Model-fusion-based online glucose concentration predictions in people with type 1 diabetes

Xia, Yu; Turksoy, Kamuran; Rashid, Mudassir; Feng, Jun‐e; Hobbs, Nicole; Hajizadeh, Iman; Samadi, Sediqeh; Sevil, Mert; Lazaro, C. Burgos; Maloney, Zacharie; Littlejohn, Elizabeth; Quinn, Laurie; Çınar, Ali

doi:10.1016/j.conengprac.2017.10.013

Cited by 33 publications

(12 citation statements)

References 62 publications

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“…Accurate glucose prediction is also vital for the early and proactive regulation of blood glucose before it drifts to undesirable levels. Therefore, numerous approaches, based on physical models or data-driven empirical models, have been proposed to predict glucose levels [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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

Xia

Yang

et al. 2021

Complex Intell. Syst.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Xia

Yang

et al. 2021

Complex Intell. Syst.

Self Cite

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“…For example, an autoregressive with exogenous input model was presented in Romero-Ugaldein et al [18] to predict interstitial glucose. In Yu et al [19], four different adaptive filters and a fusion mechanism were proposed for the online glucose concentration predictions. Combining feature ranking with support vector regression or Gaussian processes, Georga et al [20] investigated the shortterm glucose prediction.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control

Cai

Liu

et al. 2021

Preprint

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Effective postprandial glucose control is important to glucose management for subjects with diabetes mellitus. In this work, a data-driven meal bolus decision method is proposed without the need of subject-specific glucose management parameters. The postprandial glucose dynamics is learnt using Gaussian process regression. Considering the asymmetric risks of hyper-and hypoglycemia and the uncertainties in the predicted glucose trajectories, an asymmetric risk-sensitive cost function is designed. Bayesian optimization is utilized to solve the optimization problem, since the gradient of the cost function is unavailable. The proposed approach is evaluated using the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the standard insulin bolus calculator. For the case of announced meals, the proposed method achieves satisfactory and similar performance in terms of mean glucose and percentage time in [70, 180] mg/dL without increasing the risk of hypoglycemia. Similar results are observed for the case without the meal information (assuming that the patient follows a consistent diet) and the case of basal rate mismatches. In addition, advisory-mode analysis is performed based on clinical data, which indicates that the method can determine safe and reasonable meal boluses in real clinical settings. The results verify the effectiveness and robustness of the proposed method and indicate the feasibility of achieving improved postprandial glucose regulation through a data-driven optimal control method.

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“… 1 . Several statistical methods for predicting upcoming glucose values from CGM data have been suggested in the last decade, and include dynamic risk measurement, 2 stochastic models, 3,4 auto‐regressive models, 5‐7 dynamic state space models and control algorithms, 8‐18 neural network based models 19,20 support vector regression models, 21,22 and machine learning (ML)‐based models 7,22,23 …”

Section: Introductionmentioning

confidence: 99%

Improving blood glucose level predictability using machine learning

Marcus

Eldor

Yaron

et al. 2020

Diabetes Metabolism Res

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This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient‐specific supervised‐machine‐learning (SML) analysis of glucose level based on a continuous‐glucose‐monitoring system (CGM) that needs no human intervention, and minimises false‐positive alerts. The CGM data over 7 to 50 non‐consecutive days from 11 type‐1 diabetic patients aged 18 to 39 with a mean HbA1C of 7.5% ± 1.2% were analysed using four SML models. The algorithm was constructed to choose the best‐fit model for each patient. Several statistical parameters were calculated to aggregate the magnitudes of the prediction errors. The personalised solutions provided by the algorithm were effective in predicting glucose levels 30 minutes after the last measurement. The average root‐mean‐square‐error was 20.48 mg/dL and the average absolute‐mean‐error was 15.36 mg/dL when the best‐fit model was selected for each patient. Using the best‐fit‐model, the true‐positive‐hypoglycemia‐prediction‐rate was 64%, whereas the false‐positive‐ rate was 4.0%, and the false‐negative‐rate was 0.015%. Similar results were found even when only CGM samples below 70 were considered. The true‐positive‐hyperglycemia‐prediction‐rate was 61%. State‐of‐the‐art SML tools are effective in predicting the glucose level values of patients with type‐1diabetes and notifying these patients of future hypoglycemic and hyperglycemic events, thus improving glycemic control. The algorithm can be used to improve the calculation of the basal insulin rate and bolus insulin, and suitable for a closed loop “artificial pancreas” system. The algorithm provides a personalised medical solution that can successfully identify the best‐fit method for each patient.

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Model-fusion-based online glucose concentration predictions in people with type 1 diabetes

Cited by 33 publications

References 62 publications

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

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

Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control

Improving blood glucose level predictability using machine learning

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