Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Mar 9, 2015

Abstract

OBJECTIVE: Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media.

Authors

  • Azadeh Nikfarjam
    Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA anikfarj@asu.edu graciela.gonzalez@asu.edu.
  • Abeed Sarker
    Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
  • Karen O'Connor
    Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
  • Rachel Ginn
    Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
  • Graciela Gonzalez
    Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA anikfarj@asu.edu graciela.gonzalez@asu.edu.

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