Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator – in silico evaluation under intra-day variability

Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79% for 60-minute).In addition, the model provides competitive performance in providing effective prediction horizon (P H ef f ) with minimal time lag both in a simulated patient dataset (P H ef f = 29.0±0.7 for 30-min and P H ef f = 49.8±2.9 for 60-min) and in a real patient dataset (P H ef f = 19.3±3.1 for 30-min and P H ef f = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.

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“…In addition, a simple model of physical exercise was also used. Details about how the simulator was modified can be found in [32].…”

Section: Data Acquisition and Setting A Data Acquisitionmentioning

confidence: 99%

Convolutional Recurrent Neural Networks for Glucose Prediction

Daniels

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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215

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“…e proposed method is validated in the UVa/Padova simulator [27] on three challenging scenarios, including intrapatient variability in insulin sensitivity and in meal absorption rate [32].…”

Section: In Silico Evaluationmentioning

confidence: 99%

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Bertachi

Biagi

Beneyto

et al. 2020

Journal of Healthcare Engineering

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The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

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“…The 33 virtual subjects are supposed to encompass variations in the population with T1DM [29]. However, since the simulator does not incorporate intra-day variability of insulin sensitivity, and physical activity variability of the ICR, these were artificially introduced as proposed in [30].…”

Section: G Experimentation Set-upmentioning

confidence: 99%

Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes

Torrent-Fontbona

López

2019

IEEE J. Biomed. Health Inform.

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Type 1 Diabetes Mellitus (T1DM) is a chronic disease. Those who have it must administer themselves with insulin to control their blood glucose level. It is difficult to estimate the correct insulin dosage due to the complex glucose metabolism, which can lead to less than optimal blood glucose levels. This paper presents PepperRec, a case-based reasoning (CBR) bolus insulin recommender system capable of dealing with an unrestricted number of situations in which T1DM persons can find themselves. PepperRec considers several factors that affect glucose metabolism, such as data about the physical activity of the user, and can also cope with missing values for these factors. Based on CBR methodology, PepperRec uses new methods to adapt past recommendations to the current state of the user, and retains updated historical patient information to deal with slow and gradual changes in the patient over time (concept drift). The proposed approach is tested using the UVA/PADOVA simulator with 33 virtual subjects and compared with other methods in the literature, and with the default insulin therapy of the simulator. The achieved results demonstrate that PepperRec increases the amount of time the users are in their target glycaemic range, reduces the time spent below it, while maintaining, or even reducing, the time spent above it.

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Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator – in silico evaluation under intra-day variability

Abstract: Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.

Cited by 56 publications

References 31 publications

Convolutional Recurrent Neural Networks for Glucose Prediction

Convolutional Recurrent Neural Networks for Glucose Prediction

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes

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