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Using Machine Learning for Pharmacovigilance: A Systematic Review
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering. School of Business and Economics, Maastricht University, Tongersestraat 53, 6211 LM Maastricht, The Netherlands.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4029-6574
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-0322-8698
2022 (English)In: Pharmaceutics, E-ISSN 1999-4923, Vol. 14, no 2, article id 266Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 14, no 2, article id 266
Keywords [en]
pharmacovigilance, adverse drug reactions, ADRs, computational linguistics, machine learning, public health, user-generated content
National Category
Pharmaceutical Sciences Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-89482DOI: 10.3390/pharmaceutics14020266ISI: 000777690500001PubMedID: 35213998Scopus ID: 2-s2.0-85124076227OAI: oai:DiVA.org:ltu-89482DiVA, id: diva2:1643811
Note

Validerad;2022;Nivå 2;2022-03-11 (johcin);

Funder: Applied AI Digital Innovation Hub North project, funded by the European Regional Development Fund.

Available from: 2022-03-11 Created: 2022-03-11 Last updated: 2024-07-04Bibliographically approved

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Liwicki, MarcusBota, András

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