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Along with the developing technology, obesity and diabetes are increasing rapidly among people. The identification of diabetes diseases, modeling, predicting their behavior, conducting simulations, studying control and treatment methods using mathematical methods has become of great importance. In this paper, we have obtained numerical solutions by considering the glucose-insulin fractional model. This model consists of three compartments: the blood glucose concentration (G), the blood insulin concentration (I) and the ready-to-absorb glucose concentration (D) in the small intestine. The fractional derivative is used in the sense of Caputo. By performing mathematical analyzes for the Glucose-Insulin fractional mathematical model, numerical results were obtained with the help of the Euler method and graphs were drawn. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F 1 , and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place. [ABSTRACT FROM AUTHOR]

Improving the prediction of blood glucose concentration may improve the quality of life of people living with type 1 diabetes by enabling them to better manage their care. Given the anticipated benefits of such a prediction, numerous methods have been proposed. Rather than attempting to predict glucose concentration, a deep learning framework for prediction is proposed in which prediction is performed using a scale for hypo- and hyper-glycemia risk. Using the blood glucose risk score formula proposed by Kovatchev et al., models with different architectures were trained, including, a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short-term memory (LSTM) network, and an encoder-like convolutional neural network (CNN). The models were trained using the OpenAPS Data Commons data set, comprising 139 individuals, each with tens of thousands of continuous glucose monitor (CGM) data points. The training set was composed of 7% of the data set, while the remaining was used for testing. Performance comparisons between the different architectures are presented and discussed. To evaluate these predictions, performance results are compared with the last measurement (LM) prediction, through a sample-and-hold approach continuing the last known measurement forward. The results obtained are competitive when compared to other deep learning methods. A root mean squared error (RMSE) of 16 mg/dL, 24 mg/dL, and 37 mg/dL were obtained for CNN prediction horizons of 15, 30, and 60 min, respectively. However, no significant improvements were found for the deep learning models compared to LM prediction. Performance was found to be highly dependent on architecture and the prediction horizon. Lastly, a metric to assess model performance by weighing each prediction point error with the corresponding blood glucose risk score is proposed. Two main conclusions are drawn. Firstly, going forward, there is a need to benchmark model performance using LM prediction to enable the comparison between results obtained from different data sets. Secondly, model-agnostic data-driven deep learning models may only be meaningful when combined with mechanistic physiological models; here, it is argued that neural ordinary differential equations may combine the best of both approaches. These findings are based on the OpenAPS Data Commons data set and are to be validated in other independent data sets. [ABSTRACT FROM AUTHOR]

In silico simulation studies using 5807 virtual patients with insulin dependent diabetes have been conducted to estimate the risk and efficacy with the closed-loop 780G pump when switching between Medtronic Guardian Sensor 3 (GS3) and Medtronic Guardian 4 Sensor (G4S), next generation calibration free glucose sensor. To demonstrate by utilizing a case study that captures the merits of in silico studies with single hormone insulin dependent virtual patients that include variability in pharmacokinetics/pharmacodynamics, age, gender, insulin sensitivity and BMIs. Also, to show that in silico studies can uniquely isolate the effect of a single variable on clinical outcomes. Simulation studies results were compared to clinical and commercial data and were separated by age groups and pump settings. The commercial data, the clinical study data and the simulation studies predicted that switching between GS3 to G4S will introduce a change in glucose average, percentage time between 70 and 180 mg/dL, and percentage time below 70 mg/dL of: 5.2, 3.4, and 3.1 mg/dL, − 1.1, 0.2, and − 1.1%, and − 0.6, − 1.0, and − 0.3%, respectively. We demonstrated that our simulation studies were able to predict the difference in glycemic outcomes when switching between different sensors in real world setting, better than a small clinical controlled study. As predicted, switching between GS3 and G4S sensors with the 780G system does not introduce clinical risk and maintain the clinical outcomes of the sensor. We demonstrated the ability of insulin dependent diabetes virtual patients to predict clinical outcomes and to augment or even replace some small clinical studies. [ABSTRACT FROM AUTHOR]

The article presents the discussion on inaugural guideline on diabetes technology being published. Topics include created a means to utilize technology without requiring on body devices though studies in the pediatric population; and clinical trials and real‐world data demonstrated improvements in glycemia with use of automated insulin delivery (AID).

Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-based glucose predictive models. We present the accuracy and clinical acceptability of nine different models coming from the literature, from standard autoregressive to more complex neural network-based models. These results are obtained on two different datasets, namely UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and Ohio Type-1 Diabetes Mellitus (OhioT1DM), featuring artificial and real type 1 diabetic patients respectively. By providing extensive details about the data flow as well as by providing the whole source code of the benchmarking process, we ensure the reproducibility of the results and the usability of the benchmark by the community. Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment. [ABSTRACT FROM AUTHOR]

This paper presents an identifier based intelligent adaptive fuzzy control scheme with regulating blood glucose concentration in normoglycemic level of 70 mg/dl for type 1 diabetic patients. The identifier is built with fuzzy neural network (FNN) to predict the blood glucose concentration of the diabetic patient. The fuzzy based controller with generic operating regimes which cluster all the adaptive control rules is designed to robustly reject the multiple meal disturbances resulting from food intake and deal with the parametric uncertainties in model and measurement noise. All the parameters of the FNN and of the fuzzy logic system are tuned by backpropagation (BP), to achieve the control objectives. The numerical simulations are performed to show that the set point tracking, meal disturbances and measurement noise rejection can be realized within this method. [ABSTRACT FROM AUTHOR]

Introduction: Type 1 diabetes is characterised by destruction of pancreatic beta cells, leading to insulin deficiency and hyperglycaemia. The mainstay of treatment remains lifelong insulin therapy as a sustainable cure has as yet proven elusive. The burden of daily management of type 1 diabetes has contributed to suboptimal outcomes for people living with the condition. Innovative technological approaches have been shown to improve glycaemic and patient-related outcomes. Areas covered: We discuss recent advances in technologies in type 1 diabetes including closed-loop systems, also known as the 'artificial pancreas. Its various components, technical aspects and limitations are reviewed. We also discuss its advent into clinical practice, and other systems in development. Evidence from clinical studies are summarised. Expert commentary: The recent approval of a hybrid closed-loop system for clinical use highlights the significant progress made in this field. Results from clinical studies have shown safety and glycaemic benefit, however challenges remain around improving performance and acceptability. More data is required to establish long-term clinical efficacy and cost-effectiveness, to fulfil the expectations of people with type 1 diabetes. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a methodology for the estimation of glucose masses in stomach (both in solid and liquid forms), intestine, plasma and tissue; insulin masses in portal vein, liver, plasma and interstitial fluid using only plasma glucose measurement. The proposed methodology fuses glucose-insulin homoeostasis model (in the presence of meal intake) and plasma glucose measurement with a Bayesian non-linear filter. Uncertainty of the model over individual variations has been incorporated by adding process noise to the homoeostasis model. The estimation is carried out over 24 h for the healthy people as well as a type II diabetes mellitus patients. In simulation, the estimator follows the truth accurately for both the cases. Moreover, the performances of two non-linear filters, namely the unscented Kalman filter (KF) and cubature quadrature KF are compared in terms of root mean square error. The proposed methodology will be helpful in future to: (i) observe a patient's insulin-glucose profile, (ii) calculate drug dose for any hyperglycaemic patients and (iii) develop a closed-loop controller for automated insulin delivery system. [ABSTRACT FROM AUTHOR]

Type 1 diabetes is an autoimmune condition characterised by a pancreatic insulin secretion deficit, resulting in high blood glucose concentrations, which can lead to micro- and macrovascular complications. Type 1 diabetes also leads to impaired glucagon production by the pancreatic α-cells, which acts as a counter-regulatory hormone to insulin. A closed-loop system for automatic insulin and glucagon delivery, also referred to as an artificial pancreas, has the potential to reduce the self-management burden of type 1 diabetes and reduce the risk of hypo- and hyperglycemia. To date, bihormonal closed-loop systems for glucagon and insulin delivery have been based on two independent controllers. However, in physiology, the secretion of insulin and glucagon in the body is closely interconnected by paracrine and endocrine associations. In this work, we present a novel biologically-inspired glucose control strategy that accounts for such coordination. An in silico study using an FDA-accepted type 1 simulator was performed to evaluate the proposed coordinated control strategy compared to its non-coordinated counterpart, as well as an insulin-only version of the controller. The proposed coordinated strategy achieves a reduction of hyperglycemia without increasing hypoglycemia, when compared to its non-coordinated counterpart. [ABSTRACT FROM PUBLISHER]

