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Merging Biomedical Ontologies with BioSTransformers.

Safaa Menad1, Saïd Abdeddaïm1, Lina F Soualmia1

  • 1Univ Rouen Normandie, Normandie Univ, LITIS UR 4108 F-76000 Rouen, France.

Studies in Health Technology and Informatics
|August 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Siamese Models for Health Ontologies Merging (SiMHOMer) to integrate biomedical knowledge. The framework uses AI to discover new relationships, improving healthcare data organization and retrieval.

Keywords:
Biomedical OntologiesOntology MergingSiamese Neural ModelsUMLS

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Ontologies are vital for structuring biomedical knowledge.
  • Existing ontologies lack comprehensive concepts and relationships.
  • Efficient data structuring, coding, and retrieval are critical in healthcare.

Purpose of the Study:

  • To propose SiMHOMer (Siamese Models for Health Ontologies Merging) for semantic merging of healthcare ontologies.
  • To focus initially on integrating ontologies related to diseases, symptoms, drugs, and adverse events.
  • To develop a novel merging ontology for enhanced applications.

Main Methods:

  • Utilized Siamese neural models (BioSTransformers) trained on biomedical data.
  • Employed BioSTransformers to identify novel concept relationships and create new semantic links.
  • Validated the approach and identified relations using the UMLS Metathesaurus and Semantic Network.

Main Results:

  • Demonstrated the framework's capability to identify new relevant relations between biomedical concepts.
  • Successfully generated new semantic relations for ontology merging.
  • Initial validation using UMLS Metathesaurus and Semantic Network shows promising results.

Conclusions:

  • The SiMHOMer framework offers a promising approach for semantically merging healthcare ontologies.
  • The identified new relations contribute to a more comprehensive and integrated biomedical knowledge base.
  • This research paves the way for improved applications in healthcare data management and analysis.