Noninvasive Tissue Glucose Prediction in Patients with Type 1 Diabetes

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 948-P
Author(s):  
ANDREAS PFÜTZNER ◽  
FILIZ DEMIRCIK ◽  
ALEXANDER LIER ◽  
SANJA RAMLJAK
Keyword(s):  
Type 1 Diabetes ◽  
Glucose Prediction

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

AIChE Journal ◽  
2017 ◽  
Vol 64 (3) ◽  
pp. 822-834 ◽  
Author(s):  
Hong Zhao ◽  
Chunhui Zhao ◽  
Chengxia Yu ◽  
Eyal Dassau
Keyword(s):  
Type 1 Diabetes ◽  
Model Selection ◽  
Optimal Model ◽  
Order Model ◽  
Model Migration ◽  
Selection For ◽  
Glucose Prediction

scholarly journals Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes

2020 ◽  
Vol 28 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Xia Yu ◽  
Elizabeth Littlejohn ◽  
Laurie Quinn ◽  
Ali Cinar ◽  
Mudassir Rashid ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Computationally Efficient ◽  
Filtering Algorithms ◽  
Sparse Kernel ◽  
Glucose Prediction

scholarly journals Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes

2019 ◽  
Vol 13 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Nicole Hobbs ◽  
Iman Hajizadeh ◽  
Mudassir Rashid ◽  
Kamuran Turksoy ◽  
Marc Breton ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Type 1 Diabetes ◽  
Prediction Accuracy ◽  
Artificial Pancreas ◽  
Metabolic State ◽  
Physiological Variables ◽  
Additional Input ◽  
Glucose Prediction

Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Methods: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. Results: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model ( P < .001) and the standard MSO ( P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Conclusions: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

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