Journal of Pathology, Communicable Diseases and Preventive Medicines

Authors: Dr. Richa Sharma, Mr. Karan Mehta

ABSTRACT: The integration of big data analytics and machine learning (ML) has revolutionized pharmacovigilance by enabling real-time detection, prediction, and prevention of adverse drug reactions (ADRs). Traditional drug safety monitoring faces challenges such as underreporting, delayed signal detection, and fragmented data sources. Big data platforms consolidate diverse datasets from electronic health records, social media, clinical trials, and wearable devices, while ML algorithms facilitate pattern recognition, risk stratification, and predictive modeling. This paper explores the principles, methodologies, challenges, and regulatory considerations in implementing big data and ML for drug safety monitoring. Tables summarizing applications, benefits, and limitations are included. The adoption of these technologies promises enhanced patient safety, optimized therapeutic interventions, and data-driven decision-making in pharmacovigilance.

KEYWORDS: Pharmacovigilance, Big Data, Machine Learning, Adverse Drug Reactions, Drug Safety, Predictive Analytics