Relation extraction using large language models: a case study on acupuncture point locations.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39208311

Abstract

OBJECTIVE: In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to explore the performance of LLMs in extracting acupoint-related location relations and assess the impact of fine-tuning on GPT's performance.

Authors

  • Yiming Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Xueqing Peng
    Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
  • Jianfu Li
    Mayo Clinic.
  • Xu Zuo
    The University of Texas Health Science Center at Houston.
  • Suyuan Peng
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; The Second Clinical College Guangzhou University of Chinese Medicine, China.
  • Donghong Pei
    The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Cui Tao
    The University of Texas Health Science Center at Houston, USA.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Na Hong
    Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, United States.

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