Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods

Stud Health Technol Inform. 2017;245:322-326.

Abstract

Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary source to current pharmacovigilance systems. However, the performance of text mining tools applied to social media text data to discover ADRs needs to be evaluated. In this paper, we introduce the approach developed to mine ADR from French social media. A protocol of evaluation is highlighted, which includes a detailed sample size determination and evaluation corpus constitution. Our text mining approach provided very encouraging preliminary results with F-measures of 0.94 and 0.81 for recognition of drugs and symptoms respectively, and with F-measure of 0.70 for ADR detection. Therefore, this approach is promising for downstream pharmacovigilance analysis.

Keywords: Data Mining; Pharmacovigilance; Social Media.

MeSH terms

  • Adverse Drug Reaction Reporting Systems
  • Data Mining*
  • Drug-Related Side Effects and Adverse Reactions*
  • Humans
  • Pharmacovigilance
  • Semantics*
  • Social Media*