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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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A joint entity Relation Extraction method for document level Traditional Chinese Medicine texts.

Wenxuan Xu1, Lin Wang1, Mingchuan Zhang1

  • 1School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.

Artificial Intelligence in Medicine
|June 27, 2024
PubMed
Summary

This study introduces a novel framework for Traditional Chinese Medicine (TCM) entity relation mining using graph convolutional networks. The method significantly improves the extraction of diagnostic and treatment information from TCM texts.

Keywords:
Document levelJoint extractionNamed entity recognitionRelation extractionTraditional Chinese medicine

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Area of Science:

  • Computational linguistics
  • Bioinformatics
  • Traditional Chinese Medicine (TCM) research

Background:

  • Traditional Chinese Medicine (TCM) possesses a complex theoretical framework valuable for disease diagnosis and treatment.
  • Existing TCM textual data is disorganized, lacking standardization and hindering knowledge extraction.
  • Accurate entity and relation extraction is crucial for leveraging TCM knowledge.

Purpose of the Study:

  • To propose a joint extraction framework for mining entities and relations from document-level TCM texts.
  • To develop a task-specific model by fine-tuning pre-trained language models with TCM domain knowledge.
  • To enhance the extraction of diagnostic and treatment information from unstructured TCM case studies.

Main Methods:

  • A joint extraction framework utilizing graph convolutional networks (GCNs) for entity relation mining.
  • Fine-tuning pre-trained language models with TCM domain knowledge to create a specialized model.
  • Employing multi-relational GCNs, word fusion coding, and TCM lexicon information for comprehensive entity and relation extraction.

Main Results:

  • The proposed method achieved an F1-score of 90.7% for Named Entity Recognition (NER) on the TCM dataset.
  • The system obtained an F1-score of 76.14% for Relation Extraction (RE) in TCM texts.
  • The approach significantly outperforms existing state-of-the-art methods in TCM entity relation mining.

Conclusions:

  • The developed framework effectively extracts entities and relations from document-level TCM texts.
  • This advancement enhances the ability to mine valuable knowledge for TCM diagnosis and treatment.
  • The method provides a standardized approach to processing and understanding TCM textual data.