Deep transfer learning: a novel glucose prediction framework for new subjects with type 2 diabetes

Abstract

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.

Transfer learning with randomized controlled trial data for postprandial glucose prediction

“…For instance, Faruqui et al [8] suggested an approach entailing initial model learning using population data from a patient group as a source task, followed by transferring the model for individual patient-specific learning. Other studies have filtered a subset of population data based on its resemblance to a target patient, employing it as training data for either a source task [8] or a target task [15]. Furthermore, to facilitate knowledge sharing among patients, a multitasking learning strategy [21] was proposed, addressing individual model learning for all patients in parallel.…”

“…In recent years, an innovative solution has emerged to tackle data imbalance concerns by harnessing extensive patient data through transfer learning techniques [8,[14][15][16]. This strategy thrives when the feature distributions of other patient data exhibit a range of values.…”

Optimizing postprandial glucose prediction through integration of diet and exercise: Leveraging transfer learning with imbalanced patient data

“…For instance, Faruqui et al [ 9 ] suggested an approach entailing initial model learning using population data from a patient group as a source task, followed by transferring the model for individual patient-specific learning. Other studies have filtered a subset of population data based on its resemblance to a target patient, employing it as training data for either a source task [ 9 ] or a target task [ 17 ]. Furthermore, to facilitate knowledge sharing among patients, a multitasking learning strategy [ 24 ] was proposed, addressing individual model learning for all patients in parallel.…”

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

“…This is also the most common and challenging situation in applications. UDA algorithms have achieved excellent results in some classification tasks, such as object recognition [14,15], fault diagnosing [16,17] and medical image diagnosis [18,19]. The application of UDA algorithm in the agro-food field is still in its infancy and there is few related research.…”