Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study.

Background: Knowledge discovery from treatment data records from Chinese physicians is a dramatic challenge in the application of artificial intelligence (AI) models to the research of traditional Chinese medicine (TCM).

Objective: This paper aims to construct a TCM knowledge graph (KG) from Chinese physicians and apply it to the decision-making related to diagnosis and treatment in TCM.

Methods: A new framework leveraging a representation learning method for TCM KG construction and application was designed. A transformer-based Contextualized Knowledge Graph Embedding (CoKE) model was applied to KG representation learning and knowledge distillation. Automatic identification and expansion of multihop relations were integrated with the CoKE model as a pipeline. Based on the framework, a TCM KG containing 59,882 entities (eg, diseases, symptoms, examinations, drugs), 17 relations, and 604,700 triples was constructed. The framework was validated through a link predication task.

Results: Experiments showed that the framework outperforms a set of baseline models in the link prediction task using the standard metrics mean reciprocal rank (MRR) and Hits@N. The knowledge graph embedding (KGE) multitagged TCM discriminative diagnosis metrics also indicated the improvement of our framework compared with the baseline models.

Conclusions: Experiments showed that the clinical KG representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment. Our framework shows superiority of application prospects in tasks such as KG-fused multimodal information diagnosis, KGE-based text classification, and knowledge inference-based medical question answering.