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Automated detection of adverse drug reactions from social media posts with machine learning

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dc.date.accessioned 2019-01-22T20:36:45Z
dc.date.available 2019-01-22T20:36:45Z
dc.date.issued 2018
dc.identifier.issn 0302-9743
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/147961
dc.description.abstract © Springer International Publishing AG 2018. Adverse drug reactions can have serious consequences for patients. Social media is a source of information useful for detecting previously unknown side effects from a drug since users publish valuable information about various aspects of their lives, including health care. Therefore, detection of adverse drug reactions from social media becomes one of the actual tools for pharmacovigilance. In this paper, we focus on identification of adverse drug reactions from user reviews and formulate this problem as a binary classification task. We developed a machine learning classifier with a set of features for resolving this problem. Our feature-rich classifier achieves significant improvements on a benchmark dataset over baseline approaches and convolutional neural networks.
dc.relation.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subject Adverse drug reactions
dc.subject Deep learning
dc.subject Health social media analytics
dc.subject Machine learning
dc.subject Text mining
dc.title Automated detection of adverse drug reactions from social media posts with machine learning
dc.type Conference Paper
dc.relation.ispartofseries-volume 10716 LNCS
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 3
dc.source.id SCOPUS03029743-2018-10716-SID85039445423


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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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