Improved Methods for Mid-Term Blood Glucose Level Prediction Using Dietary and Insulin Logs

Abstract

Background and Objectives: The daily lifestyle management of diabetes requires accurate predictions of the blood glucose level between meals. The objective of this study was to improve the accuracy achieved by previous work, especially on the mid-term, i.e., 120 to 180 min prediction horizons, for insulin-dependent patients. Materials and Methods: An absorption model-based method is proposed to train an artificial neural network with the bolus and basal insulin dosing and timing, the baseline blood glucose level, the maximal glucose infusion rate, and the total carbohydrate content as parameters. The approach was implemented in various algorithmic setups, and it was validated on data from a small-scale clinical trial with continuous glucose monitoring. Results: Root mean square error results for the mid-term horizons are 1.72 mmol/L (120 min) and 1.95 mmol/L (180 min). The accuracy of the proposed model measured on the clinical data is better than the accuracy reported by any other currently available and comparable models. Conclusions: A relatively short (ca. two weeks) training sample of a continuous glucose monitor and dietary/insulin log is sufficient to provide accurate predictions. For the outpatient application in practice, a hybrid model is proposed that combines the present mid-term method with the authors’ previous work for short-term predictions.

Glucose trend prediction model based on improved Wavelet Transform and Gated Recurrent Unit

“…With the continuous development of glucose prediction technology, numerous scholars have started to explore the performance of different types of models in glucose prediction [17][18][19][20][21][22][23][24][25][26][27] . Based on the published literature, two types of glucose prediction methods can be classified: physiology-based glucose prediction and data-driven glucose prediction [28,29] .…”