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dc.contributor.authorFranklin, Jade
dc.contributor.authorChari, Shruthi
dc.contributor.authorForeman, Morgan A.
dc.contributor.authorSeneviratne, Oshani
dc.contributor.authorGruen, Daniel M.
dc.contributor.authorMcCusker, Jamie
dc.contributor.authorDas, Amar K.
dc.contributor.authorMcGuinness, Deborah L.
dc.identifier.citationJay D. S. Franklin, Shruthi Chari, Morgan A. Foreman, Oshani Seneviratne, Daniel M. Gruen, James P. McCusker , Amar K. Das, Deborah L. McGuinness. Knowledge Extraction of Cohort Characteristics in Research Publications. American Medical Informatics Association (AMIA) Annual Conference 2020 (Regular paper)en_US
dc.description.abstractWhen healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the patients they see. We have previously created a study cohort ontology to standardize this information and make it accessible for knowledge-based decision support. The extraction of this information from research publications is challenging, however, given the wide variance in reporting cohort characteristics in a tabular representation. To address this issue, we have developed an ontology-enabled knowledge extraction pipeline for automatically constructing knowledge graphs from the cohort characteristics found in PDF-formatted research papers. We evaluated our approach using a training and test set of 41 research publications and found an overall accuracy of 83.3% in correctly assembling the knowledge graphs. Our research provides a promising approach for extracting knowledge more broadly from tabular information in research publications.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.titleKnowledge Extraction of Cohort Characteristics in Research Publicationsen_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States