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Interactive Attention Network for Adverse Drug Reaction Classification

  • Ilseyar Alimova
  • Valery Solovyev
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 930)

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.

Keywords

Adverse drug reactions Text mining Natural language processing Health social media analytics Machine learning Deep learning 

Notes

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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Authors and Affiliations

  1. 1.Kazan (Volga Region) Federal UniversityKazanRussia

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