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Mathematical modeling and decision-making within the healthcare industry have given means to quantitatively evaluate the impact of decisions into diagnosis, screening, and treatment of diseases. In this work, we look into a specific, yet very important disease, the Alzheimer. In the United States, Alzheimer’s Disease (AD) is the 6th leading

Mathematical modeling and decision-making within the healthcare industry have given means to quantitatively evaluate the impact of decisions into diagnosis, screening, and treatment of diseases. In this work, we look into a specific, yet very important disease, the Alzheimer. In the United States, Alzheimer’s Disease (AD) is the 6th leading cause of death. Diagnosis of AD cannot be confidently confirmed until after death. This has prompted the importance of early diagnosis of AD, based upon symptoms of cognitive decline. A symptom of early cognitive decline and indicator of AD is Mild Cognitive Impairment (MCI). In addition to this qualitative test, Biomarker tests have been proposed in the medical field including p-Tau, FDG-PET, and hippocampal. These tests can be administered to patients as early detectors of AD thus improving patients’ life quality and potentially reducing the costs of the health structure. Preliminary work has been conducted in the development of a Sequential Tree Based Classifier (STC), which helps medical providers predict if a patient will contract AD or not, by sequentially testing these biomarker tests. The STC model, however, has its limitations and the need for a more complex, robust model is needed. In fact, STC assumes a general linear model as the status of the patient based upon the tests results. We take a simulation perspective and try to define a more complex model that represents the patient evolution in time.

Specifically, this thesis focuses on the formulation of a Markov Chain model that is complex and robust. This Markov Chain model emulates the evolution of MCI patients based upon doctor visits and the sequential administration of biomarker tests. Data provided to create this Markov Chain model were collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data lacked detailed information of the sequential administration of the biomarker tests and therefore, different analytical approaches were tried and conducted in order to calibrate the model. The resulting Markov Chain model provided the capability to conduct experiments regarding different parameters of the Markov Chain and yielded different results of patients that contracted AD and those that did not, leading to important insights into effect of thresholds and sequence on patient prediction capability as well as health costs reduction.



The data in this thesis was provided from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI investigators did not contribute to any analysis or writing of this thesis. A list of the ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal-investigators/ .
ContributorsCamarena, Raquel (Author) / Pedrielli, Giulia (Thesis advisor) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several sequencing and patient appointment time setting heuristics are proposed for scheduling an Outpatient Procedure Center. A discrete event simulation model

Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several sequencing and patient appointment time setting heuristics are proposed for scheduling an Outpatient Procedure Center. A discrete event simulation model is used to evaluate how scheduling heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared to current practice. Next, a bi-criteria Genetic Algorithm is used to determine if better solutions can be obtained for this single day scheduling problem. The efficacy of the bi-criteria Genetic Algorithm, when surgeries are allowed to be moved to other days, is investigated. Numerical experiments based on real data from a large health care provider are presented. The analysis provides insight into the best scheduling heuristics, and the tradeoff between patient and health care provider based criteria. Second, a multi-stage stochastic mixed integer programming formulation for the allocation of surgeries to ORs over a finite planning horizon is studied. The demand for surgery and surgical duration are random variables. The objective is to minimize two competing criteria: expected surgery cancellations and OR overtime. A decomposition method, Progressive Hedging, is implemented to find near optimal surgery plans. Finally, properties of the model are discussed and methods are proposed to improve the performance of the algorithm based on the special structure of the model. It is found simple rules can improve schedules used in practice. Sequencing surgeries from the longest to shortest mean duration causes high expected overtime, and should be avoided, while sequencing from the shortest to longest mean duration performed quite well in our experiments. Expending greater computational effort with more sophisticated optimization methods does not lead to substantial improvements. However, controlling daily procedure mix may achieve substantial improvements in performance. A novel stochastic programming model for a dynamic surgery planning problem is proposed in the dissertation. The efficacy of the progressive hedging algorithm is investigated. It is found there is a significant correlation between the performance of the algorithm and type and number of scenario bundles in a problem instance. The computational time spent to solve scenario subproblems is among the most significant factors that impact the performance of the algorithm. The quality of the solutions can be improved by detecting and preventing cyclical behaviors.
ContributorsGul, Serhat (Author) / Fowler, John W. (Thesis advisor) / Denton, Brian T. (Thesis advisor) / Wu, Teresa (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2010
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Description
Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task

Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task due to the conflicting objectives and the uncertain nature of surgeries. In this dissertation, three different methodologies are developed to address OR planning and scheduling problem. First, a simulation-based framework is constructed to analyze the factors that affect the utilization of a catheterization lab and provide decision support for improving the efficiency of operations in a hospital with different priorities of patients. Both operational costs and patient satisfaction metrics are considered. Detailed parametric analysis is performed to provide generic recommendations. Overall it is found the 75th percentile of process duration is always on the efficient frontier and is a good compromise of both objectives. Next, the general OR planning and scheduling problem is formulated with a mixed integer program. The objectives include reducing staff overtime, OR idle time and patient waiting time, as well as satisfying surgeon preferences and regulating patient flow from OR to the Post Anesthesia Care Unit (PACU). Exact solutions are obtained using real data. Heuristics and a random keys genetic algorithm (RKGA) are used in the scheduling phase and compared with the optimal solutions. Interacting effects between planning and scheduling are also investigated. Lastly, a multi-objective simulation optimization approach is developed, which relaxes the deterministic assumption in the second study by integrating an optimization module of a RKGA implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for Pareto optimal solutions, and a simulation module to evaluate the performance of a given schedule. It is experimentally shown to be an effective technique for finding Pareto optimal solutions.
ContributorsLi, Qing (Author) / Fowler, John W (Thesis advisor) / Mohan, Srimathy (Thesis advisor) / Gopalakrishnan, Mohan (Committee member) / Askin, Ronald G. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2010
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Description

This thesis attempts to explain Everettian quantum mechanics from the ground up, such that those with little to no experience in quantum physics can understand it. First, we introduce the history of quantum theory, and some concepts that make up the framework of quantum physics. Through these concepts, we reveal

This thesis attempts to explain Everettian quantum mechanics from the ground up, such that those with little to no experience in quantum physics can understand it. First, we introduce the history of quantum theory, and some concepts that make up the framework of quantum physics. Through these concepts, we reveal why interpretations are necessary to map the quantum world onto our classical world. We then introduce the Copenhagen interpretation, and how many-worlds differs from it. From there, we dive into the concepts of entanglement and decoherence, explaining how worlds branch in an Everettian universe, and how an Everettian universe can appear as our classical observed world. From there, we attempt to answer common questions about many-worlds and discuss whether there are philosophical ramifications to believing such a theory. Finally, we look at whether the many-worlds interpretation can be proven, and why one might choose to believe it.

ContributorsSecrest, Micah (Author) / Foy, Joseph (Thesis director) / Hines, Taylor (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

The purpose of this paper is to provide an analysis of entanglement and the particular problems it poses for some physicists. In addition to looking at the history of entanglement and non-locality, this paper will use the Bell Test as a means for demonstrating how entanglement works, which measures the

The purpose of this paper is to provide an analysis of entanglement and the particular problems it poses for some physicists. In addition to looking at the history of entanglement and non-locality, this paper will use the Bell Test as a means for demonstrating how entanglement works, which measures the behavior of electrons whose combined internal angular momentum is zero. This paper will go over Dr. Bell's famous inequality, which shows why the process of entanglement cannot be explained by traditional means of local processes. Entanglement will be viewed initially through the Copenhagen Interpretation, but this paper will also look at two particular models of quantum mechanics, de-Broglie Bohm theory and Everett's Many-Worlds Interpretation, and observe how they explain the behavior of spin and entangled particles compared to the Copenhagen Interpretation.

ContributorsWood, Keaten Lawrence (Author) / Foy, Joseph (Thesis director) / Hines, Taylor (Committee member) / Department of Physics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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The goal of this project was to develop a prototype for an educational tool that will help users understand how the voting system deployed by a government can affect the outcomes of elections. This tool was developed in Java SE, consisting of a model for the simulation of elections capable

The goal of this project was to develop a prototype for an educational tool that will help users understand how the voting system deployed by a government can affect the outcomes of elections. This tool was developed in Java SE, consisting of a model for the simulation of elections capable of supporting various voting systems, along with a variety of fairness measures, and educational and explanatory material. While a completed version of this tool would ideally be fully self-contained, easily accessible in-browser, and provide detailed visualizations of the simulated elections, the current prototype version consists of a GitHub repository containing the code, with the educational material and explanations contained within the thesis paper. Ultimately, the goal of this project was to be a stepping stone on the path to create a tool that will instill a measure of systemic skepticism in the user; to give them cause to question why our systems are built the way they are, and reasons to believe that they could be changed for the better. In undertaking this project, I hope to help in providing people with the political education needed to make informed decisions about how they want the government to function. The GitHub repository containing all the code can be found at, https://github.com/SpencerDiamond/Votes_that_Count

ContributorsDiamond, Spencer (Author) / Sarjoughian, Hessam (Thesis director) / Hines, Taylor (Committee member) / Barrett, The Honors College (Contributor) / Department of Physics (Contributor) / Department of English (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05