Adverse Drug Event Prediction in Pharmacovigilance Using KG-Augmented LLM

Abstract

Adverse drug events (ADEs), referring to harmful and unintended reactions from routine medication use, continue to pose a significant challenge for pharmacovigilance, one that can be addressed through AI-enabled Web services. In the United States, more than two million serious ADEs have been reported among hospitalized patients. These instances can be reduced if hospitals and health centers use systems to identify, anticipate, or estimate the likelihood of ADE occurrences documented on clinical notes. While computational methods leveraging natural language processing (NLP) have been developed for ADE detection in clinical texts, most of them rely on classical machine learning or deep learning models that often fail to incorporate domain-specific biomedical knowledge. We propose a knowledge graph-augmented large language model (LLM) framework for ADE prediction. Our approach uses zero shot prompting and instruction-based prompting for finetuning, while integrating structured knowledge from a Drug Adverse Event Knowledge Graph (KG) constructed with the Ontology of Adverse Events (OAE), RxNorm, and the Cadec ADE dataset. Experiments on ADE prediction indicate that KG-enhanced LLMs outperform standard LLM-based approaches, achieving up to an 8% improvement in F1 score with Mistral 7B and a 16% boost with GPT-4.1 on instruction-tuned ADE data, as well as a 1% and 2% F1-score increase, respectively, on instruction-tuned TAC data. These results suggest the effectiveness of combining symbolic biomedical knowledge with LLMs for advancing ADE prediction and improving drug safety in clinical settings.