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Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection.

Eric C Leas1,2, John W Ayers2,3,4, Nimit Desai2

  • 1Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States.

Journal of Medical Internet Research
|May 2, 2024
PubMed
Summary

Large language models like ChatGPT can accurately identify adverse events (AEs) in social media posts, closely matching human annotator performance. This demonstrates their potential for efficient biomedical research and content analysis.

Keywords:
AIChatGPTLLMadverse eventsannotationartificial intelligencecannabisdelta-8-THCdelta-8-tetrahydrocannabiollarge language modeltext analysis

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Area of Science:

  • Artificial Intelligence
  • Biomedical Research
  • Computational Linguistics

Background:

  • Content analysis of social media data is crucial for identifying adverse events (AEs).
  • Manual annotation by humans is time-consuming and resource-intensive.
  • Large language models (LLMs) offer potential for automating and improving content analysis efficiency.

Purpose of the Study:

  • To evaluate the efficacy of ChatGPT, a large language model, in identifying adverse events (AEs) from social media data.
  • To compare ChatGPT's performance against human annotators in detecting AEs related to delta-8-tetrahydrocannabinol.
  • To assess the potential of LLMs in enhancing the efficiency of biomedical research content analysis.

Main Methods:

  • A case study was conducted using social media posts concerning delta-8-tetrahydrocannabinol.
  • ChatGPT was provided with identical instructions given to human annotators for AE detection.
  • Performance was measured by comparing ChatGPT's AE detection rates with those of human annotators, using Fleiss' kappa for inter-rater reliability.

Main Results:

  • ChatGPT demonstrated high agreement with human annotators in detecting any adverse event (94.4%, κ=0.95).
  • ChatGPT achieved near-perfect agreement in identifying serious adverse events (99.3%, κ=0.96).
  • The results indicate ChatGPT's capability to accurately and efficiently replicate human annotation tasks.

Conclusions:

  • Large language models, exemplified by ChatGPT, show significant potential for accurate and efficient content analysis in biomedical research.
  • LLMs can effectively assist in identifying adverse events from unstructured data like social media.
  • Further research is recommended to explore generalizability across different models, datasets, and analytical tasks.