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A knowledge graph-based data harmonization framework for secondary data reuse.

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Summary
This summary is machine-generated.

This study introduces a semantic-driven framework for harmonizing heterogeneous healthcare data, enabling cross-institutional data sharing and analysis for machine learning applications.

Keywords:
Knowledge graphsOntologiesSNOMED CTSemantic interoperabilitySemantic query

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Area of Science:

  • Health Informatics
  • Data Science
  • Ontology Engineering

Background:

  • Clinical care systems generate vast amounts of heterogeneous data.
  • Data harmonization is crucial for integrating and analyzing this valuable information.

Purpose of the Study:

  • To present a semantic-driven harmonization framework for healthcare data.
  • To enable meaningful sharing and integration of data across institutions.
  • To facilitate advanced data analysis and exploitation.

Main Methods:

  • Developed an ontology-based common data model (SCDM).
  • Implemented a data transformation pipeline and a semantic query system.
  • Utilized an ontology-based infrastructure and graph database for integration.

Main Results:

  • Successfully integrated heterogeneous datasets from multiple European institutions within the Precise4Q project.
  • Enabled data scientists to explore integrated data via a semantic query system.
  • Facilitated the use of harmonized data for building machine learning models.

Conclusions:

  • The Semantic Common Data Model (SCDM) and RDF enable semantic integration of heterogeneous healthcare data.
  • The framework supports advanced data exploitation for research and clinical applications.
  • Successful application demonstrated within the European H2020 Precise4Q project.