A physiological model of glucose-insulin interaction in type 1 diabetes mellitus

Lehmann, Eldon D.; Deutsch, Tibor

doi:10.1016/0141-5425(92)90058-s

Cited by 374 publications

(281 citation statements)

References 8 publications

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“…Most blood glucose models developed to date are mathematical formalisms of physiological processes. A well-known, freely available, model is AIDA 1 [12]. The authors note that the model is not powerful enough to simulate individual patients and restrict its use to education and demonstration.…”

Section: Related Workmentioning

confidence: 99%

Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression

Bunescu

Struble

Marling

et al. 2013

2013 12th International Conference on Machine Learning and Applications

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Abstract-Patients with diabetes must continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious short-term and long-term complications. An automatic prediction model that warned people of imminent changes in their blood glucose levels would enable them to take preventive action. Modeling inter-patient differences and the combined effects of insulin and life events on blood glucose have been particularly challenging in the design of accurate blood glucose forecasting systems. In this paper, we describe a solution that uses a generic physiological model of blood glucose dynamics to generate informative features for a Support Vector Regression model that is trained on patient specific data. Experimental results show that the new prediction model outperforms all three diabetes experts involved in the study, thus demonstrating the utility of using the generic physiological features in machine learning models that are individually trained for every patient.

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Section: Related Workmentioning

confidence: 99%

Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression

Bunescu

Struble

Marling

et al. 2013

2013 12th International Conference on Machine Learning and Applications

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“…The value of this compartment cannot be measured directly. Moreover, it can be considered as a slow variable (Lehmann and Deutsch (1992)). In this way, 0…”

Section: (T) Q(t) I(t))mentioning

confidence: 99%

Nonlinear Control Analysis of an ICU Model for Tight Glycaemic Control

Kovács

Szalay

Benyó

et al. 2011

IFAC Proceedings Volumes

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Abstract:Intensive care is one of the most challenging areas of modern medicine. Maintenance of glucose levels in intensive care unit (ICU) patients via control of insulin inputs is an active research field. Accurate metabolic system models are a critical element of automatic control. Different ICU models appeared in the literature some of them already validated in clinical trials. The current paper analyzes and gives a nonlinear synthesis of a frequently used ICU metabolic system model's redefined version. The model has been already validated in clinical trials. Global control characteristics are determined using nonlinear analysis. Results of reachability and observability are explained regarding physiological meanings, and then exact linearization is computed. Finally, quasi affine linear parameter varying (qALPV) modeling methodology is applied and compared with results obtained by exact linearization. It is demonstrated that inside the chosen scheduling parameters' vertex the qAPLV model represents the nonlinear system itself without any approximation. Conclusions are drawn from this analysis for further robust nonlinear model based controller design.

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“…The controller design is based on the glucose dynamic model of Guyton et al 17 and Lehmann and Deutsch, 21 modified by Gómez et al, 19 and on the insulin dynamic described by Berger and Rodbard 18 adapted to the lispro type insulin. 26 The glucose model 21 is a compartmental model whose parameters may be consulted in Table 1.…”

Section: The Glucoregulatory Modelmentioning

confidence: 99%

“…15 Control by inversion is a technique that uses an inversion model to obtain infusion doses and also requires the availability of a glucoregulatory patient model. Various models have been proposed in the literature, from which we single out those of Bergman, 16 Guyton et al, 17 Berger and Rodbard, 18 Gómez et al, 19 Salzsieder et al, 20 Lehmann and Deutsch, 21 Hovorka et al, 22 and Dalla Man et al 23 Although a glucoregulatory inversion model has previously been used to obtain an estimation of plasma insulin, 24 it has not been used in closed-loop control. The proposed IC has been tested in silico with a simulated patient population and a glucose sensor model.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Study of an Inverse Controller for Closed- and Semiclosed-Loop Control in Type 1 Diabetes

Rodríguez-Herrero

Pérez-Gandía

Rigla

et al. 2010

Diabetes Technology & Therapeutics

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Background:Closed-loop control algorithms in diabetes aim to calculate the optimum insulin delivery to maintain the patient in a normoglycemic state, taking the blood glucose level as the algorithm's main input. The major difficulties facing these algorithms when applied subcutaneously are insulin absorption time and delays in measurement of subcutaneous glucose with respect to the blood concentration. Methods: This article presents an inverse controller (IC) obtained by inversion of an existing mathematical model and validated with synthetic patients simulated with a different model and is compared with a proportionalintegral-derivative controller. Results: Simulated results are presented for a mean patient and for a population of six simulated patients. The IC performance is analyzed for both full closed-loop and semiclosed-loop control. The IC is tested when initialized with the heuristic optimal gain, and it is compared with the performance when the initial gain is deviated from the optimal one (±10%). Conclusions:The simulation results show the viability of using an IC for closed-loop diabetes control. The IC is able to achieve normoglycemia over long periods of time when the optimal gain is used (63% for the full closedloop control, and it is increased to 96% for the semiclosed-loop control).

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A physiological model of glucose-insulin interaction in type 1 diabetes mellitus

Cited by 374 publications

References 8 publications

Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression

Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression

Nonlinear Control Analysis of an ICU Model for Tight Glycaemic Control

A Simulation Study of an Inverse Controller for Closed- and Semiclosed-Loop Control in Type 1 Diabetes

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