Studying the Efficacy of and Developing Data-Driven Real-Time Clinical Decision Support Systems for Hypotension Detection

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dc.contributor.advisorHahn, Jin-Ohen_US
dc.contributor.advisorReisner, Andrew Ten_US
dc.contributor.authorYapps, Bryce Anthonyen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2017-01-25T06:39:36Z
dc.date.available2017-01-25T06:39:36Z
dc.date.issued2016en_US
dc.description.abstractCritically ill patients admitted into intensive care units are prone to reoccurring episodes of sustained hypotension. Prolonged durations of hypotension are correlated to, and potentially cause, permanent body-wide damage to patients if not properly treated, which may result in death. Currently, typical care for the management of hypotension in the critically ill is reactive and delayed, perhaps due to clinical inertia. The purpose of this study is to describe the current problem that is faced in critical care through a retrospective analysis and introduce candidate models that may be used as clinical informatics systems for preemptive hypotension detection to aid clinicians and nurses providing care in the fast-paced clinical environment. The clinical performance of the models is quantified and the efficacy of implementation of these models is discussed.en_US
dc.identifierhttps://doi.org/10.13016/M27K12
dc.identifier.urihttp://hdl.handle.net/1903/19089
dc.language.isoenen_US
dc.subject.pqcontrolledMechanical engineeringen_US
dc.subject.pqcontrolledBiomedical engineeringen_US
dc.subject.pqcontrolledInformation scienceen_US
dc.subject.pquncontrolledautomated critical care systemsen_US
dc.subject.pquncontrolleddata-driven modelingen_US
dc.subject.pquncontrolledhypotensionen_US
dc.titleStudying the Efficacy of and Developing Data-Driven Real-Time Clinical Decision Support Systems for Hypotension Detectionen_US
dc.typeThesisen_US

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