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

被引：18

作者：

Deshpande, Sunil ^{[1
]}

Nandola, Naresh N. ^{[1
]}

Rivera, Daniel E. ^{[1
]}

Younger, Jarred W. ^{[2
]}

机构：

[1] Arizona State Univ, Sch Engn Matter Transport & Energy, Control Syst Engn Lab, Tempe, AZ 85287 USA

[2] Univ Alabama Birmingham, Dept Psychol, Birmingham, AL 35294 USA

来源：

CONTROL ENGINEERING PRACTICE | 2014年 / 33卷

基金：

美国国家卫生研究院;

关键词：

Optimized adaptive behavioral interventions; Fibromyalgia; System identification; Hybrid model predictive control; Biomedical applications; LOW-DOSE NALTREXONE; ADAPTIVE INTERVENTIONS; CRITERIA; DESIGN;

D O I：

10.1016/j.conengprac.2014.09.011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：161 / 173

页数：13

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