Adverse drug extraction in twitter data using convolutional neural network

Liliya Akhtyamova, John Cardiff, Mikhail Alexandrov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    The study of health-related topics on social media has become a useful tool for the early detection of the different adverse medical conditions. In particular, it concerns cases related to the treatment of mental diseases, as the effects of medications here often prove to be unpredictable. In our research, we use convolutional neural networks (CNN) with word2vec embedding to classify user comments on Twitter. The aim of the classification is to reveal adverse drug reactions of users. The results obtained are highly promising, showing the overall usefulness of neural network algorithms in this kind of tasks.

    Original languageEnglish
    Title of host publicationProceedings - 28th International Workshop on Database and Expert Systems Applications, DEXA 2017
    EditorsA Min Tjoa, Roland R. Wagner
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages88-92
    Number of pages5
    ISBN (Electronic)9781538610510
    DOIs
    Publication statusPublished - 25 Sep 2017
    Event28th International Workshop on Database and Expert Systems Applications, DEXA 2017 - Lyon, France
    Duration: 28 Aug 201731 Aug 2017

    Publication series

    NameProceedings - International Workshop on Database and Expert Systems Applications, DEXA
    Volume2017-August
    ISSN (Print)1529-4188

    Conference

    Conference28th International Workshop on Database and Expert Systems Applications, DEXA 2017
    Country/TerritoryFrance
    CityLyon
    Period28/08/1731/08/17

    Keywords

    • Adverse drug extraction
    • Convolutional neural network
    • Social media

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