ArticleInterdisciplinary sciences, computational life sciences2026
Construction and Application of Traditional Chinese Medicine Knowledge Graph Based on Large Language Model.
Article in Interdisciplinary sciences, computational life sciences, 2026. 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.
- Large language models linking traditional Chinese medicine knowledge and clinical practice.Chinese medicine · 2026Review
- XuanHuGPT: parameter-efficient fine-tuning of large language model in the field of traditional Chinese medicine.Chinese medicine · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Traditional Chinese Medicine (TCM) is a vital component of the Chinese heritage, embodying a wealth of medical knowledge and distinctive therapeutic practices. A critical challenge in TCM modernization lies in extracting essential information from its complex and diverse knowledge system to develop knowledge-based services, which represents a cutting-edge research focus. This study proposes a Large Language Model (LLM)-driven approach for structuring TCM knowledge integrating historical TCM texts with open-source TCM datasets. A Fine-Tuning ChatGLM3-6B (FT-ChatGLM3) model was developed on the AliCloud DSW platform, optimized specifically for Chinese-language processing to enhance semantic understanding and knowledge extraction within TCM contexts. FT-ChatGLM3 powers an intelligent TCM Q&A system, significantly improving the accuracy and efficiency of diagnosis and therapeutic recommendations. Furthermore, a BERT-based TCM Entity Recognition (TCMER) model was developed, and a knowledge graph was constructed using FT-ChatGLM3's outputs. Experimental results demonstrate that FT-ChatGLM3 achieves strong performance in TCM applications, delivering precise diagnosis and treatment suggestions. The TCMER model also exhibits high efficacy, facilitating the systematization and structuring of TCM knowledge, while improving knowledge retrieval and consistency. The integration of FT-ChatGLM3 and TCMER not only accelerates the development of TCM knowledge graphs but also advances TCM modernization and its intelligent application in global healthcare.
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40603824What OpenQuestion holds
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 OpenQuestion graph.