Construction and Research of Pediatric Pulmonary Disease Diagnosis and Treatment Experience Knowledge Graph Based on Professor Wang Lie’s Experience

To construct a knowledge graph of Professor Wang Lie, a Master of Traditional Chinese Medicine(TCM), on the diagnostics and treatment of pediatric pulmonary diseases, by providing a foundation for the inheritance of his academic thoughts and clinical experience in TCM. In this study, we focused on Professor Wang’s diagnostics and treatment experience in pediatric pulmonary diseases. By utilizing unstructured text data and integrating his academic thoughts and clinical experience, a knowledge graph was built using the Neo4j graph database. Unstructured text experience data meeting the requirements was selected, entered into the Excel® table to establish the Professor Wang’s database for the diagnosis and treatment of pediatric pulmonary diseases, and standardized processing of data was performed. In the Neo4j graph database, the schema-level graph comprised 20 entity concept labels, 29 entity nodes, 28 entity relationships, and 22 types of entity relationships. The data-level graph included 20 entity concept labels, 870 entity nodes, 22 types of entity relationships, and 1469 entity relationships. This enabled the visualization of Professor Wang’s diagnostics, treatment principles, prescription strategies, and medication patterns for pediatric pulmonary diseases. By constructing a knowledge graph of Professor’s diagnostics and treatment for pediatric pulmonary diseases based on the Neo4j graph database, Knowledge extraction, integration, and representation were attained. This work also lays the foundation for optimizing TCM treatment plans for pediatric pulmonary diseases and building intelligent diagnosis and expert systems for TCM-based treatment in the pediatric population.