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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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XML, Ontologies, and Their Clinical Applications.

Chunjiang Yu1, Bairong Shen2

  • 1Suzhou Industrial Park Institute of Services Outsourcing, No. 99 Ruoshui Road, Suzhou Industrial Park, Suzhou, 215123, Jiangsu, China.

Advances in Experimental Medicine and Biology
|November 4, 2016
PubMed
Summary
This summary is machine-generated.

Extensible Markup Language (XML) and ontologies enhance clinical data management by enabling flexible data sharing and integration. These technologies address challenges in saving, querying, and exploiting the growing volume of clinical research data.

Keywords:
Data exchangeData integrationKnowledge representationOntologyXML

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Area of Science:

  • Clinical Research Informatics
  • Biomedical Data Management

Background:

  • Information technology extensively impacts clinical research, generating vast amounts of data.
  • Challenges exist in managing, sharing, and utilizing this increasing clinical data volume.

Purpose of the Study:

  • To discuss the fundamental concepts of Extensible Markup Language (XML) and ontologies.
  • To explore the clinical applications of XML and ontologies in data management.

Main Methods:

  • Review of Extensible Markup Language (XML) principles for data formatting.
  • Introduction to ontologies as knowledge representation schemes.
  • Examination of ontology repositories like Gene Ontology and BioPortal.

Main Results:

  • XML facilitates flexible, structured electronic data sharing and is crucial for data standardization, exchange, and integration.
  • Ontologies provide standardized knowledge representation, aiding clinical research through repositories.

Conclusions:

  • XML and ontologies are vital tools for overcoming clinical data management challenges.
  • Their application improves data standardization, exchange, integration, and overall clinical research efficiency.