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Tuesday, October 26 • 2:15pm - 2:30pm
Deep Learning-Based Blood Glucose Predictors In Type 1 Diabetes

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Technical Presentations Group 4: Healthcare

Objectives: In this work, we present short-term predictions of blood glucose (BG) levels in people with type 1 diabetes (T1D) obtained with a deep-learning based architecture applied to a multivariate physiological dataset of actual T1D patients. Methods: Stacks of convolutional neural network (CNN) and long short-term memory (LSTM) units are proposed to predict BG levels for 30, 60 and 90 minutes prediction horizons (PH), given historical glucose measurements, meal information and insulin intakes. Evaluation of predictive capabilities was performed on two actual patients datasets, Replace-BG and DIAdvisor, respectively. Findings: for 90 minutes PH our model obtained mean absolute error (MAE) of 17.30 ± 2.07 and 18.23 ± 2.97 [mg/dl], root mean squared error (RMSE) of 23.45 ± 3.18 and 25.12 ± 4.65 [mg/dl]), coefficient of determination (R2) of 84.13 ± 4.22 and 82.34 ± 4.54 [%], and in terms of the continuous glucose-error grid analysis (CG-EGA) 94.71 ± 3.89 [%] and 91.71 ± 4.32 [%] accurate predictions (AP), 1.81 ± 1.06 [%] and 2.51 ± 0.86 [%]) benign errors (BE), and 3.47 ± 1.12 [%] and 5.78 ± 1.72 [%] erroneous prediction (EP), for Replace-BG and DIAdvisor datasets, respectively. Conclusion: Our investigation demonstrated that our method, compared to existing approaches in the literature, achieved superior glucose forecasting performance, showing the potential for application in decision support systems for diabetes management.

Authors: Mehrad Jaloli (University of Houston) and Marzia Cescon (University of Houston)


Marzia Cescon

University of Houston

Tuesday October 26, 2021 2:15pm - 2:30pm CDT

Attendees (5)