Pharmacovigilance in the Age of Legalized Cannabis: Using Social Media to Monitor Drug-Drug Interactions Between Immunosuppressants and Cannabis-Derived Products
Objective: To enhance the accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural language queries (NLQs) into Structured Q
ObjectiveThis study aims to identify the drugs most commonly associated with kidney stone-related adverse events using data from the FDA Adverse Event Reporting System (FAERS), providing insights for
This database contains the data obtained from the evaluation of the impact of an educational intervention on pharmcovigilance abilities and attitudes in health professionals, carried out by the autors
