Decoding Diabetes: Harnessing AI to Accurately Predict Real-Time and Future Blood Glucose Levels for Diabetes Management Using Diet, Exercise, Insulin Intake, and Heart Rate Variability

Continuous glucose monitoring (CGM) systems play a crucial role in diabetes care. Yet, they focus solely on blood glucose levels (BGL), neglect diet, exercise, and medication, and lack predictive capabilities, leaving patients and clinicians with reactive rather than proactive solutions. This study introduces Dual Temporal Recurrent Ensemble (DTRE), a novel AI model that bridges these gaps by enabling real-time BGL monitoring without a traditional CGM and forecasting BGL for 30 to 120 minutes into the future when integrated with CGM data. The model’s performance is based on two parallel branches of hybrid models that combine advanced machine learning architectures for accurate predictions and integrate key biomarkers like diet, exercise, insulin intake, heart rate (HR), and heart rate variability (HRV).

This pioneering study developed and validated DTRE on two diverse datasets, OHIOT1DM and D1NAMO, achieving exceptional forecasting accuracy: a Mean Absolute Relative Difference (MARD) of 7.6% at 30 minutes and 19.2% at 120 minutes, outperforming existing models by 13 to 41%. DTRE also surpassed commercial CGM systems for real-time BGL predictions by achieving a MARD of 7.17% on the high-frequency D1NAMO dataset compared to FreeStyle Libre 3 (7.9%) and Dexcom G7 (8.2%).

DTRE is the first AI-driven virtual BGM (vBGM) to integrate all four pillars of diabetes care and validate them on two diverse datasets. It offers a non-invasive, low-cost, and proactive solution. Its real-time insights can provide patients and clinicians with actionable data, transforming diabetes management for individuals who depend on insulin. Validation across diverse datasets underscores its potential for global application.