Model predictive control of glucose concentration in type I diabetic patients: An in silico trial (original) (raw)
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Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes
Physiological Measurement, 2004
A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L −1 per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is 0967-3334/04/040905+16$30.00 © 2004 IOP Publishing Ltd Printed in the UK 905 906 R Hovorka et al promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.
10th International Symposium on Computer Applications in Biotechnology (2007), 2007
Intra-and inter-patient variability poses a challenging task to control blood glucose concentration in diabetic patients. A data based model predictive control with state and disturbance estimation has been developed to control the blood glucose concentration in the type-I diabetic patients in the presence of meal disturbances under patient-model mismatch. Simulation studies were performed on three distinct patient models generated as a result of sensitivity analysis, which revealed that the proposed control strategy is able to control the blood glucose concentration well within the acceptable limits and also able to compensate for the slow parametric drifts.
Computer methods and programs in biomedicine, 2015
In this paper, the problem of predicting blood glucose concentrations (BG) for the treatment of patients with type 1 diabetes, is addressed. Predicting BG is of very high importance as most treatments, which consist in exogenous insulin injections, rely on the availability of BG predictions. Many models that can be used for predicting BG are available in the literature. However, it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements. The main contribution of this work is to propose a simple and identifiable linear dynamical model, which is based on the static prediction model of standard therapy. It is shown that the model parameters are intrinsically correlated with physician-set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities ...
Blood glucose control algorithms for type 1 diabetic patients: A methodological review
Biomedical Signal Processing and Control, 2013
A method for optimal continuous insulin therapy for diabetes patients has been sought since the early 1970s. Although technical and medical advances have been made, a fully automated artificial pancreas to replace the functions of the natural organ is still a research aim. This review compares recent control algorithms for type 1 diabetic patients which automatically connect continuous glucose monitoring and insulin injection, without patient intervention. Black-box model and gray-box model based control strategies are described and their performances are evaluated, with a focus on their feasibility of implementation in a real-life situation. In conclusion, a satisfactory control strategy has not yet been proposed, mainly because most control algorithms rely on continuous blood glucose measurement which is not yet available. Modeling the effect of glucose ingestion as an external disturbance on the time evolution of blood glucose concentration, is now the norm for the control community. In contrast, the effects of physical activity on the metabolic system is not yet fully understood and remain an open issue. Moreover, clinical studies on evaluation of control performance are scarce. Therefore, research on blood glucose control needs to concentrate on advanced patient modeling, control optimization and control performance evaluation under realistic patient-oriented conditions.
Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients
International Journal of Healthcare Information Systems and Informatics, 2021
Engineering smart software that can monitor, predict, and control blood glucose is critical to improving patients' quality of treatments with type 1 Diabetic Mellitus (T1DM). However, ensuring a reasonable glycemic level in diabetic patients is quite challenging, as many methods do not adequately capture the complexities involved in glycemic control. This problem introduces a new level of complexity and uncertainty to the patient's psychological state, thereby making this problem nonlinear and unobservable. In this paper, we formulated a mathematical model using carbohydrate counting, insulin requirements, and the Harris-Benedict energy equations to establish the framework for predicting and controlling blood glucose level regulation in T1DM. We implemented the framework and evaluated its performance using root mean square error (RMSE) and mean absolute error (MAE) on a case study. Our framework had less error rate in terms of RMSE and MAE, which indicates a better fit with ...
American Journal of Biomedical Sciences, 2013
In this paper, a comparison of two different approaches that can be used in developing time series mathematical models (MM) of diabetes mellitus was carried out. The trade-off should be considered between the complexity of the model and its accuracy to predict future glucose concentration. This work is a continuation of the author's work and results obtained previously and showed the potential and superiority of using autoregressive with exogenous terms (ARX) model in describing the dynamics of diabetes. Moreover, it is shown that despite the models are of general form but they are different depending on individuals' regimen of diabetes management. The last fact was demonstrated by using six diabetic patients' records, with rich information about their life style and treatment program, to derive models. In addition to that an answer is given to two main questions: how many future samples of glucose levels can be predicted with acceptable accuracy and what is the acceptable order of the model-complexity-, if the prediction horizon is specified. Both types of models were developed, tested and compared. This work emphasizes the fact that diabetes management plan should be formulated as an individualized therapeutic to achieve the desired level of diabetes control. This can be of help in improving the metabolic control of type-1diabetes patients by implementing these characteristics and models in both computerized controlled decision support system and simulation systems for educating and training of healthcare professional staff. Additionally, these MM of glucose-insulin interaction are expected to aid in reaching a generalized model.
Review and Analysis of Blood Glucose (BG) Models for Type 1 Diabetic Patients
Industrial & Engineering Chemistry Research, 2011
Blood glucose (BG) regulation in type 1 diabetic patients has been investigated by researchers for a long time. Many mathematical models mimicking the physiological behavior of diabetic patients have been developed to predict BG variations. Models characterizing meal absorption and physical activities have also been developed in the literature, as they play a significant role in altering BG levels. Hence, existing glucose-insulin dynamic models dating back from early 1960s are reviewed along with an overview of meal absorption and exercise effect models. The available knowledge-driven BG models have been classified into different families based on their origin for development. Also, five knowledge-driven BG models (with at least one model from a family) have been analyzed by either varying basal insulin or meal ingestion. The available meal absorption models have also been simulated to compare and analyze them for different meal sizes. The major objective of the analysis is to study the BG dynamics of different models at their nominal parameter values, under varying basal insulin doses and meal ingestion. Similar analysis has been performed on 10 adult patient models in a recent benchmark simulator for comparison. These results will be useful for understanding the responses of different BG models at their nominal parameter values and for preliminary selection of a suitable treatment model(s) for a patient.
Diabetes Technology & Therapeutics, 2007
Background: The development of an artificial pancreas requires an accurate representation of diabetes pathophysiology to create effective and safe control systems for automatic insulin infusion regulation. The aim of the present study is the assessment of a previously developed mathematical model of insulin and glucose metabolism in type 1 diabetes and the evaluation of its effectiveness for the development and testing of control algorithms. Methods: Based on the already existing "minimal model" a new mathematical model was developed composed of glucose and insulin submodels. The glucose model includes the representation of peripheral uptake, hepatic uptake and release, and renal clearance. The insulin model describes the kinetics of exogenous insulin injected either subcutaneously or intravenously. The estimation of insulin sensitivity allows the model to personalize parameters to each subject. Data sets from two different clinical trials were used here for model validation through simulation studies. The first set had subcutaneous insulin injection, while the second set had intravenous insulin injection. The root mean square error between simulated and real blood glucose profiles (G rms) and the Clarke error grid analysis were used to evaluate the system efficacy. Results: Results from our study demonstrated the model's capability in identifying individual characteristics even under different experimental conditions. This was reflected by an effective simulation as indicated by G rms , and clinical acceptability by the Clarke error grid analysis, in both clinical data series. Conclusions: Simulation results confirmed the capacity of the model to faithfully represent the glucose-insulin relationship in type 1 diabetes in different circumstances.