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Building a relational database for a physician document index.

B K Martin1, R Rada

  • 1School of Medicine, University of Hawaii, Honolulu 96816.

Medical Informatics = Medecine Et Informatique
|July 1, 1987
PubMed
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This study presents a method for merging medical knowledge bases like Medical Subject Headings (MeSH) and SNOMED, enabling better document retrieval and concept organization. The semi-automatic approach leverages a relational model and expert interaction for efficient integration.

Area of Science:

  • Medical Informatics
  • Knowledge Representation
  • Database Management

Background:

  • Existing medical knowledge bases (MeSH, SNOMED, CMIT) are valuable for organizing medical concepts and indexing literature.
  • Integrating these diverse knowledge bases presents challenges in concept mapping and data storage.
  • The need for unified access to medical information necessitates effective methods for knowledge base interoperability.

Purpose of the Study:

  • To demonstrate the mapping of MeSH, SNOMED, and CMIT into a relational data model.
  • To develop a semi-automatic method for merging these medical knowledge bases.
  • To facilitate enhanced document retrieval and concept organization through integrated medical knowledge.

Main Methods:

  • Mapping three medical knowledge bases (MeSH, SNOMED, CMIT) into a relational data model.

Related Experiment Videos

  • Storing the data model on Apollo workstation and Intelligent Database Machine.
  • Developing a semi-automatic merging method utilizing the relational model and synonyms.
  • Incorporating expert interaction to validate system-recommended concept merges.

    • Main Results:

    • Successful mapping of MeSH, SNOMED, and CMIT into a unified relational data model.
    • Implementation of a semi-automatic merging process that reduces manual effort.
    • Demonstration of the system's capability to recommend concept merges with expert validation.
    • Validation of the method's applicability to large-scale knowledge bases.

    Conclusions:

    • The developed method enables effective integration of disparate medical knowledge bases.
    • Semi-automatic merging enhances efficiency and accuracy in knowledge base consolidation.
    • The approach supports improved organization of medical concepts and document retrieval.
    • This methodology is scalable and suitable for integrating extensive medical knowledge resources.