<![CDATA[Background: Adverse Drug Reactions (ADRs) remain a global health problem, increasing morbidity, mortality, and costs. The Spontaneous Reporting System (SRS), while central to pharmacovigilance, suffers from underreporting and delayed signal detection. Advances in big data and data mining offer solutions to these limitations.Objective: This review evaluates the use of statistical, Bayesian, and artificial intelligence (AI)-based methods to improve early detection of ADR signals in large pharmacovigilance databases.Method: A literature review was conducted on 12 studies applying statistical methods (reporting odds ratio and proportional reporting ratio), Bayesian approaches, and AI techniques (machine learning and natural language processing) to datasets including FAERS, WHO VigiBase, VigiFlow, and national AEFI systems.Results: Disproportionality analysis aided early screening but was limited in detecting rare events and prone to false positives. Bayesian methods improved stability and accuracy for low-frequency signals. Machine learning enhanced predictive performance and reduced false alarms, while NLP facilitated processing of unstructured reports. The combined application of these methods enhanced sensitivity, specificity, and validity of pharmacovigilance systems. Conclusion: The integration of big data with statistical, Bayesian, and AI approaches significantly advances pharmacovigilance by enabling faster and more accurate ADR detection, though challenges in data quality, privacy, and clinical validation remain.]]>
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