Transformer-based models for ADR detection: cross-drug validation and benchmarking against large language models.

Background: Adverse drug reactions (ADRs) are harmful side effects of medications. Social media provides real-time, patient-generated data, though its unstructured format presents challenges. Natural language processing and transfer learning offer promising solutions. Objective: This study aimed to evaluate whether transformer-based models fine-tuned on a general ADR dataset can effectively classify ADRs from tweets related to glucagon-like peptide-1 (GLP-1) receptor agonists and to benchmark their performance against state-of-the-art large language models (LLMs). Design: This study employed a machine learning approach using transformer-based language models to classify ADRs in social media. Methods: BERT (bidirectional encoder representations from transformers)-base, BERTweet-base, and GPT-2 (Generative Pre-Trained Transformer-2) models were fine-tuned using Sarker and SIDER (Side Effect Resource) datasets for ADR classification. The test dataset comprised 396 tweets mentioning GLP-1 receptor agonists that were categorized as personal experiences. Model performance was primarily evaluated using the F1 score, which was used to select the optimal model. In addition, the fine-tuned transformer models were benchmarked against state-of-the-art LLMs, including ChatGPT 4o, ChatGPT 4o-mini, and Gemini 2.5 Flash. Results: Among 396 tweets, 116 (29.3%) were classified as ADRs and 280 (70.7%) as non-ADRs. Among the transformer-based models, BERTweet-base achieved the highest performance (accuracy: 0.835, F1: 0.729), outperforming both BERT-base (accuracy: 0.826, F1: 0.679) and GPT-2 (accuracy: 0.766, F1: 0.628). Among the LLMs, ChatGPT 4o-mini demonstrated the best results (accuracy: 0.970, F1: 0.948), followed by Gemini 2.5 Flash (accuracy: 0.954, F1: 0.919) and ChatGPT 4o (accuracy: 0.936, F1: 0.895). Overall, LLMs substantially outperformed the fine-tuned transformer-based models. Conclusion: Fine-tuned transformer-based models demonstrated reasonable performance in ADR detection from GLP-1 receptor agonist tweets, with BERTweet-base performing best. However, state-of-the-art LLMs, particularly ChatGPT 4o-mini, substantially outperformed these models, highlighting their potential for pharmacovigilance tasks.