ArticleJMIR medical informatics2022
Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- The Effect of a Traditional Chinese Medicine Course on Western Medicine Students' Attitudes Toward Traditional Chinese Medicine: Self-Controlled Pre-Post Questionnaire Study.JMIR medical education · 2026Article
- Artificial Intelligence for Spleen-Stomach Disorders in Traditional Chinese Medicine: Integrating Knowledge Graphs with Intelligent Diagnosis and Treatment.Current medical science · 2025Review
- Drug repositioning model based on knowledge graph embedding.Scientific reports · 2025Article
- Pharmacological advances in multi-targeted strategies for type 2 diabetes mellitus: a systematic perspective based on traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- Research on Traditional Chinese Medicine: Domain Knowledge Graph Completion and Quality Evaluation.JMIR medical informatics · 2024Article
- 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.Frontiers in pharmacology · 2023Article
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4 authors.
Funding
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Abstract
backgroundKnowledge 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).
objectiveThis 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.
methodsA 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.
resultsExperiments 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.
conclusionsExperiments 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.
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