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Learning Drug-Drug Interactions from the Unstructured Text of Electronic Health Records

By Srinivasan V Iyer, Paea Lependu, Rave Harpaz, Anna Bauer-mehren and Nigam H Shah

Abstract

Drug-drug interactions (DDI) account for 30 % of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using over 10 million clinical notes from the Stanford Hospital confirm that learning DDIs from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDIs and has enormous utility in drug surveillance and clinical decision support

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.227
Provided by: CiteSeerX
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