Predictive Analytics for Anticipating Adverse Drug Reactions to ameliorate Drug Development and Prescription Practices

Integrating Artificial Intelligence into Health Care involves employing AI technologies and Algorithms to reinforce various facets of Medical Domain including discovery of the drugs and its subsequent development. However, with the advent of various drugs for several medical conditions, comes the side effects. This work aims to present a comprehensive approach to drug side effect prediction using a machine learning algorithm for Drug organization and analysis of extensive drug-related datasets. Utilizing advanced data-driven methodologies techniques, the system aims to predict potential side effects early in the drug development lifecycle. The repository used serves as a crucial infrastructure for managing diverse data, including information on drugs, chemical properties, and historical side effect records. The main objective of this research work is to enhance patient safety by minimizing adverse drug reactions and reducing treatment discontinuation rates, thereby mitigating the economic impact on pharmaceutical companies. The system enables informed decision-making throughout the drug development process by empowering researchers, healthcare practitioners, and regulatory bodies with an integrated decision-support the DBMS, acting as the backbone for the development of this predictive model. The proposed model integrated into drug development, aims to revolutionize drug safety practices, offering a proactive and data-driven approach. The predicted outcomes have broader implications for advancing healthcare technologies, optimizing drug development practices, and contributing to more efficient and informed pharmaceutical sciences.