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A poster for the National Library of Medicine Informatics Training Conference 2016: https://u.osu.edu/nlm2016/conference-agenda/
Potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm. Poor quality evidence on PDDIs, combined with prescribers’ general lack of PDDI knowledge, results in thousands of preventable medication errors each year. One contributing factor is that PDDI knowledge lacks a standard computable format. To address this, we are researching efficient strategies for acquiring and representing PDDIs knowledge, focusing on assertions and their supporting evidence.
We are acquiring knowledge from several sources. First, we have transformed 410 assertions and 519 evidence items from prior work. Second, we are examining FDA-approved drug labels, and so far annotators have identified 609 evidence items relating to pharmacokinetic PDDIs from 27 FDA-approved drug labels. Third, annotators have found 230 assertions of drug-drug interactions in 158 non-regulatory documents, including full text research articles.
We are building a two-layer evidence representation, with both generic and domain-specific layers. The generic layer reuses the Micropublications Ontology to annotate assertions and their supporting data, methods, and materials. For the domain-specific component we are building DIDEO–the Drug-drug Interaction and Drug-drug Interaction Evidence Ontology. DIDEO adds specific knowledge, such as the study types required to establish a given type of PDDI. The current version of DIDEO has 385 subclass axioms, and reuses formalized knowledge items, including from the Drug Ontology, Chemical Entities of Biological Interest, the Ontology of Biomedical Investigations, and the Gene Ontology.