A Data Analytical Framework for Improving Real-Time, Decision Support Systems in Healthcare

Autor/in	Yahav, Inbal
Titel	A Data Analytical Framework for Improving Real-Time, Decision Support Systems in Healthcare
Quelle	(2010), (194 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, University of Maryland, College Park
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-1242-6868-2
Schlagwörter	Hochschulschrift; Dissertation; Operations Research; Research Methodology; Identification; Programming; Decision Support Systems; Health Services; Decision Making; Management Information Systems; Scientific and Technical Information; Case Studies; Data Analysis; Synchronous Communication; Information Management; Data Processing; Improvement Programs; Performance Technology; United States + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Research method; Forschungsmethode; Identifikation; Identifizierung; Programmierung; Entscheidungshilfe; Health service; Gesundheitsdienst; Gesundheitswesen; Decision-making; Entscheidungsfindung; Managementinformationssystem; Case study; Fallstudie; Case Study; Auswertung; Procurement of information; Informationsbeschaffung; Datenverarbeitung; Effizienzsteigerung; USA
Abstract	In 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 out-breaks. 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. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10