Benjamin Kompa1,2, Joe B Hakim3, Anil Palepu3, Kathryn Grace Kompa4, Michael Smith5, Paul A Bain6, Stephen Woloszynek7, Jeffery L Painter8, Andrew Bate9,10,11, Andrew L Beam12,13,14. 1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. 2. CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 3. Department of Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA. 4. Tufts University School of Medicine, Boston, MA, USA. 5. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 6. Countway Library of Medicine, Harvard Medical School, Boston, MA, USA. 7. Beth Israel Deaconess Medical Center, Boston, MA, USA. 8. GlaxoSmithKline, Durham, NC, USA. 9. GlaxoSmithKline, Brentford, UK. 10. Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, University of London, London, UK. 11. Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA. 12. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Andrew_Beam@hms.harvard.edu. 13. CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Andrew_Beam@hms.harvard.edu. 14. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Andrew_Beam@hms.harvard.edu.
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.
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.
Authors: Michael L Chen; Akshith Doddi; Jimmy Royer; Luca Freschi; Marco Schito; Matthew Ezewudo; Isaac S Kohane; Andrew Beam; Maha Farhat Journal: EBioMedicine Date: 2019-04-29 Impact factor: 8.143
Authors: Jonathan M Stokes; Kevin Yang; Kyle Swanson; Wengong Jin; Andres Cubillos-Ruiz; Nina M Donghia; Craig R MacNair; Shawn French; Lindsey A Carfrae; Zohar Bloom-Ackermann; Victoria M Tran; Anush Chiappino-Pepe; Ahmed H Badran; Ian W Andrews; Emma J Chory; George M Church; Eric D Brown; Tommi S Jaakkola; Regina Barzilay; James J Collins Journal: Cell Date: 2020-02-20 Impact factor: 41.582
Authors: John Jumper; Richard Evans; Alexander Pritzel; Tim Green; Michael Figurnov; Olaf Ronneberger; Kathryn Tunyasuvunakool; Russ Bates; Augustin Žídek; Anna Potapenko; Alex Bridgland; Clemens Meyer; Simon A A Kohl; Andrew J Ballard; Andrew Cowie; Bernardino Romera-Paredes; Stanislav Nikolov; Rishub Jain; Demis Hassabis; Jonas Adler; Trevor Back; Stig Petersen; David Reiman; Ellen Clancy; Michal Zielinski; Martin Steinegger; Michalina Pacholska; Tamas Berghammer; Sebastian Bodenstein; David Silver; Oriol Vinyals; Andrew W Senior; Koray Kavukcuoglu; Pushmeet Kohli Journal: Nature Date: 2021-07-15 Impact factor: 49.962