Two-stage stochastic programming for interdisciplinary pain management

Wang, Na; Rosenberger, Jay M.; Iqbal, Gazi Md Daud; Chen, Victoria; Gatchel, Robert J.; Noe, Carl E.; LeBoulluec, Aera Kim

doi:10.1080/24725579.2019.1610528

Cited by 3 publications

(23 citation statements)

References 27 publications

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“…Wang [14] developed a two-stage stochastic programming model for adaptive pain management, where transition models that are used as constraints are non-convex and quadratic. These nonconvex quadratic models are then refitted using a piecewise linear approximation.…”

Section: Background Of Pain Management Researchmentioning

confidence: 99%

“…Result shows that, the MILP gives optimal solution for most of the patients within 6 minutes. Then we compare the results with Wang's [14] results, who used regression approach to developed transition models. To speed up the solution process, we developed additional constraints based upon 3-way treatment interactions.…”

Section: Contributionmentioning

confidence: 99%

“…The purpose of the cost function in the objective is to put higher cost on treatment usage to avoid over medication. The cost function used in this research is from [14]. Parameter ρ is a treatment cost coefficient, which is used to maintain a balance between the pain outcomes and the treatment cost function.…”

Section: Stochastic Programming Formulationmentioning

confidence: 99%

“…These features fit a PLN model to the data, and remove useless inputs. We then ran the optimiza-tion problem to determine treatment policy, and compared the treatment policy with observed data and policies found in Wang [14]. A 6-minute time was used for the computational experiments.…”

Section: Case Studymentioning

confidence: 99%

“…This section compares treatment usage in different scenarios, and treatment usage frequency among this research, Wang [14], and observed data in both stages. Procedures (ProcGr1 1) are applied to patients with low frequency (< 10%) in all the scenario sets.…”

Section: Treatment Analysismentioning

confidence: 99%

See 4 more Smart Citations

Multi-Objective Two-Stage Stochastic Programming for Adaptive Interdisciplinary Pain Management with Piecewise Linear Network Transition Models

Iqbal

Rosenberger

Chen

et al. 2021

IISE Transactions on Healthcare Systems Engineering

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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.In this research we have considered five pain outcomes. To balance between different pain outcomes in the objective, we developed a survey for physicians, which is actually a pairwise comparison of different levels of different pain outcomes. Survey inputs are subjective and vary from physician to physician. In other words, inputs from multiple surveys are not entirely consistent.To get consistent weights for different levels of different pain outcomes in the two-stage stochastic program, we developed a convex quadratic programming model.To speed up the solution process, we developed additional constraints based upon 3-way treatment interactions. These 3-way treatment interaction constraints are totally consistent with twoway treatment interaction constraints. These additional constraints do not eliminate real integer solutions, but they may eliminate fractional solutions in the branch-and-bound algorithm. We then solved the original MILP with these additional logical style constraints to see the improvement in MILP.

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Section: Background Of Pain Management Researchmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Stochastic Programming Formulationmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Treatment Analysismentioning

confidence: 99%

See 3 more Smart Citations

Multi-Objective Two-Stage Stochastic Programming for Adaptive Interdisciplinary Pain Management with Piecewise Linear Network Transition Models

Iqbal

Rosenberger

Chen

et al. 2021

IISE Transactions on Healthcare Systems Engineering

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An Optimization Framework to Study the Balance Between Expected Fatalities Due to COVID-19 and the Reopening of U.S. Communities

Chen

Zhou

Fallahi

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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As communities reopen following shelter-in-place orders, they are facing two conflicting objectives. The first is to keep the COVID-19 fatality rate down. The second is to revive the U.S. economy and the livelihood of millions of Americans. In this paper, a team of researchers from the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at the University of Texas at Arlington, in collaboration with researchers from University of Texas Southwestern Medical Center and Harvard Medical School, has formulated a computationally-efficient optimization framework, referred to as COSMOS COVID-19 Linear Programming (CC19LP), to study the delicate balance between the expected fatality rate and the level of normalcy in the community. Given the disproportionate fatality characteristics of COVID-19 among those in different age groups or with an underlying medical condition or those living with crowding, the key to the CC19LP framework is a focus on "key contacts" that separate individuals at higher risk from the rest of the population. The philosophy of CC19LP lies in maximizing protection of key contacts, so as to shield high-risk individuals from infection. Given the lack of pharmaceutical solutions, i.e., a vaccine or cure, the CC19LP framework minimizes expected fatalities by optimizing the use of non-pharmaceutical interventions, namely COVID-19 testing; personal protective equipment; and social precautions, such as distancing, hand-washing, and face coverings. Low-risk individuals that are not key contacts, including most children, are unrestricted and can choose to participate in pre-pandemic normal activities, which eliminates the need for compliance across the entire population. Consequently, the CC19LP framework demonstrates optimal strategies for protecting high-risk individuals while reopening communities.

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A discrete-event simulation tool for airport deicing activities: Dallas-Fort Worth International Airport

et al. 2022

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Aircraft deicing/anti-icing fluids (ADFs) are applied to remove and prevent icing on aircraft during taxi and takeoff. The Dallas-Fort Worth (DFW) International Airport uses deicing pads for deicing activities that collect and contain the spent deicing fluids for proper treatment or disposal. Local waterways receive ADF as “drip and shear” during the aircraft taxi on the runway and then takeoff. The glycol-based ADF serves as a nutrient for bacteria that grow exponentially, deplete dissolved oxygen (DO) from receiving waterways, and subsequently kill aquatic life. In this paper, we present a data-driven discrete-event simulation modeling process developed in collaboration with DFW Airport to assess aircraft assignment strategies to deicing pad locations by monitoring impact on DO. Our process consists of the following phases: (1) Data Collection, (2) Probability Distribution Modeling, and (3) State Transition Modeling. Both Phases (2) and (3) utilized data mining approaches, including treed regression and variable selection via false discovery rate. Detailed implementation of these phases is described for the DFW Airport case study, and the DFW Airport deicing activities simulation tool framework is presented. The actual data and simulation results were compared in terms of the DO levels in airport receiving waterways to verify the model validity after implementing the proposed model for DFW. Thus, the proposed model can be implemented by airports to control and minimize the adverse environmental effects resulting from deicing activities by optimizing the aircraft assignment to the pad locations.

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Two-stage stochastic programming for interdisciplinary pain management

Cited by 3 publications

References 27 publications

Multi-Objective Two-Stage Stochastic Programming for Adaptive Interdisciplinary Pain Management with Piecewise Linear Network Transition Models

Multi-Objective Two-Stage Stochastic Programming for Adaptive Interdisciplinary Pain Management with Piecewise Linear Network Transition Models

An Optimization Framework to Study the Balance Between Expected Fatalities Due to COVID-19 and the Reopening of U.S. Communities

A discrete-event simulation tool for airport deicing activities: Dallas-Fort Worth International Airport

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