AE-GPT: Using Large Language Models to Extract Adverse Events from
  Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events
Yiming Li Jianfu Li Jianping He Cui Tao
Though Vaccines are instrumental in global health, mitigating infectious
diseases and pandemic outbreaks, they can occasionally lead to adverse events
(AEs). Recently, Large Language Models (LLMs) have shown promise in effectively
identifying and cataloging AEs within clinical reports. Utilizing data from the
Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study
particularly focuses on AEs to evaluate LLMs' capability for AE extraction. A
variety 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 3.5
model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match
and 0.816 for relaxed match. The encouraging performance of the AE-GPT
underscores LLMs' potential in processing medical data, indicating a
significant stride towards advanced AE detection, thus presumably generalizable
to other AE extraction tasks.