Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 2–Comparison of the Performance of Artificial Intelligence and Traditional Pharmacoepidemiological Techniques

Maurizio Sessa*, David Liang, Abdul Rauf Khan, Murat Kulahci, Morten Andersen

*Corresponding author for this work

Research output: Contribution to journalReviewResearchpeer-review

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Abstract

Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques. Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated. Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient’s characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods. Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.

Original languageEnglish
Article number568659
JournalFrontiers in Pharmacology
Volume11
Number of pages10
ISSN1663-9812
DOIs
Publication statusPublished - 14 Jan 2021

Bibliographical note

Funding Information:
MA professorship is supported by a grant from the Novo Nordisk Foundation to the University of Copenhagen (NNF15SA0018404). MS is supported by a grant from Helsefonden (20-B-0059).

Publisher Copyright:
© Copyright © 2021 Sessa, Liang, Khan, Kulahci and Andersen.

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Pharmacoepidemiology
  • Systematic review

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