Volume 39 Issue 10
Oct.  2023
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Article Navigation >  Journal of Nanjing University of traditional Chinese Medicine  > 2023  >  39(10): 972-978
WANG Xin-yu, YANG Tao, HU Kong-fa. An Automated Completion Study of Knowledge for the Treatment of Lung Cancer by Famous TCM Experts Based on Knowledge Representation Learning[J]. Journal of Nanjing University of traditional Chinese Medicine, 2023, 39(10): 972-978. doi: 10.14148/j.issn.1672-0482.2023.0972
Citation: WANG Xin-yu, YANG Tao, HU Kong-fa. An Automated Completion Study of Knowledge for the Treatment of Lung Cancer by Famous TCM Experts Based on Knowledge Representation Learning[J]. Journal of Nanjing University of traditional Chinese Medicine, 2023, 39(10): 972-978. doi: 10.14148/j.issn.1672-0482.2023.0972
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An Automated Completion Study of Knowledge for the Treatment of Lung Cancer by Famous TCM Experts Based on Knowledge Representation Learning

doi: 10.14148/j.issn.1672-0482.2023.0972
  • 1.

    School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China

  • 2.

    Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, Nanjing 210023, China

  • 3.

    Jiangsu Research Center for Major Health Risk Management and TCM Control Policy, Nanjing 210023, China

  • Received Date: 2023-04-23
    Available Online: 2023-11-10
    Abstract
  •   OBJECTIVE  To build the knowledge base of medical cases for the treatment of lung cancer by famous TCM experts, and to realize the automatic completion of the knowledge base.  METHODS  A method of completing the drug use of famous old Chinese medicine for the treatment of lung cancer was designed, the correlation between symptoms, diagnosis, traditional Chinese medicine, tongue image and pulse image was analyzed and divided according to the threshold, and the CrossE knowledge representation model was used in conjunction with negative sampling techniques to learn the embedded representation of each entity and relationship in the knowledge base in the vector space, and the link prediction task was carried out to mine the hidden relationships in the knowledge base.  RESULTS  Based on the Pearson correlation analysis conducted on entities within medical cases, the relationships predicted using the CrossE model achieved the following results for the Hit@1, Hit@3, Hit@5, and Hit@10 metrics: 16.19%, 29.12%, 35. 85%, and 47.60%, respectively. Additionally, the MeanRank metric reached 13.19. Compared to models such as TransE and TransR, there is a significant improvement.  CONCLUSION  The use of knowledge completion technology in conjunction with clinical practices in TCM can deeply explore the hidden knowledge of famous TCM experts in the treatment of lung cancer.

     

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