Advanced Visual Analytics Interfaces for Adverse Drug Event Detection

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MITTELSTÄDT, Sebastian, Ming C. HAO, Umeshwar DAYAL, Mei-Chun HSU, Joseph TERDIMAN, Daniel A. KEIM, 2014. Advanced Visual Analytics Interfaces for Adverse Drug Event Detection. AVI' 14 International Working Conference on Advanced Visual Interfaces. Como, May 27, 2014 - May 30, 2014. In: PAOLO PAOLINI ..., , ed.. AVI '14 Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces : May 27-30, 2014, Como, Italy. New York, NY:ACM, pp. 237-244. ISBN 978-1-4503-2775-6. Available under: doi: 10.1145/2598153.2598156

@inproceedings{Mittelstadt2014Advan-29993, title={Advanced Visual Analytics Interfaces for Adverse Drug Event Detection}, year={2014}, doi={10.1145/2598153.2598156}, isbn={978-1-4503-2775-6}, address={New York, NY}, publisher={ACM}, booktitle={AVI '14 Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces : May 27-30, 2014, Como, Italy}, pages={237--244}, editor={Paolo Paolini ...}, author={Mittelstädt, Sebastian and Hao, Ming C. and Dayal, Umeshwar and Hsu, Mei-Chun and Terdiman, Joseph and Keim, Daniel A.} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:creator>Dayal, Umeshwar</dc:creator> <dc:contributor>Mittelstädt, Sebastian</dc:contributor> <dc:contributor>Hao, Ming C.</dc:contributor> <dc:creator>Terdiman, Joseph</dc:creator> <bibo:uri rdf:resource=""/> <dc:contributor>Keim, Daniel A.</dc:contributor> <dcterms:available rdf:datatype="">2015-02-24T10:50:51Z</dcterms:available> <dc:contributor>Dayal, Umeshwar</dc:contributor> <dc:creator>Hao, Ming C.</dc:creator> <dcterms:title>Advanced Visual Analytics Interfaces for Adverse Drug Event Detection</dcterms:title> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Mittelstädt, Sebastian</dc:creator> <dcterms:rights rdf:resource=""/> <dc:contributor>Hsu, Mei-Chun</dc:contributor> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:isPartOf rdf:resource=""/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:hasPart rdf:resource=""/> <dc:date rdf:datatype="">2015-02-24T10:50:51Z</dc:date> <dcterms:issued>2014</dcterms:issued> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource=""/> <dc:contributor>Terdiman, Joseph</dc:contributor> <dc:creator>Hsu, Mei-Chun</dc:creator> <dcterms:abstract xml:lang="eng">Adverse reactions to drugs are a major public health care issue. Currently, the Food and Drug Administration (FDA) publishes quarterly reports that typically contain on the order of 200,000 adverse incidents. In such numerous incidents, low frequency events that are clinically highly significant often remain undetected. In this paper, we introduce a visual analytics system to solve this problem using (1) high scalable interfaces for analyzing correlations between a number of complex variables (e.g., drug and reaction); (2) enhanced statistical computations and interactive relevance filters to quickly identify significant events including those with a low frequency; and (3) a tight integration of expert knowledge for detecting and validating adverse drug events. We applied these techniques to the FDA Adverse Event Reporting System and were able to identify important adverse drug events, such as the known association of the drug Avandia with myocardial infarction and Seroquel with diabetes mellitus, as well as low frequency events such as the association of Boniva with femur fracture. In our evaluation, we found over 90% of the adverse drug events that were published in the Institute for Safe Medication Practices (ISMP) reports from 2009 to 2012. In addition, our domain expert was able to identify some previously unknown adverse drug events.</dcterms:abstract> <dspace:hasBitstream rdf:resource=""/> <dc:rights>terms-of-use</dc:rights> </rdf:Description> </rdf:RDF>

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