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dc.contributor.advisor | Chen, Victoria C.P. | |
dc.contributor.advisor | Rosenberger, Jay M | |
dc.creator | Viswanatha, Amith | |
dc.date.accessioned | 2022-09-15T14:06:29Z | |
dc.date.available | 2022-09-15T14:06:29Z | |
dc.date.created | 2022-08 | |
dc.date.issued | 2022-08-10 | |
dc.date.submitted | August 2022 | |
dc.identifier.uri | http://hdl.handle.net/10106/30983 | |
dc.description.abstract | The Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center has an interdisciplinary pain management program for chronic pain. This program treats patients with a holistic view of reducing chronic pain and improving their physical, mental, and social well-being through treatment interventions. The development of an adaptive treatment decision tool is main goal of the research project.
This program is modeled as a two-stage adaptive treatment decision problem, with state transition models representing the transition of patient state, treatment, and outcome variables from stage 1 to stage 2. Interactions between the patient state and treatments play a major role in determining a personalized treatment plan for individual patients. In this research, we address the challenge of modeling state-treatment interactions. We propose a LASSO based approach to develop the state transition models. The proposed approach is studied using a simulated case study structured based on the McDermott Center data. The state transition models built using the proposed method are then formulated within the multi-objective two-stage stochastic programming optimization to obtain an optimal treatment plan. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.subject | Pain management program | |
dc.subject | Statistical modeling | |
dc.subject | Adaptive decision framework | |
dc.subject | Optimization | |
dc.subject | LASSO | |
dc.subject | Interactions | |
dc.title | LASSO Based State Transition Modeling with Interactions in Adaptive Interdisciplinary Pain Management | |
dc.type | Thesis | |
dc.degree.department | Industrial and Manufacturing Systems Engineering | |
dc.degree.name | Doctor of Philosophy in Industrial Engineering | |
dc.date.updated | 2022-09-15T14:06:29Z | |
thesis.degree.department | Industrial and Manufacturing Systems Engineering | |
thesis.degree.grantor | The University of Texas at Arlington | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Doctor of Philosophy in Industrial Engineering | |
dc.type.material | text | |
dc.creator.orcid | 0000-0001-6882-731X | |
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