ArticleInternational journal of ophthalmology2025
Knowledge graph for traditional Chinese medicine diagnosis and treatment of diabetic retinopathy: design, construction, and applications.
Article in International journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- AI-Driven Plant-Derived Anti-Infectives: Integrating Traditional Wisdom into Precision Medicine Against AMR.Life (Basel, Switzerland) · 2026Review
- Construction and Applications of Knowledge Graphs in Ophthalmology.Ophthalmic research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo 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.
methodsFirst, 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.
resultsIn 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%.
conclusionThis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.