Journal of Pathology, Communicable Diseases and Preventive Medicines

Authors: Dr. Rohan Mehta, Dr. Priya Choudhary

ABSTRACT: Pharmacovigilance is a critical aspect of healthcare that focuses on detecting, assessing, and preventing adverse drug reactions (ADRs) to ensure patient safety. With the exponential growth of medical data, traditional methods of monitoring drug safety have become insufficient. Data mining techniques have emerged as powerful tools that can analyze large-scale datasets to identify hidden patterns and potential drug-related risks. This paper explores the application of various data mining methodologies in pharmacovigilance, highlighting their role in signal detection, risk assessment, and predictive analysis. Moreover, it discusses challenges associated with data heterogeneity, privacy concerns, and methodological limitations. The paper also explores the future scope of integrating advanced computational approaches, such as machine learning and artificial intelligence, with pharmacovigilance systems.

KEYWORDS: Pharmacovigilance, Data Mining, Adverse Drug Reactions, Signal Detection, Predictive Analysis, Machine Learning, Drug Safety Monitoring.