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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-01

AUTHORS

Juan M. Banda, Alison Callahan, Rainer Winnenburg, Howard R. Strasberg, Aurel Cami, Ben Y. Reis, Santiago Vilar, George Hripcsak, Michel Dumontier, Nigam Haresh Shah

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 (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources. CONCLUSIONS: Our proof-of-concept method for scoring putative drug-drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug-drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations. More... »

PAGES

45-57

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40264-015-0352-2

DOI

http://dx.doi.org/10.1007/s40264-015-0352-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024188600

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26446143


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40264-015-0352-2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40264-015-0352-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40264-015-0352-2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40264-015-0352-2'


 

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