Multi-objective blood glucose control for type 1 diabetes

Dua, Pinky; Doyle, Francis J.; Pistikopoulos, Efstratios N.

doi:10.1007/s11517-009-0453-0

Cited by 33 publications

(22 citation statements)

References 32 publications

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“…The on-line computational requirements of MPC can be reduced to simple function evaluations by designing multiparametric controllers [3,6,7,[21][22][23][24][25]. Multi-parametric controllers provide control variables as a set of explicit functions of the state variables, and the polyhedral regions in the space of the state variables where these functions are valid leading to much simpler implementation of the controller.…”

Section: Model-based Controlmentioning

confidence: 99%

Modelling and multi-parametric control for delivery of anaesthetic agents

Dua

Pistikopoulos

2010

Med Biol Eng Comput

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This article presents model predictive controllers (MPCs) and multi-parametric model-based controllers for delivery of anaesthetic agents. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. The multi-parametric controller has all the advantages of the MPC and does not require repetitive solution of optimization problem for its implementation. This is achieved by obtaining the optimal drug delivery rates as a set of explicit functions of the state of the patient. The derivation of the controllers relies on using detailed models of the system. A compartmental model for the delivery of three drugs for anaesthesia is developed. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This is achieved by using three drugs: dopamine (DP), sodium nitroprusside (SNP) and isoflurane. A number of dynamic simulation experiments are carried out for the validation of the model. The model is then used for the design of model predictive and multi-parametric controllers, and the performance of the controllers is analyzed.

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Section: Model-based Controlmentioning

confidence: 99%

Modelling and multi-parametric control for delivery of anaesthetic agents

Dua

Pistikopoulos

2010

Med Biol Eng Comput

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“…These advantages of MPC over other control strategies have promoted the use of MPC in the field of insulin delivery. Different MPC schemes are being used in artificial pancreas research, where the applicability of such control strategy has been demonstrated in in-silico studies (see for instance (Abu-Rmileh et al, 2010a;Dua et al, 2009;Grosman et al, 2010;Hovorka et al, 2004;Magni et al, 2007;Parker et al, 1999)), and clinical trials as mentioned earlier.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Hypoglycemia Prevention in Closed-Loop Artificial Pancreas for Patients with Type 1 Diabetes

Abu-Rmileh¹,

Garcia-Gabin²

2011

Diabetes - Damages and Treatments

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“…This work belongs to an ongoing effort developing building blocks for modeling and optimizing biomedical systems [12] such as that for Acute Myeloid Leukemia [13,14], Type 1 Diabetes Mellitus [15] and for the delivery of anesthesia [16,17]. We previously performed a more limited global sensi- [8]- [10,19,20] Fig.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia

Savvopoulos

Misener

Panoskaltsis

et al. 2016

IEEE Trans. Biomed. Eng.

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Abstract-Chronic Lymphocytic Leukemia (CLL) is the most common peripheral blood and bone marrow cancer in the developed world. This manuscript proposes mathematical model equations representing the disease dynamics of B-cell CLL. We interconnect delay differential cell cycle models in each of the tumor-involved disease centers using physiologically-relevant cell migration. We further introduce 5 hypothetical case studies representing CLL heterogeneity commonly seen in clinical practice and demonstrate how the proposed CLL model framework may capture disease pathophysiology across patient types. We conclude by exploring the capacity of the proposed temporally-and spatially-distributed model to capture the heterogeneity of CLL disease progression. By using Global Sensitivity Analysis, the critical parameters influencing disease trajectory over space and time are: (i) the initial number of CLL cells in peripheral blood, the number of involved lymph nodes, the presence and degree of splenomegaly; (ii) the migratory fraction of nonproliferating as well as proliferating CLL cells from bone marrow into blood and of proliferating CLL cells from blood into lymph nodes; (iii) the parameters inducing nonproliferative cells to proliferate. The proposed model offers a practical platform which may be explored in future personalized patient protocols once validated.

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Multi-objective blood glucose control for type 1 diabetes

Cited by 33 publications

References 32 publications

Modelling and multi-parametric control for delivery of anaesthetic agents

Modelling and multi-parametric control for delivery of anaesthetic agents

Hypoglycemia Prevention in Closed-Loop Artificial Pancreas for Patients with Type 1 Diabetes

A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia

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