Construction of a knowledge graph of acupuncture famous doctors' medical cases based on information extraction from large language model

Objective: To construct a knowledge graph of acupuncture case studies from renowned acupuncturists, analyze acupuncture medical case knowledge, mine implicit knowledge, and perform visual representation, providing methodological references for the research of acupuncture medical cases. Methods: The types of knowledge entities and relationships between entities involved in acupuncture case studies from renowned acupuncturists were sorted out. The TCM Miner platform was used for text annotation, and a large language model was employed to identify and label entities. The relationships between entities were determined based on Chinese medicine-related standards, literature reviews, and established rules. After knowledge fusion, the data was imported into the Neo4j graph database using the Cypher language for storage and visual representation, constructing a knowledge graph of acupuncture medical cases. Results: The acupuncture medical case knowledge graph contained 577 nodes and 3,905 relationships, with a schema comprising 5 entity classes and 5 relationship types. Through Cypher language queries, knowledge can be visually presented in four aspects: commonly used treatment methods of practitioners, research on the relationship between acupuncture techniques and diseases, exploration of disease-acupoint patterns, and research on other therapies and diseases. Discussion: The knowledge graph constructed in this study can visually display the knowledge and implicit relationships recorded in acupuncture medical cases, making it suitable for knowledge mining and visual representation of acupuncture medical cases.