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

Component identification;Healthcare utilization;Sequence clustering;Latent variable model;Medicaid system

Hilton, Ross P. ; Serban, Nicoleta Industrial and Systems Engineering Swann, Julie Vidakovic, Brani Griffin, Paul Braunstein, Mark L. ; Serban, Nicoleta

University:Georgia Institute of Technology

Department:Industrial and Systems Engineering

关键词: Component identification;    Healthcare utilization;    Sequence clustering;    Latent variable model;    Medicaid system;   

Others  :  https://smartech.gatech.edu/bitstream/1853/54387/1/HILTON-DISSERTATION-2015.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

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 usingwavelet-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 identifyingprofiles of patient utilization of the healthcare system from large, highly-sensitive, patient-level data. We combine model-based data mining methods with clustering analysisin 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 ofinterventions that improve adherence to recommended care guidelines. In Chapter IV, we propose new methods integrating survival analysis models and clustering analysis to profilepatient-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.