Towards Ontology Generation from Tables
World Wide Web
Towards semantic interoperability in healthcare: ontology mapping from SNOMED-CT to HL7 version 3
AOW '06 Proceedings of the second Australasian workshop on Advances in ontologies - Volume 72
Clinical practice guidelines: A case study of combining OWL-S, OWL, and SWRL
Knowledge-Based Systems
A model-driven approach for representing clinical archetypes for Semantic Web environments
Journal of Biomedical Informatics
Executing medical guidelines on the web: Towards next generation healthcare
Knowledge-Based Systems
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Ontology-driven hypothesis generation to explain anomalous patient responses to treatment
Knowledge-Based Systems
A knowledge-based scheduling system for Emergency Departments
Knowledge-Based Systems
Semantic service matchmaking for Digital Health Ecosystems
Knowledge-Based Systems
Utilization of ontology in health for archetypes constraint enforcement
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
A pattern-based knowledge editing system for building clinical Decision Support Systems
Knowledge-Based Systems
An approach for sub-ontology evolution in a distributed health care enterprise
Information Systems
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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.