Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base

Abstract : Semantic web technologies can support the rapid and transparent validation of scientific claims by interconnecting the assumptions and evidence used to support or challenge assertions. One important application domain is medication safety, where more efficient acquisition, representation, and synthesis of evidence about potential drugdrug interactions is needed. Potential drugdrug interactions (PDDIs), defined as two or more drugs for which an interaction is known to be possible, are a significant source of preventable drugrelated harm. The combination of poor quality evidence on PDDIs, and a general lack of PDDI knowledge by prescribers, results in many thousands of preventable medication errors each year. While many sources of PDDI evidence exist to help improve prescriber knowledge, they are not concordant in their coverage, accuracy, and agreement. The goal of this project is to research and develop core components of a new model that supports more efficient acquisition, representation, and synthesis of evidence about potential drugdrug interactions. Two Semantic Web models—the Micropublications Ontology and the Open Annotation Data Model—have great potential to provide linkages from PDDI assertions to their supporting evidence: statements in source documents that mention data, materials, and methods. In this paper, we describe the context and goals of our work, propose competency questions for a dynamic PDDI evidence base, outline our new knowledge representation model for PDDIs, and discuss the challenges and potential of our approach.
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Communication dans un congrès
Workshop on Linked Science 2014—Making Sense Out of Data (LISC2014) at ISWC 2014, Oct 2014, Riva de Garda, Italy. 2014
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  • HAL Id : hal-01076282, version 1

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Jodi Schneider, Paolo Ciccarese, Tim Clark, Richard D. Boyce. Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base. Workshop on Linked Science 2014—Making Sense Out of Data (LISC2014) at ISWC 2014, Oct 2014, Riva de Garda, Italy. 2014. <hal-01076282>

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