Data mining methods, tasks, and algorithms for adverse drug reaction analysis in pharmacovigilance: A scoping review

BACKGROUND: Pharmacovigilance is dedicated to the identification, evaluation, and prevention of adverse effects associated with the use of medicines after their commercialization. In this context, data mining techniques have been widely employed for the detection of safety signals. Although machine learning algorithms show potential to identify complex patterns and improve the prediction of adverse drug reactions, their application in pharmacovigilance databases remains limited. OBJECTIVE: To map the computational approaches used in pharmacovigilance, to identify the prevalence of traditional statistical methods and data mining techniques, and to assess the role of machine learning algorithms. METHODS: This scoping review followed PRISMA‑ScR guidelines (protocol registered on OSF: https://doi.org/10.17605/OSF.IO/KZJDT). We searched PubMed, Scopus, Embase, and Web of Science for English primary studies published from 2015 to July 2025 that applied data mining techniques to pharmacovigilance databases. Data extraction used a standardized form. RESULTS: The search identified 1,468 records, of which 162 studies were included after screening and eligibility assessment. Traditional disproportionality methods, such as Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR), were used in 87.7% of the studies, whereas 12.3% applied machine learning or deep learning techniques, mainly in classification tasks, with logistic regression being the most frequently employed algorithm. The most investigated drugs included immune checkpoint inhibitors, such as nivolumab, pembrolizumab, atezolizumab, and durvalumab. The most studied therapeutic classes were antineoplastic agents, immunosuppressants, and psycholeptics. CONCLUSION: Signal detection in pharmacovigilance remains predominantly based on classical statistical methods. Progress in the field is still constrained by the slow incorporation of advanced machine learning techniques and the limited public availability of datasets used in analyses.