Adverse Drug Event

Adverse drug events (ADEs) represent unintended harmful reactions to medications, and research focuses on improving their prediction and detection to enhance patient safety. Current efforts utilize machine learning, employing various architectures like large language models (LLMs), graph neural networks, and tensor factorization, often applied to diverse data sources including electronic health records, clinical trials, and social media. These advancements aim to improve the accuracy and efficiency of ADE identification, ultimately informing drug development, regulatory decisions, and personalized medicine strategies.