UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2013 - 20172

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    Application of Mathematical and Computational Models to Mitigate the Overutilization of Healthcare Systems
    (2017) Hu, Xia; Golden, Bruce; Barnes, Sean; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The overutilization of the healthcare system has been a significant issue financially and politically, placing burdens on the government, patients, providers and individual payers. In this dissertation, we study how mathematical models and computational models can be utilized to support healthcare decision-making and generate effective interventions for healthcare overcrowding. We focus on applying operations research and data mining methods to mitigate the overutilization of emergency department and inpatient services in four scenarios. Firstly, we systematically review research articles that apply analytical queueing models to the study of the emergency department, with an additional focus on comparing simulation models with queueing models when applied to similar research questions. Secondly, we present an agent-based simulation model of epidemic and bioterrorism transmission, and develop a prediction scheme to differentiate the simulated transmission patterns during the initial stage of the event. Thirdly, we develop a machine learning framework for effectively selecting enrollees for case management based on Medicaid claims data, and demonstrate the importance of enrolling current infrequent users whose utilization of emergency visits might increase significantly in the future. Lastly, we study the role of temporal features in predicting future health outcomes for diabetes patients, and identify the levels to which the aggregation can be most informative.
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    Regulation of Systemic Risk Through Contributory Endogenous Agent-Based Modeling
    (2013) Bristor, Aurora; Fu, Michael; Barnes, Sean; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The Financial Stability Oversight Council (FSOC) was created to identify and respond to emerging threats to the stability of the United States financial system. The research arm of the FSOC, the Office of Financial Research (OFR), has begun to explore agent-based models (ABMs) for measuring the emergent threat of systemic risk. We propose an ABM-based regulatory structure that incentivizes the honest participation and data contribution of regulated firms while providing clarity into the actions of the firms as endogenous to the market and driving emergent behavior. We build this scheme onto an existing ABM of a single-asset market to examine whether the structure of the scheme could provide its own benefits to market stabilization. We find that without regulatory intervention, markets acting within this proposed structure experience fewer bankruptcies and lower leverage buildup while returning larger profits for the same amount of risk.