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Ontological Representation of Laboratory Test Observables: Challenges and Perspectives in the SNOMED CT Observable Entity Model Adoption
BioMerieux, France; Univ Rouen, France.
Univ Rouen, France; LIMICS, France.
BioMerieux, France.
Univ Rouen, France; LIMICS, France.
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2017 (English)In: ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2017, SPRINGER INTERNATIONAL PUBLISHING AG , 2017, Vol. 10259, p. 14-23Conference paper, Published paper (Refereed)
Abstract [en]

The emergence of electronic health records has highlighted the need for semantic standards for representation of observations in laboratory medicine. Two such standards are LOINC, with a focus on detailed encoding of lab tests, and SNOMED CT, which is more general, including the representation of qualitative and ordinal test results. In this paper we will discuss how lab observation entries can be represented using SNOMED CT. We use resources provided by the Regenstrief Institute and SNOMED International collaboration, which formalize LOINC terms as SNOMED CT post-coordinated expressions. We demonstrate the benefits brought by SNOMED CT to classify lab tests. We then propose a SNOMED CT based model for lab observation entries aligned with the BioTopLite2 (BTL2) upper level ontology. We provide examples showing how a model designed with no ontological foundation can produce misleading interpretations of inferred observation results. Our solution based on a BTL2 conformant formal interpretation of SNOMED CT concepts allows representing lab test without creating unintended models. We argue in favour of an ontologically explicit bridge between compositional clinical terminologies, in order to safely use their formal representations in intelligent systems.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG , 2017. Vol. 10259, p. 14-23
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743
Keywords [en]
Biomedical ontologies and terminologies; LOINC; SNOMED CT; BioTopLite2
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-149407DOI: 10.1007/978-3-319-59758-4_2ISI: 000434473000002ISBN: 978-3-319-59758-4 (electronic)ISBN: 978-3-319-59757-7 (print)OAI: oai:DiVA.org:liu-149407DiVA, id: diva2:1229195
Conference
16th Conference on Artificial Intelligence in Medicine (AIME)
Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-06-29

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Karlsson, Daniel
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Division of Biomedical EngineeringFaculty of Science & Engineering
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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