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Significant advancements in type 1 diabetes treatment have been made in thedevelopment of state-of-the-art Artificial Pancreas Systems (APS). However,lapses currently exist in the timely treatment of unsafe blood glucose (BG)levels, especially in the case of rebound hyperglycemia. We propose a machinelearning (ML) method for predictive BG scenario categorization that outputsmessages alerting the patient to upcoming BG trends to allow for earlier,educated treatment. In addition to standard notifications of predictedhypoglycemia and hyperglycemia, we introduce BG scenario-specific alertmessages and the preliminary steps toward precise basal suggestions for theprevention of rebound hyperglycemia. Experimental evaluation on the DCLP3clinical dataset achieves >98% accuracy and >79% precision for predictingrebound high events for patient alerts.

Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention