preprints.org > computer science and mathematics > probability and statistics > doi: 10.20944/preprints202407.1735.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 20 July 2024 / Approved: 22 July 2024 / Online: 22 July 2024 (17:35:47 CEST)

Safety signal detection, an integral component of Pharmacovigilance (PhV), aims to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. Post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals but there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings, coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS), demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.

drug safety; association rule mining; Apriori; safety data; disproportionality measures; pharmacovigilance; co-occurrence analysis

Computer Science and Mathematics, Probability and Statistics

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