Unveiling differential adverse event profiles in vaccines via LLM text embeddings and ontology semantic analysis.

Journal: Journal of biomedical semantics
Published Date: May 23, 2025

Abstract

BACKGROUND: Vaccines are crucial for preventing infectious diseases; however, they may also be associated with adverse events (AEs). Conventional analysis of vaccine AEs relies on manual review and assignment of AEs to terms in terminology or ontology, which is a time-consuming process and constrained in scope. This study explores the potential of using Large Language Models (LLMs) and LLM text embeddings for efficient and comprehensive vaccine AE analysis.

Authors

  • Zhigang Wang
    Institute for Medical Science and Technology, University of Dundee, Dundee DD2 1FD, UK.
  • Xingxian Li
    College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Jie Zheng
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
  • Yongqun He
    University of Michigan Medical School, Ann Arbor, MI 48109 USA ; Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, 1301 MSRB III, 1150 W. Medical Dr., Ann Arbor, MI 48109 USA.

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