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Semantic Search for Large Scale Clinical Ontologies.

Duy-Hoa Ngo1, Madonna Kemp1, Donna Truran1

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Summary
This summary is machine-generated.

This study introduces a deep learning search system for clinical ontologies, improving concept discovery across different terminologies. The novel Triplet-BERT model enhances semantic search accuracy for clinical concept normalization and ontology matching.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Clinical ontologies are essential for organizing medical knowledge but pose challenges for concept retrieval due to vocabulary variations.
  • Existing search methods struggle with semantic differences and synonyms, hindering applications like concept normalization and ontology matching.

Purpose of the Study:

  • To develop a deep learning-based semantic search system for large clinical ontologies.
  • To address the challenge of finding concepts when queries use different vocabularies and synonyms.

Main Methods:

  • Proposed a novel Triplet-BERT model for semantic search.
  • Developed a method for generating training data directly from clinical ontologies.
  • Evaluated the model on five real-world benchmark datasets.

Main Results:

  • The Triplet-BERT model achieved high performance in both free text to concept and concept to concept searching tasks.
  • The proposed approach significantly outperformed all baseline methods in semantic search accuracy.
  • Demonstrated effectiveness in concept normalization and ontology matching applications.

Conclusions:

  • The deep learning-based semantic search system effectively overcomes vocabulary challenges in clinical ontologies.
  • The Triplet-BERT model offers a robust solution for accurate clinical concept retrieval and matching.
  • This approach advances the usability of large clinical knowledge bases.