  Significant advancements in type 1 diabetes treatment have been made in the
development 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 machine
learning (ML) method for predictive BG scenario categorization that outputs
messages alerting the patient to upcoming BG trends to allow for earlier,
educated treatment. In addition to standard notifications of predicted
hypoglycemia and hyperglycemia, we introduce BG scenario-specific alert
messages and the preliminary steps toward precise basal suggestions for the
prevention of rebound hyperglycemia. Experimental evaluation on the DCLP3
clinical dataset achieves >98% accuracy and >79% precision for predicting
rebound high events for patient alerts.
