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- Genre: Doctoral Dissertation
Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
ContributorsBrown, Justin Reed (Author) / Dinu, Valentin (Thesis advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Created2012
Description
Parkinson's disease, the most prevalent movement disorder of the central nervous system, is a chronic condition that affects more than 1000,000 U.S. residents and about 3% of the population over the age of 65. The characteristic symptoms include tremors, bradykinesia, rigidity and impaired postural stability. Current therapy based on augmentation or replacement of dopamine is designed to improve patients' motor performance but often leads to levodopa-induced complications, such as dyskinesia and motor fluctuation. With the disease progress, clinicians must closely monitor patients' progress in order to identify any complications or decline in motor function as soon as possible in PD management. Unfortunately, current clinical assessment for Parkinson's is subjective and mostly influenced by brief observations during patient visits. Thus improvement or decline in patients' motor function in between visits is extremely difficult to assess. This may hamper clinicians while making informed decisions about the course of therapy for Parkinson's patients and could negatively impact clinical care. In this study we explored new approaches for PD assessment that aim to provide home-based PD assessment and monitoring. By extending the disease assessment to home, the healthcare burden on patients and their family can be reduced, and the disease progress can be more closely monitored by physicians. To achieve these aims, two novel approaches have been designed, developed and validated. The first approach is a questionnaire based self-evaluation metric, which estimate the PD severity through using self-evaluation score on pre-designed questions. Based on the results of the first approach, a smart phone based approach was invented. The approach takes advantage of the mobile computing technology and clinical decision support approach to evaluate the motor performance of patient daily activity and provide the longitudinal disease assessment and monitoring. Both approaches have been validated on recruited PD patients at the movement disorder program of Barrow Neurological Clinic (BNC) at St Joseph's Hospital and Medical Center. The results of validation tests showed favorable accuracy on detecting and assessing critical symptoms of PD, and shed light on promising future of implementing mobile platform based PD evaluation and monitoring tools to facilitate PD management.
ContributorsPan, Di (Author) / Petitti, Diana (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Dhall, Rohit (Committee member) / Arizona State University (Publisher)
Created2013