Title:
Model-based data mining methods for identifying patterns in biomedical and health data

dc.contributor.advisor Serban, Nicoleta
dc.contributor.author Hilton, Ross P.
dc.contributor.committeeMember Swann, Julie
dc.contributor.committeeMember Vidakovic, Brani
dc.contributor.committeeMember Griffin, Paul
dc.contributor.committeeMember Braunstein, Mark L.
dc.contributor.department Industrial and Systems Engineering
dc.date.accessioned 2016-01-07T17:25:20Z
dc.date.available 2016-01-07T17:25:20Z
dc.date.created 2015-12
dc.date.issued 2015-10-12
dc.date.submitted December 2015
dc.date.updated 2016-01-07T17:25:20Z
dc.description.abstract In 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.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/54387
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Component identification
dc.subject Healthcare utilization
dc.subject Sequence clustering
dc.subject Latent variable model
dc.subject Medicaid system
dc.title Model-based data mining methods for identifying patterns in biomedical and health data
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Serban, Nicoleta
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 63115986-db70-4c06-87c4-dab394286f67
relation.isOrgUnitOfPublication 29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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