Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avert the dangers of dysglycemia (hyperglycemia or hypoglycemia). Despite the advent of sophisticated technologies such as automated insulin delivery (AID) systems, achieving optimal glycemic control remains a formidable task. AID systems integrate continuous subcutaneous insulin infusion (CSII) and continuous glucose monitors (CGM) data, offering promise in reducing variability and increasing glucose time-in-range. However, these systems often fail to prevent dysglycemia, partly due to limitations in prediction algorithms that lack the precision to avert abnormal glucose events. This gap highlights the need for proactive behavioral adjustments. We address this need with GLIMMER, Glucose Level Indicator Model with Modified Error Rate, a machine learning approach for forecasting blood glucose levels. GLIMMER categorizes glucose values into normal and abnormal ranges and devises a novel custom loss function to prioritize accuracy in dysglycemic events where patient safety is critical. To evaluate the potential of GLIMMER for T1D management, we both use a publicly available dataset and collect new data involving 25 patients with T1D. In predicting next-hour glucose values, GLIMMER achieved a root mean square error (RMSE) of 23.97 (+/-3.77) and a mean absolute error (MAE) of 15.83 (+/-2.09) mg/dL. These results reflect a 23% improvement in RMSE and a 31% improvement in MAE compared to the best-reported error rates.

本研究解决了1型糖尿病管理中血糖预测精度不足的问题，提出了一种新的机器学习方法GLIMMER，专注于报告异常血糖事件的重要性。该模型采用修改的损失函数，显著提高了血糖预测的准确性，结果表明在预测下一小时血糖值时，RMSE和MAE分别提高了23%和31%。

使用GLIMMER进行1型糖尿病管理：具有修改误差率的葡萄糖水平指示模型