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Clinical Comparable Corpus Describing the Same Subjects with Different Expressions.

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  • 1Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Bunkyo, Tokyo, Japan.

Studies in Health Technology and Informatics
|June 8, 2022
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Summary

This study introduces MedTxt-RR-JA, a Japanese clinical corpus for medical AI. It addresses linguistic variations in radiology reports, improving AI understanding of clinical documents.

Keywords:
Artificial IntelligenceNatural Language ProcessingRadiology Report

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Area of Science:

  • Medical Artificial Intelligence
  • Natural Language Processing
  • Clinical Informatics

Background:

  • Medical AI requires understanding linguistic variations in clinical documents.
  • Existing resources primarily address word or sentence-level variations.
  • A need exists for broader-scale handling of linguistic diversity in medical texts.

Purpose of the Study:

  • To introduce MedTxt-RR-JA, the first clinical comparable corpus for Japanese radiology reports.
  • To facilitate the development of medical AI systems capable of understanding diverse clinical language.
  • To provide a publicly available dataset for Japanese medical AI research.

Main Methods:

  • Recruited nine radiologists to diagnose 15 lung cancer cases from Radiopaedia.
  • Developed the Medical Text Radiology Report section Japanese version (MedTxt-RR-JA) corpus.
  • Analyzed reports for word-, sentence-, and document-level variations while maintaining content similarity.

Main Results:

  • MedTxt-RR-JA contains 135 radiology reports with significant linguistic variations.
  • The corpus demonstrates variations at word, sentence, and document levels.
  • It is the first publicly available Japanese radiology report corpus.

Conclusions:

  • MedTxt-RR-JA addresses the scarcity of Japanese medical data for AI development.
  • The corpus aids AI systems in recognizing synonyms and paraphrases in clinical documents.
  • The methodology is adaptable for creating privacy-preserving clinical corpora.