Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

Abstract

Background: Continuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN. Methods: In response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location. Results: We observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro—Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes ( P < .05). Conclusion: We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.

Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning

“…Since the recursive process in Step 1 is repeated, the amount of calculation is large. A gated-graph sequential neural network (GGS-NN) replaces the recursion process in Step 1 with a Gated Recurrent Unit (GRU), which is the gating mechanism in a recurrent neural network (RNN) and which has better performance on certain smaller datasets and removes the constraints of contraction mapping [86][87][88][89][90][91][92]. The GRU concept can be expressed using the following formula:…”