Leveraging Transformer Models for Enhanced Pharmacovigilance: A Comparative Analysis of ADR Extraction from Biomedical and Social Media Texts

The extraction of Adverse Drug Reactions from biomedical text is a critical task in the field of healthcare and pharmacovigilance. It serves as a cornerstone for improving patient safety by enabling the early identification and mitigation of potential risks associated with pharmaceutical treatments. This process not only helps in detecting harmful side effects that may not have been evident during clinical trials but also contributes to the broader understanding of drug safety in real-world settings, ultimately guiding regulatory actions and informing clinical practices. In this study, we conducted a comprehensive evaluation of eleven transformer-based models for ADR extraction, focusing on two widely used datasets: CADEC and SMM4H. The task was approached as a sequence labeling problem, where each token in the text is classified as part of an ADR or not. Various transformer architectures, including BioBERT, PubMedBERT, and SpanBERT, were fine-tuned and evaluated on these datasets. BioBERT demonstrated superior performance on the CADEC dataset, achieving an impressive F1 score of 86.13%, indicating its strong capability in recognizing ADRs within patient narratives. On the other hand, SpanBERT emerged as the top performer on the SMM4H dataset, with an F1 score of 84.29%, showcasing its effectiveness in processing the more diverse and challenging social media data. These results highlight the importance of selecting appropriate models based on the specific characteristics such as text formality, domain-specific language, and task complexity to achieve optimal ADR extraction performance.