ORCID Identifier(s)

0000-0001-6882-731X

Graduation Semester and Year

2022

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Victoria Chen

Second Advisor

Jay M. Rosenberger

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.

Keywords

Pain management program, Statistical modeling, Adaptive decision framework, Optimization, LASSO, Interactions

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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