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23
February, 2012
Thursday

Archetype Sub-Ontology: Improving Constraint-based Clinical Knowledge Model in Electronic Health Records

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.

Abstract
Sari, Anny Kartika; Rahayu, Wenny; Bhatt, Mehul, Knowledge-Based Systems, Online first, DOI: 16/j.knosys.2011.07.004

More bibliographic information.

21 August 2011 | Categories: Science | EHR: EHR | Tag(s): Archetypes, Clinical Information Systems, Ontology, Semantic, SNOMED
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