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Updated: May 12, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.

Jean-François Ethier1, Olivier Dameron, Vasa Curcin

  • 1INSERM UMR936, Université de Rennes 1, Rennes, France.

Journal of the American Medical Informatics Association : JAMIA
|April 11, 2013
PubMed
Summary
This summary is machine-generated.

A unified ontology framework integrates structural and terminological data for improved biomedical data interoperability. This approach, using the General Information Model (GIM) and LexEVS, simplifies data integration and retrieval.

Keywords:
InteroperabilityLexEVSOntologySemanticsTerminologyTranslational Medical Research

Related Experiment Videos

Last Updated: May 12, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Area of Science:

  • Biomedical Informatics
  • Data Science
  • Knowledge Representation

Background:

  • Biomedical research requires integrating diverse data sources.
  • Current interoperability efforts often isolate structural and terminological aspects.
  • A unified approach is needed to address these interdependent challenges.

Purpose of the Study:

  • To propose a unified ontology-based knowledge framework for heterogeneous data source interoperability.
  • To investigate the feasibility of using the LexEVS terminology server for implementation.
  • To address the limitations of isolated structural and terminological integration efforts.

Main Methods:

  • Developed a General Information Model (GIM) ontology to unify structural models and terminologies.
  • Integrated mapping sets within the GIM for comprehensive data representation.
  • Utilized the LexEVS terminology server for uniform access to unified resources.

Main Results:

  • The GIM framework was successfully applied in the TRANSFoRm project for data integration in a diabetes cohort study.
  • The GIM was instantiated as the clinical data integration model.
  • Mappings facilitated effective information retrieval for project software tools.

Conclusions:

  • A novel, unified approach addresses interoperability by integrating structural and semantic models.
  • The GIM abstracts over structure and coding, enabling uniform access via LexEVS (HL7 CTS2).
  • This flexible system reduces the effort for data integration and management.