Feasibility of Prioritizing Drug–Drug-Event Associations Found in Electronic Health Records

Juan M. Banda (), Alison Callahan, Rainer Winnenburg, Howard R. Strasberg, Aurel Cami, Ben Y. Reis, Santiago Vilar, George Hripcsak, Michel Dumontier and Nigam Haresh Shah
Additional contact information
Juan M. Banda: Stanford Center for Biomedical Informatics Research
Alison Callahan: Stanford Center for Biomedical Informatics Research
Rainer Winnenburg: Stanford Center for Biomedical Informatics Research
Howard R. Strasberg: Wolters Kluwer Health
Aurel Cami: Boston Children’s Hospital
Ben Y. Reis: Boston Children’s Hospital
Santiago Vilar: Columbia University Medical Center
George Hripcsak: Columbia University Medical Center
Michel Dumontier: Stanford Center for Biomedical Informatics Research
Nigam Haresh Shah: Stanford Center for Biomedical Informatics Research

Drug Safety, 2016, vol. 39, issue 1, No 5, 45-57

Abstract: Abstract Background and Objective Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug–drug-adverse event associations derived from electronic health records (EHRs). Methods We prioritized drug–drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug–drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization. Results We collected information for 5983 putative EHR-derived drug–drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug–drug-event associations (

Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s40264-015-0352-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:drugsa:v:39:y:2016:i:1:d:10.1007_s40264-015-0352-2

Ordering information: This journal article can be ordered from
http://www.springer.com/adis/journal/40264

DOI: 10.1007/s40264-015-0352-2

Access Statistics for this article

Drug Safety is currently edited by Nitin Joshi

More articles in Drug Safety from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().