Acupuncture and tuina knowledge graph with prompt learning Article Swipe
Xiaoran Li
,
Xiaosong Han
,
Siqing Wei
,
Yanchun Liang
,
Renchu Guan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2024.1346958
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2024.1346958
Introduction Acupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations. Methods (1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base. (2)A template-free Chinese text NER joint training method (TemplateFC) was proposed, which enhances the EntLM model with BiLSTM and CRF layers. Appropriate rules were set for ERE. (3) A comprehensive knowledge graph comprising 10,346 entities and 40,919 relationships was constructed based on modern literature. Results A robust TCM KG with a wide range of entities and relationships was created. The template-free joint training approach significantly improved NER accuracy, especially in Chinese text, addressing issues related to entity identification and tokenization differences. The KG provided valuable insights into acupuncture and tuina, facilitating efficient information retrieval and personalized treatment recommendations. Discussion The integration of KGs in TCM research is essential for advancing diagnostics and interventions. Challenges in NER and ERE were effectively tackled using hybrid approaches and innovative techniques. The comprehensive TCM KG our built contributes to bridging the gap in TCM knowledge and serves as a valuable resource for specialists and non-specialists alike.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fdata.2024.1346958
- https://www.frontiersin.org/articles/10.3389/fdata.2024.1346958/pdf?isPublishedV2=False
- OA Status
- gold
- Cited By
- 4
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394575962
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394575962Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fdata.2024.1346958Digital Object Identifier
- Title
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Acupuncture and tuina knowledge graph with prompt learningWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-08Full publication date if available
- Authors
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Xiaoran Li, Xiaosong Han, Siqing Wei, Yanchun Liang, Renchu GuanList of authors in order
- Landing page
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https://doi.org/10.3389/fdata.2024.1346958Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fdata.2024.1346958/pdf?isPublishedV2=FalseDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fdata.2024.1346958/pdf?isPublishedV2=FalseDirect OA link when available
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Acupuncture, Computer science, Artificial intelligence, Medicine, Psychology, Alternative medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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