Background: Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict blood glucose (BG) concentration levels and hypo/hyperglycemia events. Prediction systems using machine learning techniques have mainly focused on applications for sensor augmented pump (SAP) therapy. In contrast, insulin bolus calculators that rely on BG prediction for multiple daily insulin (MDI) injections for patients under self-monitoring blood glucose (SMBG) are scarce because of insufficient data sources and limited prediction capability of forecasting models.
Methods: We trained individualized models that can predict postprandial hypoglycemia via different machine learning algorithms using retrospective data from 10 real patients. In addition, we designed and tested a hypoglycemia reduction strategy for a similar in silico population. The system generates a bolus reduction suggestion as the scaled weighted sum of the predictions. We evaluated the general and postprandial glycemic outcomes of the in silico population to assess the systems capability of avoiding hypoglycemias.
Results: The median [IQR] sensitivity and specificity for hypoglycemia cases where the BG level was below 70 mg/dL were 0.49 [0.2-0.5] and 0.74 [0.7-0.9], respectively. For hypoglycemia cases where the BG level was below 54 mg/dL, the median [IQR] sensitivity and specificity were 0.51 [0.4-0.6] and 0.74 [0.7-0.8], respectively.
Conclusions: The results indicated a decrease of 37% in the median number of postprandial hypoglycemias median decrease of 44% for hypoglycemias of 70 mg/dL and 54 mg/dL, respectively. This dramatic reduction makes this method a good candidate to be integrated into any Decision Support System for diabetes management.
Keywords: Blood glucose; Bolus reduction; Hypoglycemia prediction; Machine learning; Postprandial hypoglycemia; Type 1 diabetes.
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