Predictive Modeling Of Drug Safety Using Adverse Drug Reaction Data

Abstract

Monitoring drug safety is important to protect patients, but traditional systems are slow because they rely on manual reporting and looking at data after problems occur. This study uses machine learning to quickly analyze large datasets like FDA FAERS and WHO VigiBase to detect harmful drug reactions earlier. By processing data such as patient details, drug information, and past reactions, models like decision trees, SVMs, and neural networks can predict serious side effects more accurately. These models improve early warnings, reduce missed risks, and help regulators act faster. Overall, the research shows how AI can make drug safety monitoring more efficient and reliable, despite challenges like complex and imbalanced data.