Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation.

Journal: Studies in health technology and informatics
Published Date: Jan 1, 2017

Abstract

In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.

Authors

  • Joseph Utecht
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
  • Mathias Brochhausen
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
  • John Judkins
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
  • Jodi Schneider
    School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
  • Richard D Boyce
    Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.

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