Skip to main content

ORIGINAL RESEARCH article

Front. Med.
Sec. Family Medicine and Primary Care
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1512329
This article is part of the Research Topic Artificial Intelligence in Traditional Medicine Research and Application View all 3 articles

Research on a Traditional Chinese Medicine Case-Based Question-Answering System Integrating Large Language Models and Knowledge Graphs

Provisionally accepted
Yuchen Duan Yuchen Duan 1Qingqing Zhou Qingqing Zhou 1Yu Li Yu Li 1Chi Qin Chi Qin 1Ziyang Wang Ziyang Wang 1Hongxing Kan Hongxing Kan 1,2Jili Hu Jili Hu 1,2*
  • 1 School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, Anhui Province, China
  • 2 Center for Xin’an Medicine and Modernization of Traditional Chinese Medicine of IHM, Anhui University of Chinese Medicine, Hefei, China

The final, formatted version of the article will be published soon.

    Introduction: Traditional Chinese Medicine (TCM) case records encapsulate vast clinical experiences and theoretical insights, holding significant research and practical value. However, traditional case studies face challenges such as large data volumes, complex information, and difficulties in efficient retrieval and analysis. This study aimed to address these issues by leveraging modern data techniques to improve access and analysis of TCM case records.Methods: A total of 679 case records from Wang Zhongqi, a renowned physician of Xin'an Medicine, a branch of TCM, covering 41 diseases, were selected. The study involved four stages: pattern layer construction, knowledge extraction, integration, and data storage and visualization. A large language model (LLM) was employed to automatically extract key entities, including symptoms, pathogenesis, treatment principles, and prescriptions. These were structured into a TCM case knowledge graph.Results: The LLM successfully identified and extracted relevant entities, which were then organized into relational triples. A TCM case query system based on natural language input was developed. The system's performance, evaluated using the RAGAS framework, achieved high scores: 0.9375 in faithfulness, 0.9686 in answer relevancy, and 0.9500 in context recall; In human evaluations, the levels of safety and usability are significantly higher than those of LLMs without using RAG.Discussion: The results demonstrate that integrating LLMs with a knowledge graph significantly enhances the efficiency and accuracy of retrieving TCM case information. This approach could play a crucial role in modernizing TCM research and improving access to clinical insights. Future research may explore expanding the dataset and refining the query system for broader applications.

    Keywords: Large Language Model, knowledge graph, Traditional Chinese Medicine, Question answering system, interdisciplinary research

    Received: 16 Oct 2024; Accepted: 20 Dec 2024.

    Copyright: © 2024 Duan, Zhou, Li, Qin, Wang, Kan and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jili Hu, School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230038, Anhui Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.