390 research outputs found
Linear parameter-varying model to design control laws for an artificial pancreas
The contribution of this work is the generation of a control-oriented model for insulin-glucose dynamic regulation in type 1 diabetes mellitus (T1DM). The novelty of this model is that it includes the time-varying nature, and the inter-patient variability of the glucose-control problem. In addition, the model is well suited for well-known and standard controller synthesis procedures. The outcome is an average linear parameter-varying (LPV) model that captures the dynamics from the insulin delivery input to the glucose concentration output constructed based on the UVA/Padova metabolic simulator. Finally, a system-oriented reinterpretation of the classical ad-hoc 1800 rule is applied to adapt the model's gain. The effectiveness of this approach is quantified both in open- and closed-loop. The first one by computing the root mean square error (RMSE) between the glucose deviation predicted by the proposed model and the UVA/Padova one. The second measure is determined by using the ν-gap as a metric to determine distance, in terms of closed-loop performance, between both models. For comparison purposes, both open- (RMSE) and closed-loop (ν-gap metric) quality indicators are also computed for other control-oriented models previously presented. This model allows the design of LPV controllers in a straightforward way, considering its affine dependence on the time-varying parameter, which can be computed in real-time. Illustrative simulations are included. In addition, the presented modeling strategy was employed in the design of an artificial pancreas (AP) control law that successfully withstood rigorous testing using the UVA/Padova simulator, and that was subsequently deployed in a clinical trial campaign where five adults remained in closed-loop for 36 h. This was the first ever fully closed-loop clinical AP trial in Argentina, and the modeling strategy presented here is considered instrumental in resulting in a very successful clinical outcome.Fil: Colmegna, Patricio Hernán. Universidad Nacional de Quilmes. Departamento de Ciencia y TecnologĂa; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Sánchez Peña, Ricardo S.. Instituto TecnolĂłgico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Gondhalekar, R.. Harvard University; Estados Unido
Optimal design of feasible clinical tests for the identification of physiological models of type 1 diabetes mellitus
Questa tesi concerne la progettazione di test clinici per l'identificazione di modelli fisiologici del diabete mellito di tipo 1. La progettazione ottimale di esperimenti basata su modello si applica a tal proposito e la fattibilitĂ dei test ideati viene valutata tramite una serie di indici introdotti ad hoc. Il lavoro si conclude con l'impiego di tecniche di progettazione robusta che siano in grado di garantire al test i requisiti di applicabilitĂ clinica necessar
Robust Bayesian Satisficing
Distributional shifts pose a significant challenge to achieving robustness in
contemporary machine learning. To overcome this challenge, robust satisficing
(RS) seeks a robust solution to an unspecified distributional shift while
achieving a utility above a desired threshold. This paper focuses on the
problem of RS in contextual Bayesian optimization when there is a discrepancy
between the true and reference distributions of the context. We propose a novel
robust Bayesian satisficing algorithm called RoBOS for noisy black-box
optimization. Our algorithm guarantees sublinear lenient regret under certain
assumptions on the amount of distribution shift. In addition, we define a
weaker notion of regret called robust satisficing regret, in which our
algorithm achieves a sublinear upper bound independent of the amount of
distribution shift. To demonstrate the effectiveness of our method, we apply it
to various learning problems and compare it to other approaches, such as
distributionally robust optimization
A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System
In this paper, we build a new, simple, and interpretable mathematical model
to describe the human glucose-insulin system. Our ultimate goal is the robust
control of the blood glucose (BG) level of individuals to a desired healthy
range, by means of adjusting the amount of nutrition and/or external insulin
appropriately. By constructing a simple yet flexible model class, with
interpretable parameters, this general model can be specialized to work in
different settings, such as type 2 diabetes mellitus (T2DM) and intensive care
unit (ICU); different choices of appropriate model functions describing uptake
of nutrition and removal of glucose differentiate between the models. In both
cases, the available data is sparse and collected in clinical settings, major
factors that have constrained our model choice to the simple form adopted.
The model has the form of a linear stochastic differential equation (SDE) to
describe the evolution of the BG level. The model includes a term quantifying
glucose removal from the bloodstream through the regulation system of the human
body, and another two terms representing the effect of nutrition and externally
delivered insulin. The parameters entering the equation must be learned in a
patient-specific fashion, leading to personalized models. We present numerical
results on patient-specific parameter estimation and future BG level
forecasting in T2DM and ICU settings. The resulting model leads to the
prediction of the BG level as an expected value accompanied by a band around
this value which accounts for uncertainties in the prediction. Such
predictions, then, have the potential for use as part of control systems which
are robust to model imperfections and noisy data. Finally, a comparison of the
predictive capability of the model with two different models specifically built
for T2DM and ICU contexts is also performed.Comment: 47 pages, 9 figures, 7 table
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