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OBJECTIVE Closed-loop insulin delivery offers a promising treatment option, but to date, it has only been evaluated in type 1 diabetes. Our aim was to evaluate the feasibility of fully closed-loop subcutaneous insulin delivery in insulin-naïve patients with type 2 diabetes. RESEARCH DESIGN AND METHODS Twelve subjects (seven males, age 57.2 years, BMI 30.5 kg/m²) with noninsulin-treated type 2 diabetes (HbA1c 8.4% [68 mmol/mol], diabetes duration 7.6 years) underwent two 24-h visits (closed-loop and control) in a randomized crossover design. During closed-loop visits, the subjects' routine diabetes therapy was replaced with model predictive control algorithm-driven subcutaneous insulin pump delivery based on real-time continuous glucose monitoring. Meals were unannounced, and no additional insulin was administered for carbohydrates consumed. During control visits, the usual diabetes regimen was continued (metformin 92%, sulfonylureas 58%, dipeptidyl peptidase-4 inhibitors 33%). On both visits, subjects consumed matched 50- to 80-g carbohydrate meals and optional 15-g carbohydrate snacks and remained largely sedentary. Plasma glucose measurements evaluated closed-loop performance. RESULTS Compared with conventional therapy, 24 h of closed-loop insulin delivery increased overall the median time in target plasma glucose (3.9-8.0 mmol/L) from 24 to 40% (P = 0.016), despite sensor under-reading by a median of 1.2 mmol/L. The benefit of the closed-loop system was more prominent overnight, with greater time in target glucose (median 78 vs. 35%; P = 0.041) and less time in hyperglycemia (22 vs. 65%; P = 0.041). There was no hypoglycemia during either intervention. CONCLUSIONS A closed-loop system without meal announcement and using subcutaneous insulin delivery in insulin-naïve patients with type 2 diabetes appears feasible and safe. Improvement in postprandial glucose control may require further optimization of system performance. [ABSTRACT FROM AUTHOR]

This paper deals with subcutaneous blood glucose level control. Inverse optimal trajectory tracking for discrete time non-linear positive systems is applied. The scheme is developed for MIMO (multi-input, multi-output) affine systems. The control law calculates the subcutaneous insulin delivery rate in order to prevent hyperglycemia and hypoglycemia events. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an affine form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Then this model is used to synthesize an inverse optimal controller in order to regulate the subcutaneous blood glucose level for a Type 1 Diabetes Mellitus patient the applicability of the proposed scheme is illustrated via simulation using a recurrent neural network in order to model the insulin-glucose dynamics. [ABSTRACT FROM AUTHOR]

The author comments on an article by Breton et al. on artificial pancreas in patients with type 1 diabetes. According to the author, ideal control by an artificial pancreas would therefore encompass autonomous control with normalization both of glycemia and of the glucose fluxes that contribute to it under all physiological circumstances. The author says that it would reduce the complications that arise form their dysregulation.

SUMMARY An optimal state feedback controller is designed with the objective of minimizing the elevated glucose levels caused by meal intake in Type 1 diabetic subjects, by the minimal infusion of insulin. The states for the controller based on linear quadratic regulator theory are estimated from noisy data using Kalman filter. The controller designed for a physiological relevant mathematical model is coupled with another model for simulating meal dynamics, which converts meal intake into glucose appearance rate in the plasma. The tuning parameters (weighting matrices) of the controller and the design parameters (noise covariance matrices) of the Kalman filter are optimized using genetic algorithm. The controller based on the combined framework of evolutionary computing and state estimated linear quadratic regulator is found to maintain normoglycemia for meal intakes of varying carbohydrate content. The proposed approach addresses noisy output measurement, modeling error and delay in sensor measurement. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents study that proposed a novel blend of model predictive control (MPC) and iterative learning control (ILC), also called learning-type MPC (L-MPC), for closed-loop control in an artificial pancreatic beta-cell. The proposed approach is initially tested on the Adult Average subject. The study showed that the blood glucose concentrations, after 20 days, can be maintained within 68-145 milligrams per decilitre (mg/dl) when the means are repetitive. L-MPC can generate superior control performance compared with that realized under MPC.

