Ontology-enabled Analysis of Study Populations

Authors
Chari, Shruthi
Qim, Miao
Agu, Nkechinyere
Seneviratne, Oshani
McCusker, Jamie
Bennett, Kristin P.
Das, Amar
McGuinness, Deborah L.
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Issue Date
2019-10-01
Keywords
Health Empowerment by Analytics, Learning, and Semantics (HEALS)
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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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