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Updated: Jun 14, 2025

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Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT.

TianCheng Xu1,2, Jing Wen1,2, Lei Wang3

  • 1Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.

Database : the Journal of Biological Databases and Curation
|August 30, 2024
PubMed
Summary

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This summary is machine-generated.

The ACUBERT model enhances meridian classification in acupuncture indications, outperforming other models. This deep learning approach improves accuracy for standardized traditional Chinese medicine treatments.

Area of Science:

  • Traditional Chinese Medicine
  • Natural Language Processing
  • Machine Learning

Background:

  • Non-quantitative descriptions in acupuncture limit standardized treatments.
  • Accurate meridian classification is crucial for diagnosis and treatment.

Purpose of the Study:

  • To evaluate the effectiveness of the Acupuncture Bidirectional Encoder Representations from Transformers (ACUBERT) model for meridian entity recognition and classification.
  • To address discrepancies in meridian classification within acupuncture indications.
  • To develop a standardized approach for acupuncture treatment using deep learning.

Main Methods:

  • Developed the ACUBERT model based on BERT architecture.
  • Utilized a pretraining corpus of 54,593 entities from 82 acupuncture medical books.

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  • Trained the meridian differentiation model using the eight principles and zang-fu differentiation.
  • Compared ACUBERT with Support Vector Machine and Random Forest models.

    • Main Results:

    • ACUBERT demonstrated superior classification effectiveness compared to baseline models.
    • The model achieved optimal performance at Epoch 5, with precision, recall, and F1 scores exceeding 0.8.
    • Established an acupuncture-indication knowledge base (ACU-IKD) and the ACUBERT model.

    Conclusions:

    • The ACUBERT model significantly improves the classification accuracy of meridian attribution in acupuncture indications.
    • Deep learning methods based on BERT offer advantages for multi-category, large-scale text classification in traditional Chinese medicine.
    • This study contributes to the standardization of acupuncture diagnosis and treatment methods.