RESEARCH COMMONS
LIBRARY

Adaptive Pain Management Decision Support System

ResearchCommons/Manakin Repository

Adaptive Pain Management Decision Support System

Show simple item record

dc.contributor.author Lin, Ching-Feng en_US
dc.date.accessioned 2011-03-03T21:53:26Z
dc.date.available 2011-03-03T21:53:26Z
dc.date.issued 2011-03-03
dc.date.submitted January 2010 en_US
dc.identifier.other DISS-10983 en_US
dc.identifier.uri http://hdl.handle.net/10106/5530
dc.description.abstract Pain management is an international health issue. The Eugene McDermott Center for Pain Management at the University of Texas Southwestern MedicalCenter at Dallas conducts a two-stage interdisciplinary pain management program that considers a wide variety of treatments. Prior to treatment (stage 1), an evaluation records the patient's pain characteristics, medical history and related health parameters. A treatment regime is then determined. At the midpoint of their program (stage 2), an evaluation is conducted to determine if an adjustment in the treatment should be made. A final evaluation is conducted at the end of the program to assess final outcomes. The structure of this decision-making process uses dynamic programming (DP) to generate adaptive treatment strategies for this two-stage program. Our stochastic DP formulation considers the expected final outcomes when determining treatment. An approximate DP solution method is employed in which state transition models are constructed empirically based on data from the pain management program, and the future value function is approximated using state space discretization based on a Latin hypercube. The state transition probabilistically models how a patient's pain characteristics change from stage 1 to stage 2. The optimization seeks to minimize pain while penalizing excessive. en_US
dc.description.sponsorship Chen, Victoria en_US
dc.language.iso en en_US
dc.publisher Industrial & Manufacturing Engineering en_US
dc.title Adaptive Pain Management Decision Support System en_US
dc.type Ph.D. en_US
dc.contributor.committeeChair Chen, Victoria en_US
dc.degree.department Industrial & Manufacturing Engineering en_US
dc.degree.discipline Industrial & Manufacturing Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level doctoral en_US
dc.degree.name Ph.D. en_US

Files in this item

Files Size Format View
Lin_uta_2502D_10983.pdf 3.087Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Browse

My Account

Statistics

About Us