Using semantic web technologies for cohort identification from electronic health records for clinical research

The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. One of the key requirements to perform GWAS is the identification of subject cohorts with accurate classification of disease phenotypes. In t...

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Bibliographic Details
Published inAMIA Summits on Translational Science proceedings Vol. 2012; pp. 10 - 19
Main Authors Pathak, Jyotishman, Kiefer, Richard C, Chute, Christopher G
Format Journal Article
LanguageEnglish
Published United States American Medical Informatics Association 2012
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Summary:The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. One of the key requirements to perform GWAS is the identification of subject cohorts with accurate classification of disease phenotypes. In this work, we study how emerging Semantic Web technologies can be applied in conjunction with clinical data stored in electronic health records (EHRs) to accurately identify subjects with specific diseases for inclusion in cohort studies. In particular, we demonstrate the role of using Resource Description Framework (RDF) for representing EHR data and enabling federated querying and inferencing via standardized Web protocols for identifying subjects with Diabetes Mellitus. Our study highlights the potential of using Web-scale data federation approaches to execute complex queries.
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ISSN:2153-4063