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.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54387 |
| Date | 07 January 2016 |
| Creators | Hilton, Ross P. |
| Contributors | Serban, Nicoleta |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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