A method for systematic discovery of adverse drug events from clinical notes. (31st July 2015)

• Record Type: Journal Article
• Authors: Wang, Guan; Jung, Kenneth; Winnenburg, Rainer; Shah, Nigam H
• Is Part Of: Journal of the American Medical Informatics Association. Volume 22:Number 6(2015:Nov.)
• Page Start: 1196
• Page End: 1204
• Publication Date: 2015-07-31
• Subjects: post market drug safety surveillance -- pharmacovigilance -- machine learning -- EMR mining; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv102
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective Adverse drug events (ADEs) are undesired harmful effects resulting from use of a medication, and occur in 30% of hospitalized patients. The authors have developed a data-mining method for systematic, automated detection of ADEs from electronic medical records. Materials and Methods This method uses the text from 9.5 million clinical notes, along with prior knowledge of drug usages and known ADEs, as inputs. These inputs are further processed into statistics used by a discriminative classifier which outputs the probability that a given drug–disorder pair represents a valid ADE association. Putative ADEs identified by the classifier are further filtered for positive support in 2 independent, complementary data sources. The authors evaluate this method by assessing support for the predictions in other curated data sources, including a manually curated, time-indexed reference standard of label change events. Results This method uses a classifier that achieves an area under the curve of 0.94 on a held out test set. The classifier is used on 2 362 950 possible drug–disorder pairs comprised of 1602 unique drugs and 1475 unique disorders for which we had data, resulting in 240 high-confidence, well-supported drug-AE associations. Eighty-seven of them (36%) are supported in at least one of the resources that have information that was not available to the classifier. Conclusion This method demonstrates the feasibility of systematic post-marketing surveillance for ADEs using electronic medical records, a key component of the learning healthcare system.