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dc.contributor.advisorSerban, Nicoleta
dc.contributor.authorHilton, Ross P.
dc.date.accessioned2016-01-07T17:25:20Z
dc.date.available2016-01-07T17:25:20Z
dc.date.created2015-12
dc.date.issued2015-10-12
dc.date.submittedDecember 2015
dc.identifier.urihttp://hdl.handle.net/1853/54387
dc.description.abstractIn this thesis we provide statistical and model-based data mining methods for pattern detection with applications to biomedical and healthcare data sets. In particular, we examine applications in costly acute or chronic disease management. In Chapter II, we consider nuclear magnetic resonance experiments in which we seek to locate and demix smooth, yet highly localized components in a noisy two-dimensional signal. By using wavelet-based methods we are able to separate components from the noisy background, as well as from other neighboring components. In Chapter III, we pilot methods for identifying profiles of patient utilization of the healthcare system from large, highly-sensitive, patient-level data. We combine model-based data mining methods with clustering analysis in order to extract longitudinal utilization profiles. We transform these profiles into simple visual displays that can inform policy decisions and quantify the potential cost savings of interventions that improve adherence to recommended care guidelines. In Chapter IV, we propose new methods integrating survival analysis models and clustering analysis to profile patient-level utilization behaviors while controlling for variations in the population’s demographic and healthcare characteristics and explaining variations in utilization due to different state-based Medicaid programs, as well as access and urbanicity measures.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectComponent identification
dc.subjectHealthcare utilization
dc.subjectSequence clustering
dc.subjectLatent variable model
dc.subjectMedicaid system
dc.titleModel-based data mining methods for identifying patterns in biomedical and health data
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentIndustrial and Systems Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberSwann, Julie
dc.contributor.committeeMemberVidakovic, Brani
dc.contributor.committeeMemberGriffin, Paul
dc.contributor.committeeMemberBraunstein, Mark L.
dc.date.updated2016-01-07T17:25:20Z


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