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Automatic Prediction of Semantic Labels for French Medical Terms.

Thierry Hamon1,2, Natalia Grabar3

  • 1Université Paris-Saclay, CNRS, LISN, F-91400, Orsay, France.

Studies in Health Technology and Informatics
|May 25, 2022
PubMed
Summary

This study develops methods for semantic labeling of French medical terms using the Unified Medical Language System (UMLS). Both experiments achieved high accuracy, demonstrating effective term detection and labeling in medical texts.

Keywords:
FrenchMachine LearningNLPSemantic labelingterminology

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Accurate semantic labeling of medical terms is crucial for information retrieval and knowledge extraction from clinical texts.
  • Existing methods may struggle with the nuances of French medical terminology and context.

Purpose of the Study:

  • To develop and evaluate methods for semantic labeling of terms within French medical corpora.
  • To compare two approaches: predicting labels for identified terms versus detecting and labeling terms in raw text.

Main Methods:

  • Utilized two French medical corpora and a subset of the Unified Medical Language System (UMLS).
  • Experiment 1: Predicted semantic labels for pre-identified terms based on word/term structure and context.
  • Experiment 2: Detected terms within raw text and predicted their semantic labels.

Main Results:

  • Achieved an F-measure exceeding 0.90 in both experimental setups.
  • Demonstrated the effectiveness of both term-based and context-based approaches for semantic labeling.

Conclusions:

  • The proposed methods are highly effective for semantic labeling of French medical terms.
  • Both approaches show strong performance, indicating robustness in term identification and classification within medical corpora.