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Though Vaccines are instrumental in global health, mitigating infectiousdiseases and pandemic outbreaks, they can occasionally lead to adverse events(AEs). Recently, Large Language Models (LLMs) have shown promise in effectivelyidentifying and cataloging AEs within clinical reports. Utilizing data from theVaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this studyparticularly focuses on AEs to evaluate LLMs' capability for AE extraction. Avariety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama 2,were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict matchand 0.816 for relaxed match. The encouraging performance of the AE-GPTunderscores LLMs' potential in processing medical data, indicating asignificant stride towards advanced AE detection, thus presumably generalizableto other AE extraction tasks.

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events