Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review

Benjamin Kompa, Joe B. Hakim, Anil Palepu, Kathryn Grace Kompa, Michael Smith, Paul A. Bain, Stephen Woloszynek, Jeffery L. Painter, Andrew Bate and Andrew L. Beam ()
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Benjamin Kompa: Harvard Medical School
Joe B. Hakim: Harvard-MIT
Anil Palepu: Harvard-MIT
Kathryn Grace Kompa: Tufts University School of Medicine
Michael Smith: Harvard T.H. Chan School of Public Health
Paul A. Bain: Countway Library of Medicine, Harvard Medical School
Stephen Woloszynek: Beth Israel Deaconess Medical Center
Jeffery L. Painter: GlaxoSmithKline
Andrew Bate: GlaxoSmithKline
Andrew L. Beam: Harvard Medical School

Drug Safety, 2022, vol. 45, issue 5, No 8, 477-491

Abstract: Abstract Introduction Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear. Objective The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning. Design The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise. Results The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices. Conclusion Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing.

Date: 2022
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DOI: 10.1007/s40264-022-01176-1

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