Parallel software architecture for the next generation of glucose monitoring

Koutný, Tomáš; Ubl, Martin

doi:10.1016/j.procs.2018.10.197

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…The recent developments in CGM technology [2] provide realtime monitoring of the current glucose, so people can observe their glucose concentration trend visually on devices. It has been proven that CGM technology helps T1D subjects to have a better glycemic control [3], [4] by alerting them when hypoor hyperglycemia events occur [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

121

115

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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: Introductionmentioning

confidence: 99%

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

121

115

View full text Add to dashboard Cite

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“…As part of the scope, the imputed data distribution should match that of the non-imputed data qualitatively, which is achieved using the previous simple and straightforward method. However, it is important to stress that there are a number of more sophisticated numerical methods (see for instance [ 21 , 22 , 23 ]). In the Appendix A we provide further insight regarding the imputed data; specifically, we show the percentage of imputed data per individual, as well as the mean and standard deviation of consecutive imputed values.…”

Section: Materials and Methodsmentioning

confidence: 99%

Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks

Toledo-Marín

Ali

Rooij

et al. 2023

JCM

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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.

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“…Filters are connected in a linear chain. Entities communicate by passing small messages called device events [ 28 ]. A device event contains a single item of information passed from the event source filter through the chain to the last filter in the chain.…”

Section: Introductionmentioning

confidence: 99%

Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment

Ubl

Koutný

Cioppa

et al. 2022

Sensors

Self Cite

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Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively.

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Parallel software architecture for the next generation of glucose monitoring

Cited by 11 publications

References 13 publications

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks

Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment

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