Diabetes is a metabolic disorder characterized by persistent hyperglycemia, with its incidence steadily rising worldwide. Blood glucose monitoring is a core measure in diabetes management, and continuous glucose monitoring provides more comprehensive...
In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of...
In recent years, with increasing social pressure and irregular schedules, many people have developed unhealthy eating habits, which has resulted in an increasing number of patients with diabetes, a disease that cannot be cured under the current medic...
Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the...
In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the...
Diabetes mellitus is a chronic metabolic disorder characterized by the dysregulation of blood glucose, which can lead to a range of serious health complications if not properly managed. Continuous glucose monitoring (CGM) is a cutting-edge technology...
Introduction: Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden...
This paper presents an individualized multiple linear regression model based on compositional data where we predict the mean and coefficient of variation of blood glucose in individuals with type 1 diabetes for the long-term (2 and 4 h). From these p...
In this paper we investigate the effect of two preprocessing techniques, data imputation and smoothing, in the prediction of blood glucose level in type 1 diabetes patients, using a novel deep learning model called Transformer. We train three models:...
Glucose management at night is a major challenge for people with type 1 diabetes (T1D), especially for those managed with multiple daily injections (MDIs). In this study, we developed machine learning (ML) and deep learning (DL) models to predict noc...
Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient predicti...
More than 14% of the world’s population suffered from diabetes mellitus in 2022. This metabolic condition is defined by increased blood glucose concentrations. Among the different types of diabetes, type 1 diabetes, caused by a lack of insulin...
Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturna...
This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons—30, 60, and 120 min—and compares their performance with ten recently proposed neural networks. The twelve models’ perfo...
Background and Objectives: The daily lifestyle management of diabetes requires accurate predictions of the blood glucose level between meals. The objective of this study was to improve the accuracy achieved by previous work, especially on the mid-ter...
Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger c...
Wearable sensors offer a promising platform for non-invasive glucose monitoring by indirectly predicting glucose levels from physiological signals. However, machine learning models trained on such data often suffer degraded performance when applied t...
Predictors for glucose intolerance postpartum were evaluated in women with gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. 1841 women were tested for GDM in a prospective cohort study. A postpartum 75g...
This paper proposes an RG hyperparameter optimization approach, based on a sequential use of random search (R) and grid search (G), for improving the blood glucose level prediction of boosting ensemble learning models. An indirect prediction of blood...
Background: Type 1 Diabetes Mellitus (T1D) is an autoimmune disease that can cause serious complications that can be avoided by preventing the glycemic levels from exceeding the physiological range. Straightforwardly, many data-driven models were dev...
Background/Objectives: Accurate blood glucose forecasting is critical for closed-loop insulin delivery systems to support effective disease management in people with type 1 diabetes (T1D). While long short-term memory (LSTM) neural networks have show...
Continuous glucose monitoring data have strong time variability as well as complex non-stationarity and nonlinearity. The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decom...
Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-con...
Common dysglycemia measurements including fasting plasma glucose (FPG), oral glucose tolerance test (OGTT)-derived 2 h plasma glucose, and hemoglobin A1c (HbA1c) have limitations for children. Dynamic OGTT glucose and insulin responses may better ref...
Diabetes is an increasingly common disease that poses an immense challenge to public health. Hyperglycemia is also a common complication in clinical patients in the intensive care unit, increasing the rate of infection and mortality. The accurate and...
Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (...
The glucose–insulin regulatory system and its glucose oscillations is a recurring theme in the literature because of its impact on human lives, mostly the ones affected by diabetes mellitus. Several approaches have been proposed, from mathemati...
Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to...
The global effort to manage diabetes effectively is driving continuous innovation in glucose monitoring devices. While current systems have improved patient care, persistent challenges with sensor stability and invasiveness highlight the need for adv...
Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring...
Diabetes has become a serious health concern. The use and popularization of blood glucose measurement devices have led to a tremendous increase on health for diabetics. Tracking and maintaining traceability between glucose measurements, insulin doses...
Due to the complexity of blood glucose dynamics and the high variability of the physiological structure of diabetic patients, implementing a safe and effective insulin dosage control algorithm to keep the blood glucose of diabetic patients within the...
The detection of glucose is crucial for diagnosing diseases such as diabetes and enables timely medical intervention. In this study, a disposable enzymatic screen-printed electrode electrochemical biosensor enhanced with machine learning (ML) for qua...
The paper “Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions” (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diab...
Type 1 diabetes is an autoimmune disease that occurs when the immune system unintentionally attacks and damages β cells in the pancreas, reducing the organ’s ability to produce insulin. An artificial pancreas is a technology that uses a pu...
Blood glucose stability in diabetic patients determines the degree of health, and changes in blood glucose levels are related to the outcome of diabetic patients. Therefore, accurate monitoring of blood glucose has a crucial role in controlling diabe...
Objective: Saliva glucose has been widely used in diagnosing and monitoring diabetes, but the saliva collection method will affect saliva glucose concentration. So, this study aims to identify the ideal saliva collection method. Method: A total amoun...
The triglyceride glucose (TyG) index, derived from a combination of fasting glucose and triglycerides, has been suggested as a useful marker for insulin resistance (IR), in addition to modified TyG indices that combine obesity parameters. This study...
This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictio...
Postprandial Hyperglycemia (PPHG) persistently threatens patients’ health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data usin...
(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e...
Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels in...
In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A furth...
Continuous monitoring of glucose levels is important for diabetes management and prevention. While traditional glucose monitoring methods are often invasive and expensive, recent approaches using machine learning (ML) models have explored non-invasiv...
Background: T2DM heterogeneity affects responsiveness to lifestyle treatment. Beta-cell failure and nonalcoholic fatty liver disease (NAFLD) independently predict T2DM, but NAFLD inconsistently predicts metabolic response to lifestyle intervention. A...
Background: Gastric cancer is the fifth most prevalent cancer diagnosed worldwide, and approximately 40% of patients who experience recurrence within five years develop hematogenous metastasis. Given that early prediction of recurrence may improve ov...
Background/Objectives: Postprandial glucose variability is a key challenge in diabetes management for patients receiving multiple daily insulin injections (MDI). This study evaluated transformer-based machine-learning models for predicting post-prand...
Developing reliable noninvasive diagnostic and monitoring systems for diabetes remains a significant challenge, especially in the e-healthcare domain, due to computational inefficiencies and limited predictive accuracy in current approaches. The curr...
Continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) systems have revolutionized diabetes management by delivering real-time, dynamic insights into blood glucose levels. This article provides a concise overview of the evolution of C...
Diabetes is a chronic disease that can affect human health negatively when the glucose levels in the blood are elevated over the creatin range called hyperglycemia. The current devices for continuous glucose monitoring (CGM) supervise the glucose lev...
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