930-P: Blood Glucose Levels Prediction Accuracy for T1DM Patients Using Neural Networks to Combine Insulin Doses, Food Nutrients, and Heart Rate

This study analyzed the accuracy of a BGL predictive model (BGL-PM) for type 1 diabetes mellitus patients (T1DM) in a real-world environment. The study population consisted of 10 individuals with T1DM, half of them were female, age 33 (SD:11.2), BMI of 26.1 (4.2) and 60% were under carbohydrate-count treatment. After consent, patients underwent a medical evaluation and registered their daily activities using a smartphone application (GlucoTrends) for 28 days, with BGL and heart rate continuously monitored. BGL-PM was developed using a Deep Learning architecture, based on Recurrent Neural Networks. Models were trained for each patient using different training sets sizes (7, 14, 21 days). Prediction accuracy was evaluated by Mean Absolute Percentage Error (MAPE) on the last 5 days for different Prediction Horizons (PH): 30, 60, 120, 180 and 360 minutes, comparing full day and nocturnal period. The model predicted BGL with relevant accuracy for the dataset with 21 training days up to 60 minutes in both periods: full day (median MAPE 22.5%) and nocturnal (14.3%) (Figure). The BGL-PM was able to provide useful BGL predictions, especially during the night period, which can be improved by increasing the training period. Consequently, this BGL-PM poses as a complementary tool for the prevention of acute complications such as hypoglycemia and hyperglycemia in the management of DM.

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