Diabet. Med. 27, 480–484 (2010) Aims We assessed an extended interruption of subcutaneous insulin delivery during overnight closed-loop glucose control in children and adolescents with Type 1 diabetes (T1D). Methods In seven young subjects with T1D [age 14.2 ± 2.1 years, diabetes duration 6.9 ± 4.0 years, glycated haemoglobin (HbA1c) 8.0 ± 1.5%, body mass index (BMI) 21.4 ± 4.0 kg/m2, total daily insulin dose 0.9 ± 0.2 units/kg/day; mean ± sd) participating in overnight closed-loop glucose control studies, insulin delivery was interrupted for at least 90 min on the basis of predicted hypoglycaemia, low prevailing glucose levels or a too-steep decline in glucose levels. Results Insulin delivery was interrupted for 165 (105, 210) min [median, interquartile range (IQR)]. Plasma glucose was 6.2 ± 3.2 mmol/l at the time of interruption and 5.5 ± 2.0 mmol/l 105 min later ( P = 0.15, paired t-test). Plasma glucose declined during the first hour of the interruption at a rate of 0.02 ± 0.03 mmol/l per min and reached a nadir of 5.2 ± 2.7 mmol/l; 105 min after the interruption, plasma glucose was increasing at a rate of 0.01 ± 0.03 mmol/l per min. When insulin delivery restarted, plasma glucose was 6.4 ± 2.2 mmol/l and peaked at 7.9 ± 2.1 mmol/l in 60 min ( P = 0.01). Physiological levels of plasma insulin were measured throughout with a nadir of 119 ± 78 pmol/l. Conclusions A prolonged interruption of insulin delivery during overnight closed-loop glucose control to prevent hypoglycaemia was not associated with an increased risk of hyperglycaemia in young people with T1D. [ABSTRACT FROM AUTHOR]

Intensive insulin therapy aimed at achieving normal glucose levels significantly reduces the complications that are associated with diabetes but is also associated with an increased risk of low glucose levels (hypoglycemia). The growing use of continuous glucose monitors has stimulated the development of the artificial pancreas, a closed-loop insulin-delivery system aimed at restoring near-normal glucose levels while reducing the risk of hypoglycemia. The artificial pancreas comprises three components: a continuous glucose monitor, an insulin infusion pump and a control algorithm delivering insulin according to real-time glucose readings. In this article, we review closed-loop glucose control, including its components, development, testing and clinical application. INSET: Key issues. [ABSTRACT FROM AUTHOR]

The article presents a study examining the influence of input excitation upon the quality of empirical dynamic models used for the prediction of blood glucose trends in type 1 diabetes patients. The ability for high-quality models to improve glycemic regulation for individuals with type 1 diabetes is noted. The fact that bolused insulin and meal carbohydrates are the most influential inputs influencing the concentration of glucose in the blood is mentioned. Also noted is the possibility that inputs with high levels of excitation may cause extreme hypoglycemia or hyperglycemia for the patient.

For people with type 1 diabetes, automatic controllers aim to maintain the blood glucose concentration within the desired range of 60–120 mg/dL by infusing the appropriate amount of insulin in the presence of meal and exercise disturbances. Blood glucose concentration outside the desired range can be harmful to an individual’s health but concentration below 60 mg/dL, a state known as hypoglycemia, is considered to be more harmful than the concentration above 120 mg/dL, a state known as hyperglycemia. In this paper, two techniques to address this issue within a multi-parametric model based control framework are presented. The first technique introduces asymmetry into the objective function to penalize the deviation towards hypoglycemia more than the deviation towards hyperglycemia. The second technique is based upon placing higher priority on satisfaction of constraints on hypoglycemia than on satisfaction of constraints on hyperglycemia. The performance of both the control techniques is analyzed and compared in the presence of disturbances. [ABSTRACT FROM AUTHOR]

OBJECTIVE -- To assess whether prandial insulin, in addition to basal insulin, has an effect on the rate of glucose appearance from a meal in people with type 1 diabetes. RESEARCH DESIGN AND METHODS -- The rate of glucose appearance from a mixed meal (Ra[sub meal]) was investigated in six adult (aged 24 ± 2 years), lean (BMI 23.6 ± 1.5 kg/m²) subjects with well-controlled type 1 diabetes (duration 7.9 ± 6.9 years, A1C 7.6 ± 0.9%) with/without prandial insulin. Actrapid was infused to maintain euglycemia before meals were consumed. Subjects consumed two identical meals on separate occasions, and Ra[sub meal] was measured using a dual isotope method. [6,6-²H[sub 2]] glucose was incorporated into the meal (0.081 g/kg body wt), and a primed constant/variable rate infusion of [1,2,3,4,5,6,6-²H[sub 2]]glucose was administered. In the tests with prandial insulin, an additional bolus dose of Actrapid was given 20 min before the meal at 0.1 units/kg body wt. RESULTS -- Insulin concentration with prandial insulin was significantly higher than during basal insulin studies (119 ± 16 vs. 66 ± 15 pmol/l, P = 0.03 by paired t test). Despite differences in insulin concentration, there were no differences in total glucose appearance (3,398 ± 197 vs. 3,307 ± 343 µmol/kg) or time taken for 25% (33.1 ± 3.3 vs. 31.7 ± 3.5 min), 50% (54.6 ± 3.5 vs. 54.1 ± 4.7 min), and 75% (82.9 ± 7.1 vs. 82.8 ± 5.8 min) of total glucose appearance. The fraction of the glucose dose appearing in the circulation was the same for basal (73 ± 8%) and prandial (75 ± 4%) study days. CONCLUSIONS -- These results suggest that meal glucose appearance is independent of prandial insulin concentration in people with type 1 diabetes. [ABSTRACT FROM AUTHOR]

