Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 1, 2017

Abstract

OBJECTIVE: Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media.

Authors

  • Anne Cocos
    Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia Philadelphia, PA, USA.
  • Alexander G Fiks
    Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia Philadelphia, PA, USA.
  • Aaron J Masino
    Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, 3535 Market Street, Suite 1024, Philadelphia, PA, 19104, USA. masinoa@email.chop.edu.

Keywords

External Resources

Popular Topics
Recent Journals