Long-Term Predictive Analytics of Continuous Glucose Sensing for Enhanced Glycemic Control

Type 1 diabetes (T1D) presents an escalating health challenge requiring precise glycemic control within management protocols. The need for energy-efficient wearable medical-grade devices and biosensors, notably continuous glucose monitoring (CGM), facilitates real-time monitoring and augments interest in blood glucose prediction. However, existing research predom-inantly concentrates on short-term blood glucose forecasting, overlooking the critical need for accurate long-term prediction. This work addresses this gap by exploring the feasibility and efficacy of long-term blood glucose prediction (60, 120, and 240 minutes) leveraging deep multitask learning (DMTL). In addition, long-short-term memory (LSTM) based recurrent neural network (RNN) architecture has been explored within the shared layers of the DMTL framework. The predictive model effectively captures blood glucose trends, achieving a root mean square error (RMSE) of 56.66±6.96 mg/dL at the 240-minute (four-hour) mark while maintaining 90.36±4.22 % predictions within clinically benign zones in the Clarke error grid. These predictions offer a strategic advantage in long-term care planning, facilitating proactive interventions to stabilize glycemic levels through insulin therapy and dietary intake adjustments. Furthermore, predictive analytics hold significant promise in advancing next-generation AI-enabled automated self-management assistive tools, including artificial pancreas and predictive low-glucose suspend systems.