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    Ontology-enabled Analysis of Study Populations

    Author
    Chari, Shruthi; Qim, Miao; Agu, Nkechinyere; Seneviratne, Oshani; McCusker, Jamie; Bennett, Kristin; Das, Amar; McGuinness, Deborah
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    Date Issued
    2019-10-01
    Subject
    Health Empowerment by Analytics, Learning, and Semantics (HEALS)
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    URI
    http://ceur-ws.org/Vol-2456/paper30.pdf
    Abstract
    We address the problem of modeling study populations in research studies in a declarative manner. Research studies often have a great degree of variability in the reporting of population descriptions. To make study populations easily accessible for decision making related to study applicability, we will show the usage of our ontology-enabled prototype system in different applications. Our system leverages our Study Cohort Ontology and the related cohort Knowledge Graph (as described in our accepted resource track paper). We aim to address three retrospective population analysis scenarios, designed to specifically determine the study match, study limitations, and evaluate the study quality. We also provide visualizations of a patient (or patient population) to a treatment arm. In addition, for each guideline recommendation that depends upon a study, we provide a summary of the relevant study’s cohort description. We describe some of our applications and their potential impacts.;
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    https://tw.rpi.edu/project/HEALS;
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