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

TL;DR

This paper introduces a machine learning approach for predicting blood glucose trends in type 1 diabetes, enabling earlier alerts and basal rate adjustments to prevent rebound hyperglycemia, thereby improving patient management.

Contribution

It presents a novel ML-based method for BG scenario prediction with specific alerts and preliminary basal adjustment suggestions to prevent rebound hyperglycemia.

Findings

Achieved over 98% accuracy in rebound high event prediction

Attained more than 79% precision in alerts

Demonstrated potential for timely intervention in diabetes management

Abstract

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.