OBJECTIVE -- We propose a novel algorithm to adjust prandial insulin dose using sparse blood glucose measurements. The dose is adjusted on the basis of a performance measure for the same meal on the previous day. We determine the best performance measure and tune the algorithm to match the recommendations of experienced physicians. RESEARCH DESIGN AND METHODS -- Eleven subjects with type 1 diabetes, using continuous subcutaneous insulin infusion, were recruited (seven women and four men, aged 21-65 years with A1C of 7.1 ± 1.3%). Basal insulin infusion rates were optimized. Target carbohydrate content for the lunch meal was calculated on the basis of a weight-maintenance diet. Over a period of 2-4 days, subjects were asked to measure their blood glucose according to the algorithm's protocol. Starting with their usual insulin-to-carbohydrate ratio, the insulin bolus dose was titrated downward until postprandial glucose levels were high (180-250 mg/dl [10-14 mmol/l]). Subsequently, physicians made insulin bolus recommendations to normalize postprandial glucose concentrations. Graphical methods were then used to determine the most appropriate performance measure for the algorithm to match the physician's decisions. For the best performance measure, the gain of the controller was determined to be the best match to the dose recommendations of the physicians. RESULTS -- The correlation between the clinically determined dose adjustments and those of the algorithm is R² = 0.95, P < le - 18. CONCLUSIONS -- We have shown how engineering methods can be melded with medical expertise to develop and refine a dosing algorithm. This algorithm has the potential of drastically simplifying the determination of correct insulin-to-carbohydrate ratios. [ABSTRACT FROM AUTHOR]

Background The last two decades have witnessed unprecedented technological progress in the development of continuous glucose sensors, resulting in the first generation of commercial glucose monitors. This has fuelled the development of prototypes of a closed-loop system based on the combination of a continuous monitor, a control algorithm, and an insulin pump. Method A review of electromechanical closed-loop approaches is presented. This is followed by a review of existing prototypes and associated glucose sensors. A literature review was undertaken from 1960 to 2004. Results Two main approaches exist. The extracorporeal s.c.–s.c. approach employs subcutaneous glucose monitoring and subcutaneous insulin delivery. The implantable i.v.–i.p. approach adopts intravenous sampling and intraperitoneal insulin delivery. Feasibility of both solutions has been demonstrated in small-scale laboratory studies using either the classical proportional–integral–derivative controller or a model predictive controller. Performance in the home setting has yet to be demonstrated. Conclusions The glucose monitor remains the main limiting factor in the development of a commercially viable closed-loop system, as presently available monitors fail to demonstrate satisfactory characteristics in terms of reliability and/or accuracy. Regulatory issues are the second limiting factor. Closed-loop systems are likely to be used first by health-care professionals in controlled environments such as intensive care units. [ABSTRACT FROM AUTHOR]

Aims To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system. Methods Paired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.–s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements (‘s.c.’–s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed. Results Glucose concentration was brought from hyper- to normoglycaemia and kept in the physiological range (6–7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.–s.c. route) and 3 (‘s.c.’–s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 ± 0.2 mmol/l and 6.6 ± 0.3 mmol/l for i.v.–s.c. and ‘s.c.’–s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes ( P > 0.05). Conclusions The MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial β-cell in the subcutaneous route Type 1 diabetic patients. [ABSTRACT FROM AUTHOR]

An algorithm designed to automatically control insulin delivery was tested in rats with Type 1 diabetes. This nonlinear algorithm included a fading memory component of proportional and derivative errors in order to simulate normal insulin secretion. Error-weighting functions for the proportional and derivative terms were used with a performance index designed for error adaptation. In the first version of the algorithm, the proportional gain was adaptively varied. In the second version, a low rate of basal insulin delivery was adaptively varied. Six 6-h studies with each version were conducted using frequent blood sampling and intravenous insulin delivery. In Version 2 studies, blood glucose levels during the last two hours were well-controlled and significantly lower than in Version 1 (118 ± 2.0 vs. 130 ± 2.9 mg/dL). Neither version produced hypoglycemia. Future research using this algorithm needs to focus on automated glucose sensing in combination with insulin delivery. [ABSTRACT FROM AUTHOR]