A Convolutional Neural Network (CNN) algorithm achieved the best classification performance for adherence detection in simulated type 2 diabetes CGM signals, with an average accuracy of 77.5%.
Does a deep learning approach improve adherence detection accuracy compared to standard logistic regression in simulated CGM signals for type 2 diabetes patients?
Convolutional Neural Networks achieved 77.5% accuracy in detecting insulin adherence from simulated CGM signals in type 2 diabetes, demonstrating the potential of deep learning for adherence detection systems.
Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.
Mohebbi et al. (Sat,) conducted a other in Type 2 diabetes. Convolutional Neural Networks (CNNs) vs. Standard logistic regression baseline was evaluated on Adherence detection accuracy. A Convolutional Neural Network (CNN) algorithm achieved the best classification performance for adherence detection in simulated type 2 diabetes CGM signals, with an average accuracy of 77.5%.