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

Deshpande, Sunil; Nandola, Naresh N.; Rivera, Daniel E.; Younger, Jarred

doi:10.1016/j.conengprac.2014.09.011

Cited by 21 publications

(13 citation statements)

References 34 publications

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“…A critical tradeoff must be considered when choosing model structure: maximize flexibility to best capture system dynamics while avoiding the need to have more model parameters than can be reliably identified from the data 32 . Autoregressive models with exogenous inputs (ARX) models are frequently used for black-box system identification because they can capture underlying system dynamics in diverse applications and because parameterization using the ARX structure guarantees uniqueness of solution and identification of the global minimum of the error function 30,[33][34][35][36] .…”

Section: Auto-regressive Model With Exogenous Inputs Fits Inos Dynamimentioning

confidence: 99%

Experimental control of macrophage pro-inflammatory dynamics using predictive models

Weinstock

Forsmo

Wilkinson

et al. 2019

Preprint

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Macrophage activity is a major component of the healthy response to infection and injury that consists of tightly regulated early pro-inflammatory activation followed by anti-inflammatory and regenerative activity. In numerous diseases, however, macrophage polarization becomes dysregulated and can not only impair recovery, but can also promote further injury and pathogenesis, e.g. after injury, in diabetes, or in Alzheimer's disease. Dysregulated macrophages may either fail to polarize or become chronically polarized, resulting in increased production of cytotoxic factors, diminished capacity to clear pathogens, or failure to promote tissue regeneration.In these cases, a method of predicting and dynamically controlling macrophage polarization will enable a new strategy for treating diverse inflammatory diseases. In this work, we developed a model-predictive control framework to temporally regulate macrophage polarization. Using RAW 264.7 macrophages as a model system, we enabled temporal control by identifying transfer function models relating the polarization marker iNOS to exogenous pro-and anti-inflammatory stimuli. These stimuli-to-iNOS response models were identified using linear autoregressive with exogenous input terms (ARX) equations and were coupled with nonlinear elements to account for experimentally identified supra-additive and hysteretic effects. Using this model architecture, we were able to reproduce experimentally observed temporal iNOS dynamics induced with lipopolysaccharides (LPS) and interferon gamma (IFN-γ). Moreover, the identified model enabled the design of time-varying input trajectories to experimentally sustain the duration and magnitude of iNOS expression. By designing transfer function models with the intent to predict cell behavior, we were able to predict and experimentally obtain temporal regulation of iNOS expression using LPS and IFN-γ from both naïve and non-naïve initial states. Moreover, our data driven models revealed decaying magnitude of iNOS response to LPS stimulation over time that could be recovered using combined treatment with both LPS and IFN-. Given the importance of dynamic tissue macrophage polarization and overall inflammatory regulation to a broad number of diseases, the temporal control methodology presented here will have numerous applications for regulating immune activity dynamics in chronic inflammatory diseases.3

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Section: Auto-regressive Model With Exogenous Inputs Fits Inos Dynamimentioning

confidence: 99%

Experimental control of macrophage pro-inflammatory dynamics using predictive models

Weinstock

Forsmo

Wilkinson

et al. 2019

Preprint

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“…Early versions of their dynamic behaviour model are based on the theory of planned behaviour (TBP) [34], with later versions based on SCT [30]. Their models have been applied to smoking cessation [55], fibromyalgia pain [10], weight gain [13,34], and to physical activity [43]. Thus far the examples have been quite simple with inputs and outputs that can easily be quantified such as the number of steps walked [43].…”

Section: State Of the Artmentioning

confidence: 99%

Persuasive Health

Wilson

Helal

Bull

et al. 2019

Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare

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With individual behaviour and lifestyle determining 30-50% of people's health, research and supportive technology for affecting behaviour alteration remain urgently needed. Most existing persuasive systems are designed to persuade a user to change a finite set of behaviours to achieve a specific goal. However, if the user's situation or goal changes, such systems cannot adapt to the changes. A much more robust type of persuasive system is needed today to enable adequate health navigation and to empower people to face and change their own realities in terms of a large variety of health behaviours and lifestyles. In this paper, we provide a perspective on the impressive body of work contributed over the past 15 years, to better look into the future of persuasive health and to the opportunities a broader theoretical framework and practical methodologies may bring about. We present a taxonomy that attempts to explain the contributions in this field including health behaviour theory, cybernetic actionbehaviour models, social cognitive theory, and control theory. We identify potentially promising approaches to advance persuasive health's efficacy in empowering individuals to improve their own health outcomes.

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“…The ARX system identification approach has been used for developing a dynamical model for assigning naltrexone dosages as a fibromyalgia treatment based on clinical data [25]. Discrete time modeling using an ARX model with multiple inputs has been used where the clinical data are fitted to a parametric multi-input of the model.…”

Section: Fmentioning

confidence: 99%

ARX System Identification in Biomedical Applications

2019

IJITEE

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System identification approach is a data driven establishing mathematical model of the system that has been widely applied to the astronomy, automatic control, spaceflight, aviation, economics as well as marine ecology and society. At present, system identification has been widely used in the field of biomedical engineering. The status of system identification technique becomes increasingly important with the rapid development of science and technology in various disciplines. This paper is firstly introduced both linear and nonlinear system identification, then briefly explained the Autoregressive Exogenous (ARX) approach, and finally the applications based on ARX system identification in the biomedical engineering field have been presented.

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

Cited by 21 publications

References 34 publications

Experimental control of macrophage pro-inflammatory dynamics using predictive models

Experimental control of macrophage pro-inflammatory dynamics using predictive models

Persuasive Health

ARX System Identification in Biomedical Applications

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