A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

Zafra-Cabeza, Ascensión; Rivera, Daniel E.; Ridao, Miguel A.; Camacho, Eduardo F.

doi:10.1109/tcst.2010.2052256

Cited by 17 publications

(11 citation statements)

References 32 publications

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“…In this paper, we describe how control systems engineering [4] offers a novel and potentially powerful framework for optimizing the effectiveness of broad classes of adaptive behavioral interventions. Specifically, the development of decision policies from control engineering coupled with technological enhancements in information and computer technology can result in adaptive interventions that minimize waste, increase compliance, and enhance overall intervention potency [5][6][7][8][9]. By relying on repeated assessments of participant response and model-based operationalized treatment decisions, it is possible to ascertain the optimal treatment regimen for an individual participant with less active Research: Dynamical systems and control engineering provide a powerful, broad-based methodological framework for modeling and decisionmaking in behavioral settings that can serve to benefit modern time-varying, adaptive interventions.…”

Section: Introductionmentioning

confidence: 99%

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

Deshpande

Rivera

Younger

et al. 2014

Behav. Med. Pract. Policy Res.

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The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

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Section: Introductionmentioning

confidence: 99%

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

Deshpande

Rivera

Younger

et al. 2014

Behav. Med. Pract. Policy Res.

Self Cite

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“…These studies employ both time-invariant and time-varying models, depending on the specific problem. Moreover, some of the work in the literature also uses controller design methods to design adaptive intervention for special problems in behavioral science [12], [13]. Most of these use model predictive controller (MPC) approach.…”

Section: A Previous Workmentioning

confidence: 99%

A robust MPC approach to the design of behavioural treatments

Bekiroğlu

Lagoa

Murphy

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-The objective of this paper is to provide some initial results on the application of control tools to the problem treatment design. Human behavior and reaction to treatment is complex and dependent on many unmeasurable external stimuli. Therefore, to the best of our knowledge, it cannot be described by simple models. Hence, one of the main messages in this paper is that, to design a treatment (controller) one cannot rely on exact models. More precisely, to be able to design effective treatments, we propose to use "simple" uncertain affine models whose response "covers" the most probable subject responses. So, we propose a simple model that contains two different types of uncertainties: one aimed at uncertainty of the dynamics and another aimed at approximating external perturbations that patients face in their daily life. With this model at hand, we design a robust model predictive controller, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms. I. INTRODUCTIONAlthough, the use of control algorithms in treatment context is not new there is still limited use of the concept of feedback in the design of treatments especially in the area of behavioral sciences. In this paper, we present a preliminary attempt to apply some well-known concepts from robust controller design to the development of efficient and robust intervention for behavioural treatments.In the design of treatments in a behavioral context, one usually starts with data from several patients collected in a study. From this, one determines an approximate behavioral model to be used in the design of the control law. The first one is both variability in behavior and the way patients respond to treatment in study data leads to high inaccuracy of the models obtained. Moreover, in a practical setting a patient's behavior is always affected by external perturbations from events in daily life. All this leads to a large amount of uncertainty in the model and, hence, the need for feedback and, especially, robustness. Therefore, given the fact that one can increase the robustness of the overall treatment by using a control approach, the interest in using algorithms from control theory in a treatment context is growing. Moreover, the increasing use of portable devices enables the collection of information and delivery of the treatment in a timely manner and the possibility of automating treatments.

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“…Among these lie adaptive interventions, which consider adjusting treatment dosages over time based on participant response. Control engineering offers a broad-based solution framework for optimizing the effectiveness of such interventions and has been proposed as an enabler for more efficacious treatments that minimize waste, increase compliance, and enhance the intervention potency (Rivera et al, 2007; Zafra-Cabeza et al, 2011; Riley et al, 2011; Deshpande et al, 2014). …”

Section: Introductionmentioning

confidence: 99%

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

Deshpande

Nandola

Rivera

et al. 2014

Control Engineering Practice

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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.

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A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

Cited by 17 publications

References 32 publications

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

A robust MPC approach to the design of behavioural treatments

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

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