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A hierarchical method to automatically encode Chinese diagnoses through semantic similarity estimation.

Wenxin Ning1, Ming Yu2, Runtong Zhang3

  • 1Health Care Services Research Center, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, PR China. nwx14@mails.tsinghua.edu.cn.

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|March 5, 2016
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Summary

This study introduces an automated method for assigning International Classification of Diseases, 10th Revision (ICD-10) codes to Chinese medical diagnoses, improving efficiency in hospitals.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing

Background:

  • Rapid increase in Chinese medical documents necessitates efficient coding.
  • Current manual coding processes in Chinese hospitals are time-consuming.

Purpose of the Study:

  • To develop an automated system for assigning ICD-10 codes to Chinese diagnoses from electronic medical records.
  • To enhance the efficiency and accuracy of medical coding in Chinese healthcare settings.

Main Methods:

  • Proposed two encoding methods: flat and hierarchical, utilizing a Chinese standard diagnostic library.
  • Applied semantic similarity estimation (knowledge-based and distributional) for Chinese biomedical text.
  • Incorporated Chinese language characteristics into distributional semantics.

Main Results:

  • The hierarchical method demonstrated superior accuracy and time efficiency over the flat method.
  • Automated encoding achieved high performance: 92.57% precision, 89.63% recall, and 91.08% F-score.
  • Semantic similarity estimation was crucial for effective encoding, with significant performance drops without it.

Conclusions:

  • The hierarchical structure of ICD-10 codes improves automated code assignment performance.
  • Semantic similarity estimation is essential for processing Chinese medical text.
  • The developed method significantly reduces workload and boosts efficiency in Chinese hospital coding.