A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE

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dc.contributor.advisorShmeuli, Galiten_US
dc.contributor.authorYahav, Inbalen_US
dc.contributor.departmentBusiness and Management: Decision & Information Technologiesen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2010-10-07T05:45:22Z
dc.date.available2010-10-07T05:45:22Z
dc.date.issued2010en_US
dc.description.abstractIn this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to differentiate between models that are based on known baseline behavior and those based on a baseline with missing information. We apply and illustrate the framework in the context of two important healthcare contexts: biosurveillance and kidney allocation. In the biosurveillance context, we address the problem of early detection of disease outbreaks. We discuss integer programming-based univariate monitoring and statistical and operations research-based multivariate monitoring approaches. We assess method performance on authentic biosurveillance data. In the kidney allocation context, we present a two-phase model that combines an integer programming-based learning phase and a data-analytical based real-time phase. We examine and evaluate our method on the current Organ Procurement and Transplantation Network (OPTN) waiting list. In both contexts, we show that our framework produces significant improvements over existing methods.en_US
dc.identifier.urihttp://hdl.handle.net/1903/10826
dc.subject.pqcontrolledInformation Scienceen_US
dc.subject.pquncontrolledbiosurveillanceen_US
dc.subject.pquncontrolleddata miningen_US
dc.subject.pquncontrolleddecision supporten_US
dc.subject.pquncontrolledhealthcareen_US
dc.subject.pquncontrolledkidney allocationen_US
dc.subject.pquncontrolledreal-timeen_US
dc.titleA DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCAREen_US
dc.typeDissertationen_US

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