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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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A Pattern-Based Method for Medical Entity Recognition From Chinese Diagnostic Imaging Text.

Zihong Liang1, Junjie Chen2, Zhaopeng Xu1

  • 1School of Computer Science, South China Normal University, Guangzhou, China.

Frontiers in Artificial Intelligence
|March 18, 2021
PubMed
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This study introduces a pattern-based method for extracting tumor information from Chinese medical texts. The approach effectively identifies tumor sites and sizes, demonstrating robust performance in medical information extraction.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Electronic medical records (EMRs) present complex challenges for knowledge extraction.
  • Accurate identification of medical entities and relations within EMRs remains an open research problem.
  • High complexity hinders the extraction of valuable insights from unstructured medical data.

Purpose of the Study:

  • To develop and evaluate a pattern-based method for extracting tumor-related entities and attributes.
  • To address the challenge of extracting specific tumor information from Chinese diagnostic imaging reports.
  • To improve the accuracy and efficiency of medical information extraction from unstructured text.

Main Methods:

  • A three-step pattern-based approach was employed.
Keywords:
clinical textinformation extractionmedical named entity recognitionnatural language processingpattern-based strategy

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  • Keyword matching identified primary tumor sites.
  • Regular expressions extracted primary tumor size information.
  • Defined rules acquired metastatic tumor sites.

    • Main Results:

    • The method achieved an overall F1 score of 0.755.
    • Specific F1 scores for extraction tasks were 0.784 (primary sites), 0.822 (size), and 0.740 (metastatic sites).
    • The system demonstrated high precision (0.825) and recall (0.697).

    Conclusions:

    • The proposed method is stable and robust across varying data volumes.
    • It achieved competitive performance in the CHIP 2018 open challenge.
    • The approach is effective for extracting tumor entities from Chinese diagnostic imaging texts.