Translating ICD-11 into French using lexical-based approach: a preliminary study.

Area of Science:

• Medical Informatics
• Computational Linguistics
• Health Terminology

Background:

• Accurate translation of health terminologies is crucial for international healthcare.
• The 11th edition of the International Classification of Diseases (ICD-11) requires a French version for widespread adoption.
• Existing translation methods may lack the specificity required for complex biomedical terms.

Purpose of the Study:

• To develop and evaluate a lexical approach for translating ICD-11 terms into French.
• To assess the efficacy of Natural Language Processing (NLP) techniques in biomedical terminology translation.
• To utilize a comprehensive portal of biomedical terminologies and ontologies for enhanced translation accuracy.

Main Methods:

• A lexical approach employing Natural Language Processing (NLP) techniques was proposed.
• The method utilized 56 biomedical terminologies and ontologies from the Cross-lingual Health Multiple Terminologies and Ontologies Portal.
• A sample of 336 ICD-11 terms was used to test the translation algorithm.

Main Results:

• The NLP-based algorithm translated 164 out of 336 (49%) ICD-11 terms into at least one French equivalent.
• The lexical approach demonstrated feasibility in translating a significant portion of the targeted health terms.
• The reliance on curated biomedical terminologies facilitated the translation process.

Conclusions:

• The proposed lexical NLP approach is a viable method for translating ICD-11 into French.
• Leveraging biomedical terminologies and ontologies enhances the accuracy and scope of health term translation.
• Further development can improve the translation rate and expand the applicability of this method.