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Using Machine Learning for Pharmacovigilance: A Systematic Review

Pharmaceutics, 2022-01, Vol.14 (2), p.266 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1999-4923 ;EISSN: 1999-4923 ;DOI: 10.3390/pharmaceutics14020266 ;PMID: 35213998

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  • Title:
    Using Machine Learning for Pharmacovigilance: A Systematic Review
  • Author: Pilipiec, Patrick ; Liwicki, Marcus ; Bota, András
  • Subjects: ADRs ; adverse drug reactions ; Algorithms ; Artificial intelligence ; computational linguistics ; Data mining ; Digital libraries ; Drugs ; machine learning ; Maskininlärning ; Natural language processing ; Pharmaceutical industry ; pharmacovigilance ; Product safety ; Public health ; R&D ; Research & development ; Social networks ; Systematic Review ; User generated content
  • Is Part Of: Pharmaceutics, 2022-01, Vol.14 (2), p.266
  • Description: 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.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1999-4923
    EISSN: 1999-4923
    DOI: 10.3390/pharmaceutics14020266
    PMID: 35213998
  • Source: Directory of Open Access Scholarly Resources (ROAD)
    Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    SWEPUB Freely available online
    ProQuest Central
    DOAJ Directory of Open Access Journals
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