Automatic Extraction of Drug Adverse Effects From Product Characteristics (SPCs): A Text Versus Table Comparison

Stud Health Technol Inform. 2016;228:339-43.


Background: Potential adverse effects (AEs) of drugs are described in their summary of product characteristics (SPCs), a textual document. Automatic extraction of AEs from SPCs is useful for detecting AEs and for building drug databases. However, this task is difficult because each AE is associated with a frequency that must be extracted and the presentation of AEs in SPCs is heterogeneous, consisting of plain text and tables in many different formats.

Methods: We propose a taxonomy for the presentation of AEs in SPCs. We set up natural language processing (NLP) and table parsing methods for extracting AEs from texts and tables of any format, and evaluate them on 10 SPCs.

Results: Automatic extraction performed better on tables than on texts.

Conclusion: Tables should be recommended for the presentation of the AEs section of the SPCs.

Publication types

  • Comparative Study

MeSH terms

  • Automation*
  • Drug-Related Side Effects and Adverse Reactions*
  • Humans
  • Information Storage and Retrieval*
  • Natural Language Processing
  • Product Surveillance, Postmarketing
  • Software