Stochastic programming for interdisciplinary pain management

Gazi Md Daud Iqbal, Jay M. Rosenberger, Victoria C.P. Chen, Rohit Rawat, Robert J. Gatchel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pain is the most common symptom when a patient visits a physician. People experience pain throughout their lifetime at different degrees. If short term pain is not treated properly, then it can become long term pain, which is also known as chronic pain. The Eugene McDermott Center for pain management at UT Southwestern Medical Center conducts a two-stage pain management program for chronic pain. This research uses a two-stage stochastic programming approach to optimize personal adaptive treatment strategies for pain management. The goal is to generate adaptive treatment strategies using statistics based optimization approaches that can be used by physicians to prescribe treatment to the patients. Transition models predict how a patient with certain characteristics will react to treatments. This research uses Piecewise Linear Networks (PLN) to represent transition models. A mixed integer linear program is developed to integrate those PLN transition models into an optimization problem.

Original languageEnglish
Title of host publication67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
EditorsHarriet B. Nembhard, Katie Coperich, Elizabeth Cudney
PublisherInstitute of Industrial Engineers
Pages946-951
Number of pages6
ISBN (Electronic)9780983762461
StatePublished - 1 Jan 2017
Event67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 - Pittsburgh, United States
Duration: 20 May 201723 May 2017

Publication series

Name67th Annual Conference and Expo of the Institute of Industrial Engineers 2017

Other

Other67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
CountryUnited States
CityPittsburgh
Period20/05/1723/05/17

    Fingerprint

Keywords

  • Mixed integer linear programming
  • Pain management
  • Piece-wise linear network model
  • Two-stage stochastic optimization

Cite this

Iqbal, G. M. D., Rosenberger, J. M., Chen, V. C. P., Rawat, R., & Gatchel, R. J. (2017). Stochastic programming for interdisciplinary pain management. In H. B. Nembhard, K. Coperich, & E. Cudney (Eds.), 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 (pp. 946-951). (67th Annual Conference and Expo of the Institute of Industrial Engineers 2017). Institute of Industrial Engineers.