Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

Sunil Deshpande; Naresh N. Nandola; Daniel Rivera; Jarred W. Younger

doi:10.1016/j.conengprac.2014.09.011

Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

Sunil Deshpande, Naresh N. Nandola, Daniel Rivera, Jarred W. Younger

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.

Original language	English (US)
Pages (from-to)	161-173
Number of pages	13
Journal	Control Engineering Practice
Volume	33
DOIs	https://doi.org/10.1016/j.conengprac.2014.09.011
State	Published - Dec 1 2014

Keywords

Biomedical applications
Fibromyalgia
Hybrid model predictive control
Optimized adaptive behavioral interventions
System identification

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

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title = "Optimized treatment of fibromyalgia using system identification and hybrid model predictive control",

abstract = "The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.",

keywords = "Biomedical applications, Fibromyalgia, Hybrid model predictive control, Optimized adaptive behavioral interventions, System identification",

author = "Sunil Deshpande and Nandola, {Naresh N.} and Daniel Rivera and Younger, {Jarred W.}",

note = "Funding Information: Support for this work has been provided by the Office of Behavioral and Social Sciences Research (OBSSR) of the National Institutes of Health (NIH) and the National Institute on Drug Abuse (NIDA) through grants R21 DA024266 and K25 DA021173 . The content is solely the responsibility of the authors and does not necessarily represent the official views of OBSSR, NIDA, or the NIH. Jarred Younger received support from the American Fibromyalgia Syndrome Association (AFSA) . Insights provided by Linda M. Collins and Jessica Trail of the Methodology Center, Penn State University during the conduct of this research are greatly appreciated.",

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doi = "10.1016/j.conengprac.2014.09.011",

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AU - Deshpande, Sunil

AU - Nandola, Naresh N.

AU - Rivera, Daniel

AU - Younger, Jarred W.

N1 - Funding Information: Support for this work has been provided by the Office of Behavioral and Social Sciences Research (OBSSR) of the National Institutes of Health (NIH) and the National Institute on Drug Abuse (NIDA) through grants R21 DA024266 and K25 DA021173 . The content is solely the responsibility of the authors and does not necessarily represent the official views of OBSSR, NIDA, or the NIH. Jarred Younger received support from the American Fibromyalgia Syndrome Association (AFSA) . Insights provided by Linda M. Collins and Jessica Trail of the Methodology Center, Penn State University during the conduct of this research are greatly appreciated.

PY - 2014/12/1

Y1 - 2014/12/1

N2 - The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.

AB - The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.

KW - Biomedical applications

KW - Fibromyalgia

KW - Hybrid model predictive control

KW - Optimized adaptive behavioral interventions

KW - System identification

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Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

Abstract

Keywords

ASJC Scopus subject areas

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