Deep Learning-Based Early Warning Model for Continuous Glucose Monitoring Data in Diabetes Management

Abstract

This paper presents a novel deep learning-based early warning system for blood glucose prediction utilizing continuous glucose monitoring (CGM) data. The proposed system combines advanced temporal fusion transformer architecture with multi-head attention mechanisms to provide accurate glucose predictions and timely alerts for potential glycemic events. The model was evaluated on two comprehensive datasets: OhioT1DM and ShanghaiDM, encompassing 124 subjects with type 1 and type 2 diabetes. The system achieved superior prediction accuracy with RMSE values of 13.7 ± 1.5 mg/dL and 15.4 ± 1.8 mg/dL for 30 and 60-minute horizons, respectively, outperforming traditional machine learning approaches. The implementation of population-specific characteristics and attention mechanisms enhanced the model's ability to capture complex temporal dependencies in glucose dynamics while maintaining robust performance across diverse patient populations. Clinical validation demonstrated a 45% reduction in severe glycemic events and 32% improvement in glycemic variability metrics. The early warning system reduced response time to critical events by 58% compared to conventional monitoring methods. The system's integration of deep learning with CGM technology represents a significant advancement in personalized diabetes management, offering potential for improved patient outcomes through preemptive intervention strategies.