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Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning

Xiaoyi Pan1, Boyu Chen1, Heng Weng2

  • 1School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China.

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Summary
This summary is machine-generated.

This study introduces TNorm, a novel method for extracting and normalizing temporal expressions in Chinese clinical texts. TNorm effectively processes discharge summaries, improving clinical data analysis.

Keywords:
Clinical textHeuristic ruleMachine learningPattern learningTemporal expression extractionTemporal expression normalization

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Artificial Intelligence

Background:

  • Clinical narratives contain vital temporal information for disease progression and treatment tracking.
  • Accurate extraction and normalization of temporal expressions enhance clinical research and practice.

Purpose of the Study:

  • To develop and evaluate TNorm, a new approach for automatic temporal expression extraction and normalization in Chinese clinical text.

Main Methods:

  • TNorm employs a three-stage process: extraction, classification, and normalization.
  • It utilizes heuristic rules, pattern learning, and machine learning for identifying and standardizing temporal expressions.
  • The system was tested on 1459 Chinese discharge summaries.

Main Results:

  • TNorm achieved a precision of 0.8491, recall of 0.8328, and F1 score of 0.8409 for temporal expression normalization.
  • The combined approach of extraction, prediction, and normalization proved effective.

Conclusions:

  • TNorm offers an effective automatic solution for temporal expression extraction and normalization in Chinese clinical text.
  • Evaluation on discharge summaries confirms TNorm's capability in enhancing clinical data analysis.