Using Machine Learning for Pharmacovigilance: A Systematic Review

Patrick Pilipiec, Marcus Liwicki, András Bota*

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

Abstract

Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance.

Original languageEnglish
Article number266
Number of pages25
JournalPharmaceutics
Volume14
Issue number2
DOIs
Publication statusPublished - 23 Jan 2022

Keywords

  • ADRs
  • adverse drug reactions
  • computational linguistics
  • machine learning
  • pharmacovigilance
  • public health
  • user-generated content
  • WORDS
  • SOCIAL MEDIA
  • TEXT
  • HEALTH

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