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

Knowledge representation and management enabling intelligent interoperability - principles and standards.

Bernd Blobel1

  • 1eHealth Competence Center, University Hospital Regensburg, Regensburg, Germany. bernd.blobel@klinik.uni-regensburg.de

Studies in Health Technology and Informatics
|April 2, 2013
PubMed
Summary
This summary is machine-generated.

This study explores knowledge representation for medical decision support systems (DSSs), focusing on ontologies and standards to improve healthcare interoperability. It details a system-theoretical approach for managing medical knowledge effectively.

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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:

  • Medical Informatics
  • Computer Science

Background:

  • Healthcare is undergoing paradigm shifts driven by technology.
  • There is a critical need for automated interoperability in health services.
  • Effective knowledge management is essential for advanced medical decision support.

Purpose of the Study:

  • To address the challenge of knowledge representation and management in medical decision support systems (DSSs).
  • To propose a system-theoretical, architecture-centric approach for DSSs.
  • To review existing and emerging knowledge representation and management standards.

Main Methods:

  • Introduced definitions related to knowledge representation and decision support.
  • Presented a system-theoretical, architecture-centric approach for DSSs.
  • Utilized systems of ontologies for knowledge representation.

Main Results:

  • A framework for knowledge representation in DSSs was outlined.
  • The application of ontologies for structuring medical knowledge was demonstrated.
  • An overview of current and future knowledge management standards was provided.

Conclusions:

  • Knowledge representation and management are crucial for enhancing medical decision support.
  • An ontology-based, system-theoretical approach offers a robust method for DSS knowledge management.
  • Understanding standards is key to achieving seamless healthcare interoperability.