source for obtaining the health-related information, such as identification of adverse drug reactions. One of the tasks of extracting adverse drug reactions from social media is the classification of entities that describe the state of health. In this paper, we investigate the applicability of Interactive Attention Network for identification of adverse drug reactions from user reviews. We formulate this problem as a binary classification task. We show the effectiveness of this method on a number of publicly available corpora.
dc.relation.ispartofseries Communications in Computer and Information Science
dc.subject Adverse drug reactions
dc.subject Deep learning
dc.subject Health social media analytics
dc.subject Machine learning
dc.subject Natural language processing
dc.subject Text mining
dc.title Interactive attention network for adverse drug reaction classification
dc.type Conference Paper
dc.relation.ispartofseries-volume 930
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 185
dc.source.id SCOPUS18650929-2018-930-SID85054802269


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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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