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dc.contributor.advisorCollin M. Stultz.en_US
dc.contributor.authorMyers, Paul Danielen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2017-10-18T15:10:22Z
dc.date.available2017-10-18T15:10:22Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111927
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 55-61).en_US
dc.description.abstractThe accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care for patients with cardiovascular disease. Commonly used risk metrics have traditionally been based on simple models that incorporate various aspects of the medical history, presenting signs and symptoms, and lab values. More sophisticated methods, such as those based on signal processing and machine learning, form an attractive platform to build improved risk metrics because they can offer deeper insights into aspects of clinical data that cannot be approached by simpler methods. In particular, generalized additive models can exhibit comparable or superior performance to conventional logistic regression models, while simultaneously providing potentially useful prognostic information on the relationship between patient outcomes and clinical variables. Moreover, artificial neural networks can provide a convenient formalism for combining rich time series information from physiological signals with more conventional patient features. In this work, models based on signal processing and machine learning techniques are developed and applied to two independent datasets consisting of post-acute coronary syndrome patients to predict cardiovascular death at various endpoints. The models are shown to successfully identify high-risk patients, thereby demonstrating the potential of these techniques to improve the management of patients with cardiovascular disease.en_US
dc.description.statementofresponsibilityby Paul Daniel Myers.en_US
dc.format.extent61 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleIdentifying patients at high risk of death with novel computational biomarkersen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1005737131en_US


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