Multi-objective two-stage stochastic programming for adaptive interdisciplinary pain management with piecewise linear network transition models

Pain is a major health problem for many people, and pain management is currently innovating because of the opioid crisis in the United States. Existing models optimizing personal adaptive treatment strategies for chronic pain management have only considered one pain outcome. However, most of the pai...

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Bibliographic Details
Published in:IISE transactions on healthcare systems engineering Vol. 11; no. 3; pp. 240 - 254
Main Authors: Iqbal, Gazi Md Daud, Rosenberger, Jay, Chen, Victoria, Gatchel, Robert, Noe, Carl
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 03.07.2021
Taylor & Francis Ltd
Subjects:
Linear programming
Mixed integer
Optimization
Pain
Pain management
Piecewise linear network model; mixed integer linear program; convex quadratic programming; two-stage stochastic programming; pain management; odd's ratio
Stochastic programming
ISSN:2472-5579, 2472-5587
Online Access:Get full text
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Summary:Pain is a major health problem for many people, and pain management is currently innovating because of the opioid crisis in the United States. Existing models optimizing personal adaptive treatment strategies for chronic pain management have only considered one pain outcome. However, most of the pain management centers consider multiple pain outcome measures to identify pain intensity. Consequently, this research uses five pain outcomes. Transition models are represented by piecewise linear networks (PLN). A multi-objective mixed integer linear program (MILP) is developed to optimize treatment strategies for patients based upon on these transition models. A convex quadratic program (QP) is developed to determine weights for multiple levels of multiple pain outcomes that are consistent with surveys submitted by pain management experts. Results show that the MILP that considers multiple pain outcomes yields treatment recommendations with better expected outcomes compared to observed data and to solutions from an optimization model with a single pain outcome objective.
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ISSN:2472-5579
2472-5587
DOI:10.1080/24725579.2021.1947922