Galen: a third generation terminology tool to support a multipurpose national coding system for surgical procedures

Area of Science:

• Medical Informatics
• Computational Linguistics
• Ontology Engineering

Background:

• Developing standardized medical terminology is complex and labor-intensive.
• Existing coding systems often lack language independence and reusability.
• The GALEN project addresses these challenges with innovative terminology tools.

Purpose of the Study:

• To apply GALEN's language-independent concept reference model and tools to create a new surgical procedure coding system (CCAM) in France.
• To leverage artificial intelligence, ontologies, and natural language processing to support traditional medical coding processes.
• To contribute to a multilingual knowledge repository for Europe.

Main Methods:

• Utilized the GALEN concept reference model and compositional formalism.
• Employed the integrated CLAW software for processing French medical language rubrics.
• Applied a medically oriented recursive ontology and natural language processing techniques.
• Generated controlled French natural language from the concept model for label finalization.

Main Results:

• Successfully applied GALEN tools to develop the CCAM coding system for surgical procedures.
• Created a language-independent knowledge repository supporting multicultural Europe.
• Demonstrated the power of AI and ontology-based tools in streamlining medical coding.
• The classification manager effectively retrieved rubrics within a semantic network.

Conclusions:

• GALEN's approach offers a powerful, reusable, and computer-processable method for medical terminology development.
• AI-driven tools can significantly enhance the efficiency and accuracy of creating medical coding systems.
• The developed system supports multilingualism and semantic interoperability in healthcare terminology.