Abstract

Accurate prediction of blood glucose variations in type 2 diabetes (T2D) will facilitate better glycemic control and decrease the occurrence of hypoglycemic episodes as well as the morbidity and mortality associated with T2D, hence increasing the quality of life of patients. Owing to the complexity of the blood glucose dynamics, it is difficult to design accurate predictive models in every circumstance, i.e., hypo/normo/hyperglycemic events. We developed deep-learning methods to predict patient-specific blood glucose during various time horizons in the immediate future using patient-specific every 30-min long glucose measurements by the continuous glucose monitoring (CGM) to predict future glucose levels in 5 min to 1 h. In general, the major challenges to address are (1) the dataset of each patient is often too small to train a patient-specific deep-learning model, and (2) the dataset is usually highly imbalanced given that hypo- and hyperglycemic episodes are usually much less common than normoglycemia. We tackle these two challenges using transfer learning and data augmentation, respectively. We systematically examined three neural network architectures, different loss functions, four transfer-learning strategies, and four data augmentation techniques, including mixup and generative models. Taken together, utilizing these methodologies we achieved over 95% prediction accuracy and 90% sensitivity for a time period within the clinically useful 1 h prediction horizon that would allow a patient to react and correct either hypoglycemia and/or hyperglycemia. We have also demonstrated that the same network architecture and transfer-learning methods perform well for the type 1 diabetes OhioT1DM public dataset.

Highlights

  • Type 2 diabetes (T2D) is a multifactorial progressive chronic metabolic disorder, accounting for approximately 90% of all cases of diabetes[1]

  • The results suggest that the transfer-learning models training data from the target patient and, better than (Transfer[1] and Transfer2) can sometimes outperform the pre- those by existing classification methods examined in predicting trained models in convolutional neural networks (CNNs) models

  • High intracellular glucose concentration leads to the exhaustion of the antioxidant pathways, altered regulation of gene transcription and increased expression of pro-inflammatory molecules resulting in cellular dysfunction and death[65]

Read more

Summary

Introduction

Type 2 diabetes (T2D) is a multifactorial progressive chronic metabolic disorder, accounting for approximately 90% of all cases of diabetes[1].

Objectives
Methods
Findings
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call