oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Archetype sub-ontology: Improving constraint-based clinical knowledge model in electronic health records
Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia; Department of Computer Science and Electronics, Gadjah Mada University, Yogyakarta, Indonesia.
Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia.
SFB/TR 8 Spatial Cognition, Informatics, University of Bremen, Bremen, Germany. (AASS)ORCID iD: 0000-0002-6290-5492
2012 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 26, p. 75-85Article in journal (Refereed) Published
Abstract [en]

The global effort in the standardization of electronic health records has driven the need for a model to allow medical practitioners to interact with the newly standardized medical information system by focusing on the actual medical concepts/processes rather than the underlying data representations. An archetype has been introduced as a model that represents functional health concepts or processes such as admission record, which enables capturing all information relevant to the processes transparently to the users. However, it is necessary to ensure that the archetypes capture accurately all information relevant to the archetype concepts. Therefore, a semantic backbone is required for each of the archetype.

In this paper, we propose the development of an archetype sub-ontology for each archetype to represent the semantic content of the corresponding archetype. The sub-ontology is semi-automatically extracted from a standard health ontology, in this case SNOMED CT. Two steps performed to build an archetype sub-ontology are the annotation process and the extraction process, in which some rules have to be applied to maintain the validity of sub-ontology. The approach is evaluated by utilizing the archetype sub-ontologies produced in the development of a new archetype to ensure that only relevant archetypes can be linked to the archetype being developed, so that the only relevant data are captured using the particular archetype. It is shown that the method produces better results than the current approach in which an archetype sub-ontology is not used. We conclude that the archetype sub-ontology can represent well the semantic content of archetype.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 26, p. 75-85
Keywords [en]
Semantic relevance; Sub-ontology derivation; Archetype; Electronic health record; Medical information systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-64253DOI: 10.1016/j.knosys.2011.07.004ISI: 000299979400009Scopus ID: 2-s2.0-84155180991OAI: oai:DiVA.org:oru-64253DiVA, id: diva2:1174469
Note

Funding Agencies:

Ministry of National Education of the Republic of Indonesia  

Alexandar von Humboldt Foundation (Germany)  

Germany Research Foundation (DFG) 

Available from: 2018-01-15 Created: 2018-01-15 Last updated: 2018-09-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopushttps://doi.org/10.1016/j.knosys.2011.07.004

Authority records BETA

Bhatt, Mehul

Search in DiVA

By author/editor
Bhatt, Mehul
In the same journal
Knowledge-Based Systems
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 42 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf