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Interactive Attention Network for Adverse Drug Reaction Classification

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Artificial Intelligence and Natural Language (AINL 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 930))

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Abstract

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.

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Notes

  1. 1.

    https://web-radr.eu/.

  2. 2.

    https://github.com/songyouwei/ABSA-PyTorch.

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Acknowledgments

This work was supported by the Russian Science Foundation Grant No. 18-11-00284. The authors are grateful to Elena Tutubalina for useful discussions about this study.

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  1. Kazan (Volga Region) Federal University, Kazan, Russia

    Ilseyar Alimova & Valery Solovyev

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  1. Ilseyar Alimova
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  2. Valery Solovyev
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Correspondence to Ilseyar Alimova .

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  1. Data and Web Science Group, University of Mannheim, Mannheim, Baden-Württemberg, Germany

    Dmitry Ustalov

  2. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  3. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

  4. Mendel University, Brno, Czech Republic

    Jan Žižka

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Alimova, I., Solovyev, V. (2018). Interactive Attention Network for Adverse Drug Reaction Classification. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-01204-5_18

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