A Gain-Scheduling Model Predictive Controller for Blood Glucose Control in Type 1 Diabetes

Abu-Rmileh, Amjad; Garcia-Gabin, Winston

doi:10.1109/tbme.2009.2033663

Cited by 29 publications

(14 citation statements)

References 27 publications

(30 reference statements)

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“…Also, compared with the performance of MPC using the linearized Dalla Man model, multilevel MPC achieves significantly better performance, in particular, avoiding the hypoglycemic and hyperglycemic episodes found by Abu-Rmileh and Garcia-Gabin. 18 The performance of the two MPCs were also analyzed using the time percentage within ranges metric. This metric gives the percentage of the testing period time during which the patient's blood glucose is within the acceptable (70-180 mg/dl), hypoglycemic (<70 mg/dl), and hyperglycemic (>180 mg/dl) ranges.…”

Section: Resultsmentioning

confidence: 99%

Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

Garcia-Gabin

Jacobsen

2013

J Diabetes Sci Technol

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Multilevel models open new possibilities for designing glucose control algorithms. They allow controllers to take into account variables that have a strong influence on glucose homeostasis. A model-based controller was used for demonstrating how improved knowledge of the multilevel nature of diabetes increases the robustness and performance of glucose control algorithms. Using the proposed multi-level approach, a reduction of the hypoglycemic risk and robust behaviour for intrapatient variability was demonstrated.

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Section: Resultsmentioning

confidence: 99%

Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

Garcia-Gabin

Jacobsen

2013

J Diabetes Sci Technol

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“…Therefore, the glucose controller should be able to provide personalized and adaptive treatment recommendations. The majority of approaches applied toward the development of glucose controllers [6] are based on either proportional integral derivative (PID) control [84], [85] or model predictive control (MPC) [86] [87] [88] [89] [90] [91] [92] [93] [94] [95][96]. MPC-based glucose controllers have gained wider acceptance due to the MPC’s ability to handle 1) high nonlinearities in glucose–insulin metabolism, caused by saturation and inhibition effects evidenced by chemical substrates and hormones involved in enzyme dynamics and hormonal control effects, 2) time delays in subcutaneous–subcutaneous (sc-sc) route due to the delayed effect of infused subcutaneous insulin and the glucose diffusion from the blood to the subcutaneous space, and 3) inaccuracies in subcutaneous glucose measurements.…”

Section: Section Iiicdss For Diabetes Managementmentioning

confidence: 99%

A Review of Emerging Technologies for the Management of Diabetes Mellitus

Zarkogianni

Litsa

Mitsis

et al. 2015

IEEE Trans. Biomed. Eng.

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Objective High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. Methods A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. Results Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. Conclusion Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. Significance The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

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“…30,31 It has 3 compartments and the input is delivered at compt. 1, then it follows either (1) 3 5 = , while in model 3a this index is estimated with the other parameters.…”

Section: Model Representationsmentioning

confidence: 99%

Pharmacokinetic Model of the Transport of Fast-Acting Insulin From the Subcutaneous and Intradermal Spaces to Blood

Kulkarni

Chan

et al. 2015

J Diabetes Sci Technol

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Pharmacokinetic (PK) models describing the transport of insulin from the injection site to blood assist clinical decision making and are part of in silico platforms for developing and testing of insulin delivery strategies for treatment of patients with diabetes. The ability of these models to accurately describe all facets of the in vivo insulin transport is therefore critical for their application. Here, we propose a new model of fast-acting insulin analogs transport from the subcutaneous and intradermal spaces to blood that can accommodate clinically observed biphasic appearance and delayed clearance of injected insulin, 2 phenomena that are not captured by existing PK models. To develop the model we compare 9 insulin transport PK models which describe hypothetical insulin delivery pathways potentially capable of approximating biphasic appearance of exogenous insulin. The models are tested with respect to their ability to describe clinical data from 10 healthy volunteers which received 1 subcutaneous and 2 intradermal insulin injections on 3 different occasions. The optimal model, selected based on information and posterior identifiability criteria, assumes that insulin is delivered at the administrative site and is then transported to the bloodstream via 2 independent routes (1) diffusion-like process to the blood and (2) combination of diffusion-like processes followed by an additional compartment before entering the blood. This optimal model accounts for biphasic appearance and delayed clearance of exogenous insulin. It agrees better with the clinical data as compared to commonly used models and is expected to improve the in silico development and testing of insulin treatment strategies, including artificial pancreas systems.

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A Gain-Scheduling Model Predictive Controller for Blood Glucose Control in Type 1 Diabetes

Cited by 29 publications

References 27 publications

Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

A Review of Emerging Technologies for the Management of Diabetes Mellitus

Pharmacokinetic Model of the Transport of Fast-Acting Insulin From the Subcutaneous and Intradermal Spaces to Blood

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