Citation:

Rajesh Munirathnam, 2022. "The Future of Pharmacovigilance: Using Data Science to Predict and Prevent Adverse Drug Reactions", ESP Journal of Engineering & Technology Advancements, 2(4): 130-141.

Abstract:

Pharmacovigilance (PV), which is the study of measuring and assessing the adverse effects of drugs, has seen enhancements in the last couple of decades. The conventional systems have mainly incorporated file-based reporting, reactive monitoring, and post-event analysis of ADRs. But with the flood of real-world health information and the emergence of new data analysis methods, pharmacovigilance is going through a shift. New technology tools such as machine learning (ML), artificial intelligence (AI), and other analytical big data tools are powering PV towards a more predictive mode. Through the use of big data in EHRs, social media, clinical trials, and patient registries, data science is able to identify ADRs earlier in order to prevent them. It nowadays offers the possibilities of risk assessment, recommended dosages of a drug, and the prevention of severe ADRs. This paper aims to discuss the present and future of data science in pharmacovigilance, the main methodologies, case studies, and challenges that denote the potential of preventing ADRs.

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Keywords:

Pharmacovigilance, Adverse Drug Reactions, Data Science, Machine Learning, Artificial Intelligence, Big Data, Drug Safety.