Graduation Semester and Year
2013
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Victoria Chen
Abstract
Pain management is a major global health problem. The World Health Organization estimates that, globally, 1 in 5 adults suffer from chronic pain and in the United States alone, chronic pain affects nearly 100 million adults resulting in an estimated annual cost of $560 to $635 billion. The University of Texas at Arlington and the Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center at Dallas (The Center) are collaborating to seek adaptive treatment strategies for interdisciplinary pain management in a two-stage program. Interdisciplinary pain management combines multiple disciplines of professionals to understand the biological and psychosocial factors causing a patient's pain and to determine the best treatments among many to administer. To improve current and future pain outcomes, our adaptive interdisciplinary pain management framework employs approximate dynamic programming with state transition and outcome models estimated from actual patient data. The sequential treatment structure of the data leads to a form of endogeneity. This research develops a process based on the inverse probability of treatment weighted method to address the endogeneity while estimating state transition and outcome models. First, a method is developed for independent treatments then a general method is developed for correlated treatments. Results are presented using data from the Center.
Disciplines
Engineering | Operations Research, Systems Engineering and Industrial Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
LeBoulluec, Aera Kim, "Outcome And State Transition Modeling For Adaptive Interdisciplinary Pain Management" (2013). Industrial, Manufacturing, and Systems Engineering Dissertations. 7.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/7
Comments
Degree granted by The University of Texas at Arlington