Journal
COMPLEX & INTELLIGENT SYSTEMS
Volume 8, Issue 3, Pages 1875-1887Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s40747-021-00360-7
Keywords
Transfer learning; Blood glucose prediction; Deep learning network; Dynamic time warping (DTW)
Categories
Funding
- National Natural Science Foundation of China [61903071, 61973067]
- National Key R&D Program of China [2018YFC2001004]
- Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support [20161430]
Ask authors/readers for more resources
The study aimed to design a novel framework for cross-subject glucose prediction using instance-based and network-based deep transfer learning. By applying dynamic time warping to identify the proper source domain dataset, a network-based deep transfer learning method was designed to improve generalization capability. Experimental results demonstrated that the framework achieved more accurate glucose predictions for new subjects with type 2 diabetes.
Blood glucose (BG) prediction is an effective approach to avoid hyper- and hypoglycemia, and achieve intelligent glucose management for patients with type 1 or serious type 2 diabetes. Recent studies have tended to adopt deep learning networks to obtain improved prediction models and more accurate prediction results, which have often required significant quantities of historical continuous glucose-monitoring (CGM) data. However, for new patients with limited historical dataset, it becomes difficult to establish an acceptable deep learning network for glucose prediction. Consequently, the goal of this study was to design a novel prediction framework with instance-based and network-based deep transfer learning for cross-subject glucose prediction based on segmented CGM time series. Taking the effects of biodiversity into consideration, dynamic time warping (DTW) was applied to determine the proper source domain dataset that shared the greatest degree of similarity for new subjects. After that, a network-based deep transfer learning method was designed with cross-domain dataset to obtain a personalized model combined with improved generalization capability. In a case study, the clinical dataset demonstrated that, with additional segmented dataset from other subjects, the proposed deep transfer learning framework achieved more accurate glucose predictions for new subjects with type 2 diabetes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available