Knowledge graph for traditional Chinese medicine diagnosis and treatment of diabetic retinopathy: design, construction, and applications

AIM: To develop a traditional Chinese medicine (TCM) knowledge graph (KG) for diabetic retinopathy (DR) diagnosis and treatment by integrating literature and medical records, thereby enhancing TCM knowledge accessibility and providing innovative approaches for TCM inheritance and DR management. METHODS: First, a KG framework was established with a schema-layer design. Second, high-quality literature and electronic medical records served as data sources. Named entity recognition was performed using the ALBERT-BiLSTM-CRF model, and semantic relationships were curated by domain experts. Third, knowledge fusion was mainly achieved through an alias library. Subsequently, the data layer was mapped to the schema layer to refine the KG, and knowledge was stored in Neo4j. Finally, exploratory work on intelligent question answering was conducted based on the constructed KG. RESULTS: In Neo4j, a KG for TCM diagnosis and treatment was constructed, incorporating 6 types of labels, 5 types of relationships, 5 types of attributes, 822 nodes, and 1,318 relationship instances. This systematic KG supports logical reasoning and intelligent question answering. The question answering model achieved a precision of 95%, a recall of 95%, and a weighted F1-score of 95%. CONCLUSION: This study proposes a semi-automatic knowledge-mapping scheme to balance integration efficiency and accuracy. Clinical data-driven entity and relationship construction enables digital dialectical reasoning. Exploratory applications show the KG’s potential in intelligent question answering, providing new insights for TCM health management.