According to a well-written systematic review on the application of natural language processing (NLP), Pilipiec et al.,1 concluded that the analysis of data based on texts highlighting adverse events may constitute an improvement in current pharmacovigilance analysis and related data-gathering.
The increasing amount of user-generated content on the Internet is becoming a potential source of pharmacovigilance data which, with the advent of text mining techniques and artificial intelligence, has resulted in powerful algorithms and methods for NLP.
The aim of this study was to review the existing evidence on, and the effectiveness of NLP to understand user-generated content for pharmacovigilance.
From 5318 initially selected records, the authors chose and read the 16 publications considered relevant for the systematic review. The authors highlight several important findings from their study:
- Promising potential for the application of natural language processing for pharmacovigilance purposes
- Many of the identified adverse drug reactions, or ADRs, were consistent with those found in the package insert. However, there were some correctly identified new, previously unknown ADRs
- The application of computational linguistics may be useful for pharmacovigilance, as a complementary tool to retrieve ADRs shown on user-generated content
- 1.Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics. 2022;14(2). doi:10.3390/pharmaceutics14020266
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