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

Abstract

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.

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

“…This discrepancy between acquired knowledge and clinical reality may result in limited clinical guidance. Furthermore, most existing studies focus on general knowledge graphs ( Long et al, 2019 ; Weng et al, 2022 ), with a relative lack of graph studies that incorporate clinical disease characteristics. The application of graphs aims to obtain more accurate and comprehensive knowledge of TCM diagnosis and treatment, screen effective diagnosis and treatment plans, and improve clinical efficacy.…”

Research on Traditional Chinese Medicine: Domain Knowledge Graph Completion and Quality Evaluation

“…Experts judged the correctness of these triples, and the accuracy was calculated [41]. Through complex network analysis methods such as clustering [42] and t-SNE visualization [43] (a visualization method of data after dimensionality reduction), KG disease classification knowledge was summarized and compared with prior knowledge, aiming to determine whether the data distribution of the constructed graph was consistent with prior knowledge.…”

Precision Nursing Research Based on Multimodal Knowledge Graph

“…Rotmensch and Halpern [19] organized the content of the "Medical Equipment" journal into a knowledge graph, providing an intuitive representation of the elds and topics covered by the journal. Weng H and colleagues [20] researched the construction of a knowledge graph for traditional Chinese medicine. Yin Y [21] and colleagues, based on the construction of a traditional Chinese medicine knowledge graph, developed intelligent applications for medical knowledge.…”