SmartCGMS as a Testbed for a Blood-Glucose Level Prediction and/or Control Challenge with (an FDA-Accepted) Diabetic Patient Simulation

Koutný, Tomáš; Ubl, Martin

doi:10.1016/j.procs.2020.10.048

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…For example, we proposed a new approach to metabolic modelling [ 38 ]. With a different, possibly new simulator, we can use the SmartCGMS ability to change the used in silico simulator to another one as easily as only modifying the experimental setup [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…The controller needs specific insulin-pump software. To evaluate the controller, we propose use of the SmartCGMS framework [ 19 ]. It is a signal-processing framework developed in modern C++, designed to run on low-power, safety-critical devices, such as an insulin pump.…”

Section: Introductionmentioning

confidence: 99%

“…SmartCGMS is a framework for signal analysis and processing [ 19 ]. It is written in modern C++ and is based on high-level architecture principles.…”

Section: Introductionmentioning

confidence: 99%

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

Ubl

Koutný

Cioppa

et al. 2022

Sensors

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…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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“…To process the measured data, we used our own specialized software that eventually evolved to SmartCGMS -continuous glucose monitoring and controlling framework 15 . This software enables us to conduct advanced research on diabetes treatment.…”

Section: Methodsmentioning

confidence: 99%

Physiological reconstruction of blood glucose level using CGMS-signals only

Koutný

2022

Sci Rep

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Patient with diabetes must regularly monitor blood glucose level. Drawing a blood sample is a painful and discomfort experience. Alternatively, the patient measures interstitial fluid glucose level with a sensor installed in subcutaneous tissue. Then, a model of glucose dynamics calculates blood glucose level from the sensor-measured, i.e., interstitial fluid glucose level of subcutaneous tissue. Interstitial fluid glucose level can significantly differ from blood glucose level. The sensor is either factory-calibrated, or the patient calibrates the sensor periodically by drawing blood samples, when glucose levels of both compartments are steady. In both cases, the sensor lifetime is limited up to 14 days. This is the present state of the art. With a physiological model, we would like to prolong the sensor lifetime with an adaptive approach, while requiring no additional blood sample. Prolonging sensor’s lifetime, while reducing the associated discomfort, would considerably improve patient’s quality of life. We demonstrate that it is possible to determine personalized model parameters from multiple CGMS-signals only, using an animal experiment with a hyperglycemic clamp. The experimenter injected separate glucose and insulin boluses to trigger rapid changes, on which we evaluated the ability to react to non-steady glucose levels in different compartments. With the proposed model, 70%, 80% and 95% of the calculated blood glucose levels had relative error less than or equal to 21.9%, 32.5% and 43.6% respectively. Without the model, accuracy of the sensor-estimated blood glucose level decreased to 39.4%, 49.9% and 99.0% relative errors. This confirms feasibility of the proposed method.

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SmartCGMS as a Testbed for a Blood-Glucose Level Prediction and/or Control Challenge with (an FDA-Accepted) Diabetic Patient Simulation

Cited by 4 publications

References 24 publications

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

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

Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks

Physiological reconstruction of blood glucose level using CGMS-signals only

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