Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study

Reddy, Monika; Pesl, Peter; Xenou, Maria; Toumazou, C.; Johnston, Desmond G.; Georgiou, Pantelis; Herrero, Pau; Oliver, Nick

doi:10.1089/dia.2015.0413

Cited by 66 publications

(85 citation statements)

References 28 publications

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“…2) Clinical Data: There are two clinical datasets. The first one was obtained from our ABC4D project [21]. T1D diabetic subjects participated and were monitored for 6 consecutive months with Dexcom CGM devices (San Diego, CA).…”

Section: Resultsmentioning

confidence: 99%

“…GluNet offers a personalized glucose forecast, and the model can evolve when new personal data are collected. We have evaluated GluNet on 10 simulated virtual T1D adult and adolescent subjects, respectively, generated from the UVA/Padova T1D simulator [20], as well as two clinical dataset including 10 T1D subjects from the ABC4D project using Dexcom T M CGM sensors [21], and 6 T1D subjects from Ohio Dataset [22]. The results show that the proposed framework achieves state-of-the-art glucose forecasting in terms of accuracy and time lag on both in silico and the clinical dataset.…”

Section: Introductionmentioning

confidence: 99%

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GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

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For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in-silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28±2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83±3.49 mg/dL) with time lag (17.78±8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

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128

115

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“…ABC4D has previously been validated in silico [Herrero 2015a, Herrero 2015b] and tested in clinical trials [Pesl 2016b, Reddy 2016]. …”

Section: Methodsmentioning

confidence: 99%

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

Herrero

Bondia

Adewuyi

et al. 2017

Computer Methods and Programs in Biomedicine

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“…Clinical data was obtained from a clinical study at Imperial College Healthcare NHS Trust St. Marys Hospital, London (UK) consisting of multiple phases evaluating the benefits of an advanced insulin bolus calculator for T1D subjects [33]. The dataset in consideration was collected from a 6-month period involving 10 adult subjects with T1D.…”

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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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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Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study

Abstract: Background: The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin

Cited by 66 publications

References 28 publications

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

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

Convolutional Recurrent Neural Networks for Glucose Prediction

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