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Hypergraph Based Data Model for Complex Health Data Exploration and Its Implementation in PREDIMED Clinical Data

Christophe Cancé1, Christian Lenne2, Svetlana Artemova3,2,4

  • 1TIMC, Univ. Grenoble Alpes, CNRS, VetAgro'Sup, Grenoble INP, CHU Grenoble Alpes, F-38000, Grenoble, France.

Studies in Health Technology and Informatics
|June 8, 2022
PubMed
Summary
This summary is machine-generated.

Physicians can now explore complex patient data using a novel hypergraph data model. This system enhances data analysis and visualization within the PREDIMED Clinical Data Warehouse (CDW).

Keywords:
Clinical Data Warehousedata lakeflexibilityhypergraphsmassive and complex data interactive explorationproperty graphs

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Area of Science:

  • Medical Informatics
  • Data Science
  • Clinical Data Management

Background:

  • Healthcare data is complex and massive, posing challenges for physician analysis.
  • Existing data models may lack the flexibility to adapt to evolving medical practices and standards.

Purpose of the Study:

  • To develop a hypergraph-based operational data model for enhanced physician exploration and analysis of patient data.
  • To create an interactive system for qualitative analysis of complex clinical information.

Main Methods:

  • Developed a hypergraph operational data model within the PREDIMED Clinical Data Warehouse (CDW).
  • Implemented the model using a property graph database linked to an interactive graphical interface.
  • Integrated real-time search, visualization, and analysis tools.

Main Results:

  • The hypergraph model provides a dual graphical and formal structure for representing medical concepts and semantic relations.
  • The implementation allows physicians to easily navigate, explore, and analyze patient data interactively.
  • The system supports agile structural changes, adapting to evolving healthcare techniques and practices.

Conclusions:

  • The developed hypergraph data model empowers physicians with advanced tools for complex patient data analysis.
  • The flexible and adaptable implementation ensures compatibility with evolving interoperability standards in healthcare.