Abstract
Background: Pharmacovigilance (PV) is the activity to identify comprehensive information on the safety characteristics of the drug after its marketing. The PV data sources are dynamic, large, structured, and unstructured; therefore, the automation of data processing is essential. Purpose: This review aims to identify the machine learning applications in PV activities. Methods: Nine (9) studies that were published within the period from 2016 to 2020 were reviewed. The studies were extracted from two databases; PubMed and web of science. The review and analysis were done in December 2020. Results: The supervised and semi-supervised learning techniques are applied in the main three PV group activities; adverse drug reactions (ADRs) and signal detection, individual case safety reports (ICSRs) identification, and ADRs prediction. Future research is needed to identify the applicability of unsupervised learning in PV and to formulate the legal framework of the false positive predicted data.
Reference22 articles.
1. World Health Organization. The Importance of Pharmacovigilance [Internet]. 2002. Available from: https://apps.who.int/iris/handle/10665/42493. [Accessed: October 6, 2020]
2. European Medication Agency. Pharmacovigilance: Overview [Internet]. 2020. Available from: https://www.ema.europa.eu/en/human-regulatory/overview/pharmacovigilance-overview. [Accessed: October 7, 2020]
3. VigiBase now contains around 17 million ADR reports | SpringerLink [Internet]. 2020. Available from: https://link.springer.com/article/10.1007/s40278-018-45575-x. [Accessed: October 7, 2020]
4. Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. 2019
5. Agbabiaka TB, Savović J, Ernst E. Methods for causality assessment of adverse drug reactions: A systematic review. Drug Safety. 2008;31(1):21-37