A review of personalized blood glucose prediction strategies for T1DM patients

Oviedo, Silvia; Vehı́, Josep; Calm, Remei; Armengol, Joaquim

doi:10.1002/cnm.2833

Cited by 231 publications

(177 citation statements)

References 117 publications

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“…In recent years many algorithms have been proposed for glucose forecasting [8], such as polynomial models (autoregressive (AR), autoregressive exogenous (ARX), autoregressive moving-average (ARMA)) [9], machine learning models The work was supported by EPSRC EP/P00993X/1. [10] and latent-variable-based statistical models [11].…”

Section: Introductionmentioning

confidence: 99%

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

127

115

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For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in-silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28±2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83±3.49 mg/dL) with time lag (17.78±8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm.

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Section: Introductionmentioning

confidence: 99%

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Liu

Zhu

et al. 2020

IEEE J. Biomed. Health Inform.

127

115

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“…A significant amount of research has been done for forecasting blood glucose levels within a short to mid-term horizon (15min-2hours) [18], and some of this work has been translated to commercial products, such as the predictive lowglucose insulin suspension systems (Medtronic 640G and Tandem Basal-IQ). However, such short prediction horizons might not always be sufficient to prevent overnight hypoglycaemia and do not prevent the user from waking up during the night in the case of a prediction alert being triggered.…”

Section: Introductionmentioning

confidence: 99%

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

Güemes

Cappon

Hernandez

et al. 2020

IEEE J. Biomed. Health Inform.

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In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are starting to become a reality, their elevated cost and performance below user expectations is hindering their adoption. Hence, a decision support system that helps people with type 1 diabetes, on multiple daily injections or insulin pump therapy, to avoid undesirable overnight blood glucose fluctuations (hyper-or hypoglycaemic) is an attractive alternative. In this paper, we introduce a novel data-driven approach to predict the quality of overnight glycaemic control in people with type 1 diabetes by analyzing commonly gathered data during the day-time period (continuous glucose monitoring data, meal intake and insulin boluses). The proposed approach is able to predict whether overnight blood glucose concentrations are going to remain within or outside the target range, and therefore allows the user to take the appropriate preventive action (snack or change in basal insulin). For this purpose, a number of popular established machine learning algorithms for binary classification were evaluated and compared on a publicly available clinical dataset (i.e. OhioT1DM). Although there is no clearly superior classification algorithm, this study indicates that, by using commonly gathered data in type 1 diabetes management, it is possible to predict the quality of overnight glycaemic control with reasonable accuracy (AUC-ROC=0.7).

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“…However, the inclusion off such models tends to add unwarranted complexity as the pathophysiological and etiological relevance of these states are not well-understood, while producing negligible variations in the observable input-output behaviors that are presently more relevant to healthcare outcomes. Thus, in the development and analysis of systems for the closed-loop treatment of diabetes, models are generally categorized according to their level of detail and may be considered as either (i) reduced complexity control-oriented models -for controller synthesis methods [53], or (ii) high-fidelity models for analysis and validation [55], [56]. In either case, these models attempt to replicate the glucoregulatory dynamics of diabetic patients.…”

Section: Modeling For Analysis and Control Of Diabetesmentioning

confidence: 99%

“…Despite its complexity, the Cambridge model is frequently used in internal model or model predictive control schemes [53], [74] and has been the basis for several clinically tested investigatory closed-loop devices [75].…”

Section: ) Minimal Glucoregulatory Modelsmentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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A review of personalized blood glucose prediction strategies for T1DM patients

Cited by 231 publications

References 117 publications

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

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