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Development of a Methodological Approach for Data Quality Ontology in Diabetes Management
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Author(s): Alireza Rahimi (University of New South Wales, Australia & Isfahan University of Medical Sciences, Iran & SWSLHD General Practice Unit, Australia), Nandan Parameswaran (The University of New South Wales, Australia), Pradeep Kumar Ray (University of New South Wales, Australia), Jane Taggart (University of New South Wales Australia, & SWSLHD General Practice Unit, Australia), Hairong Yu (University of New South Wales, Australia)and Siaw-Teng Liaw (University of New South Wales, Australia & SWSLHD General Practice Unit, Australia)
Copyright: 2016
Pages: 22
Source title:
E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-8756-1.ch023
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Abstract
The role of ontologies in chronic disease management and associated challenges such as defining data quality (DQ) and its specification is a current topic of interest. In domains such as Diabetes Management, a robust Data Quality Ontology (DQO) is required to support the automation of data extraction semantically from Electronic Health Record (EHR) and access and manage DQ, so that the data set is fit for purpose. A five steps strategy is proposed in this paper to create the DQO which captures the semantics of clinical data. It consists of: (1) Knowledge acquisition; (2) Conceptualization; (3) Semantic modeling; (4) Knowledge representation; and (5) Validation. The DQO was applied to the identification of patients with Type 2 Diabetes Mellitus (T2DM) in EHRs, which included an assessment of the DQ of the EHR. The five steps methodology is generalizable and reusable in other domains.
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