Scientific article
Open access

Performance of Machine Learning Methods to Classify French Medical Publications

Published inStudies in health technology and informatics, vol. 294, no. Challenges of Trustable AI and Added-Value on Health, p. 874-875
Publication date2022-05-25

Many medical narratives are read by care professionals in their preferred language. These documents can be produced by organizations, authorities or national publishers. However, they are often hardly findable using the usual query engines based on English such as PubMed. This work explores the possibility to automatically categorize medical documents in French following an automatic Natural Language Processing pipeline. The pipeline is used to compare the performance of 6 different machine learning and deep neural network approaches on a large dataset of peer-reviewed weekly published Swiss medical journal in French covering major topics in medicine over the last 15 years. An accuracy of 96% was achieved for 5-topic classification and 81% for 20-topic classification.

  • Document classification
  • French
  • Revue Médicale Suisse
  • Deep learning
  • Machine learning
  • Natural language processing
  • Unstructured medical data
  • PubMed
  • Neural Networks, Computer
  • Language
Citation (ISO format)
ZAGHIR, Jamil et al. Performance of Machine Learning Methods to Classify French Medical Publications. In: Studies in health technology and informatics, 2022, vol. 294, p. 874–875. doi: 10.3233/SHTI220613
Main files (1)
Article (Published version)
ISSN of the journal0926-9630

Technical informations

Creation06/07/2022 2:26:00 PM
First validation06/07/2022 2:26:00 PM
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