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CMCN: Chinese medical concept normalization using continual learning and knowledge-enhanced.

Pu Han1, Xiong Li2, Zhanpeng Zhang2

  • 1School of Management, Nanjing University of Posts & Telecommunications, Nanjing 210003, China; Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China.

Artificial Intelligence in Medicine
|September 6, 2024
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Summary
This summary is machine-generated.

Chinese medical concept normalization (CMCN) is improved using a novel deep learning model. This approach enhances information extraction for better biomedical research and AI integration in healthcare.

Keywords:
Continual learningDeep learningMedical concept normalization

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Artificial Intelligence in Medicine

Background:

  • Medical Concept Normalization (MCN) is vital for biomedical research, but Chinese MCN (CMCN) lags due to linguistic complexity and resource scarcity.
  • Deep learning excels in complex NLP tasks, showing promise for specialized biomedical knowledge discovery.

Purpose of the Study:

  • To advance Chinese medical concept normalization (CMCN) using deep learning.
  • To develop and evaluate a novel model for improved CMCN, focusing on disease names.

Main Methods:

  • Developed a deep learning model incorporating polymorphic semantic information.
  • Employed multi-task learning for knowledge enhancement and continual learning to retain key medical features.
  • Focused on disease names as the core component of CMCN.

Main Results:

  • The best-performing model, GCBM-BSCL, achieved 76.12% Accuracy@1, 87.20% Accuracy@5, and 90.02% Accuracy@10 on a self-built Chinese disease dataset.
  • Demonstrated the effectiveness of the proposed deep learning approach for CMCN.

Conclusions:

  • The study significantly advances CMCN, knowledge mining, and AI applications in the medical field.
  • The developed model offers a robust solution for processing Chinese medical text.