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Medication safety as a use case for argumentation mining
We present a use case for argumentation mining, from biomedical informatics, specifically from medication safety. Tens of thousands of preventable medical errors occur in the U.S. each year, due to limitations in the information available to clinicians. Current knowledge sources about potential drug-drug interactions (PDDIs) often fail to provide essential management recommendations and differ significantly in their coverage, accuracy, and agreement. The Drug Interaction Knowledge Base Project (Boyce, 2006-present; dikb.org) is addressing this problem.
Our current work is using knowledge representations and human annotation in order to represent clinically-relevant claims and evidence. Our data model incorporates an existing argumentation-focused ontology, the Micropublications Ontology. Further, to describe more specific information, such as the types of studies that allow inference of a particular type of claim, we are developing an evidence-focused ontology called DIDEO--Drug-drug Interaction and Drug-drug Interaction Evidence Ontology. On the curation side, we will describe how our research team is hand-extracting knowledge claims and evidence from the primary research literature, case reports, and FDA-approved drug labels for 65 drugs.
We think that medication safety could be an important domain for applying automatic argumentation mining in the future. In discussions at Dagstuhl, we would like to investigate how current argumentation mining techniques might be used to scale up this work. We can also discuss possible implications for representing evidence from other biomedical domains.
Talk for Dagstuhl Seminar 16161: Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments