信阳师范大学学报（自然科学版）2025，Vol.38Issue(3)：289-296,8.DOI:10.3969/j.issn.2097-583X.2025.03.006
基于GPT模型的医疗知识图谱构建方法
Construction method of medical knowledge graphs based on the GPT model
摘要
Abstract
A method for building knowledge graphs was proposed based on the GPT model,using electronic medical records as the corpus.Through prompt design,GPT was guided to achieve objectives such as structure design,knowledge extraction,relationship limitations and format conversion.This approach facilitates tasks such as ontology construction and knowledge management,ultimately integrating the results into a medical process knowledge graph.The results indicated that:(1)prompts can guide GPT to understand the task's essence and automatically construct the ontology model,but there are issues with accuracy and consistency;(2)GPT achieved an F1 score of 0.847 in the named entity recognition tasks,comparable to current mainstream deep learning models;(3)GPT has advantages in synonym recognition,acronym replacement and hidden relationship inference.Additionally,the efficiency of this GPT-based method compared to traditional knowledge graph construction methods was explored,providing some valuable insights into building knowledge graphs in the context of large language models.关键词
智慧医疗/知识图谱构建/GPT模型/提示工程Key words
smart healthcare/knowledge graph construction/GPT model/prompt engineering分类
社会科学引用本文复制引用
宋海涛,陈达,高军礼,宋俊,钟慧..基于GPT模型的医疗知识图谱构建方法[J].信阳师范大学学报（自然科学版）,2025,38(3):289-296,8.基金项目
国家自然科学基金项目(72171089) （72171089）
广州市基础研究计划市校(院)联合资助项目(2023A03J0279) （院）
