Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention
This addresses timely treatment lapses in artificial pancreas systems for diabetes patients, but it is incremental as it builds on existing APS advancements.
The paper tackles the problem of preventing rebound hyperglycemia in type 1 diabetes by proposing a machine learning method for predictive blood glucose scenario categorization, which outputs alert messages and basal rate suggestions, achieving over 98% accuracy and 79% precision in predicting rebound high events on a clinical dataset.
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.