Pharmacovigilance Through Machine Learning: A Review

Abstract

Pharmacovigilance is the science and practices related to obtaining information, identifying, evaluating, verifying, and mitigating adverse effects with pharmaceutical products. In essence, pharmacovigilance sheets medicine safety. As medical specialists with a strong background in this area, pharmacists play crucial roles in maintaining health systems that support the appropriate and safe use of medication. The discipline of medicine has shown a great deal of interest in artificial intelligence (AI), a vast subfield of computer science, due to its capacity for pattern detection, problem solving, and decision making. As a branch of artificial intelligence (AI), machine learning (ML) focuses on computers' capacity to absorb information, learn from it, and organize the data they are processing by adjusting algorithms. Machine learning technique are supervised learning, Semi supervised learning, Unsupervised Learning, and Reinforcement learning. A fascinating trip through time, the history of machine learning is filled with significant events, creative thinkers, and ground-breaking inventions. We will explore the intriguing background of machine learning in this post and gain a deeper understanding of the technology that is changing the world. Machine learning having so many application in pharmacovigilance some are: Machine Learning to identify adverse drug reaction and adverse drug event, detect Patients at High Risk for ADRs, identify Image, Speech and Pattern Recognition, predicting dermatological, healthcare and covid-19 pandemic etc.