Blood Glucose Level Prediction Research Articles

Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention.

Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus are precisely associated with the poor lifestyle adopted by patients. In particular, a healthy diet and nutritious food are the key to success for such patients. This study was done to help T2DM patients improve their health by developing a favorable lifestyle under an AI-assisted Continuous glucose monitoring (CGM) digital system. This study aims to reduce the blood glucose level fluctuations of such patients by rectifying their daily diet and maintaining their exertion vs. food consumption records. In this study, a well-precise prediction is obtained by training the ML model on a dataset recorded from CGM sensor devices attached to T2DM patients under observation. As the data obtained from the CGM sensor is time series, to predict blood glucose levels, the time series analysis and forecasting are done with XGBoost, SARIMA, and Prophet. The results of different Models are then compared based on performance metrics. This helped in monitoring various trends, specifically irregular patterns of the patient's glucose data, collected by the CGM sensor. Later, keeping track of these trends and seasonality, the diet is adjusted accordingly by adding or removing particular food and keeping track of its nutrients with the intervention of a commercially available all-in-one AI solution for food recognition. This created an interactive assistive system, where the predicted results are compared to food contents to bring the blood glucose levels within the normal range for maintaining a healthy lifestyle and to alert about blood glucose fluctuations before the time that are going to occur sooner. This study will help T2DM patients get in managing diabetes and ultimately bring HbA1c within the normal range (<= 5.7%) for diabetic and pre-diabetic patients, three months after the intervention.

After-meal blood glucose level prediction for type-2 diabetic patients

Type 2 Diabetes, a metabolic disorder disease, is becoming a fast growing health crisis worldwide. It reduces the quality of life, and increases mortality and health care costs unless managed well. After-meal blood glucose level measure is considered as one of the most fundamental and well-recognized steps in managing Type 2 diabetes as it guides a user to make better plans of their diet and thus control the diabetes well. In this paper, we propose a data-driven approach to predict the 2 h after meal blood glucose level from the previous discrete blood glucose readings, meal, exercise, medication, & profile information of Type 2 diabetes patients. To the best of our knowledge, this is the first attempt to use discrete blood glucose readings for 2 h after meal blood glucose level prediction using data-driven models. In this study, we have collected data from five prediabetic and diabetic patients in free living conditions for six months. We have presented comparative experimental study using different popular machine learning models including support vector regression, random forest, and extreme gradient boosting, and two deep layer techniques: multilayer perceptron, and convolutional neural network. We present also the impact of different features in blood glucose level prediction, where we observe that meal has some modest and medication has a good influence on blood glucose level.

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels, posing significant health risks such as cardiovascular disease, and nerve, kidney, and eye damage. Effective management of blood glucose is essential for individuals with diabetes to mitigate these risks. This study introduces the Glu-Ensemble, a deep learning framework designed for precise blood glucose forecasting in patients with type 2 diabetes. Unlike other predictive models, Glu-Ensemble addresses challenges related to small sample sizes, data quality issues, reliance on strict statistical assumptions, and the complexity of models. It enhances prediction accuracy and model generalizability by utilizing larger datasets and reduces bias inherent in many predictive models. The framework's unified approach, as opposed to patient-specific models, eliminates the need for initial calibration time, facilitating immediate blood glucose predictions for new patients. The obtained results indicate that Glu-Ensemble surpasses traditional methods in accuracy, as measured by root mean square error, mean absolute error, and error grid analysis. The Glu-Ensemble framework emerges as a promising tool for blood glucose level prediction in type 2 diabetes patients, warranting further investigation in clinical settings for its practical application.

Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating.

Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.

Ultra-conformal epidermal antenna for multifunctional motion artifact-free sensing and point-of-care monitoring

Accurate acquisition of physiological and physical information from human tissue is essential for health monitoring, disease prevention and treatment. The existing antennas with traditional rigid or flexible substrates are susceptible to motion artifacts in wearable applications due to the miniaturization limitation and lack of proper adhesion and conformal interfaces with the skin. Recent advances in wearable radio frequency (RF) bioelectronics directly drawn on the skin are a promising solution for future skin-interfaced devices. Herein, we present a first-of-its kind epidermal antenna architecture with skin as the antenna substrate, which is ultra-low profile, ultra-conformal, ultra-compact, and simple fabrication without specialized equipment. The radiation unit and ground of antenna are drawn directly on the skin with the strong adhesion and ultra conformality. Therefore, this RF device is highly adaptable to motion. As a proof-of- feasibility, epidermal antenna can be freely drawn on demand at different locations on the skin for the development of temperature sensor, skin hydration sensor, strain sensor, glucose sensor and other devices. An epidermal antenna-based temperature sensor can offer accurate and real-time monitoring of human body temperature changes in the ultra-wideband (UWB) range. The results during the monitoring of hydration level with and without stretching show that the epidermal antenna drawn on the skin is motion artifact-free. We also designed an epidermal antenna array employing a horseshoe-shaped configuration for the precise identification of various gestures. In addition, the non-invasive blood glucose level (BGL) monitoring results during the in-vivo experiments report high correlation between the epidermal antenna responses and BGLs, without any time hysteresis. After the prediction of BGL by BP network, all the predicted BGL values are fallen 100% into the clinically acceptable zones. Together, these results show that epidermal antenna offers a promising new approach for biosensing platform.

AWD-stacking: An enhanced ensemble learning model for predicting glucose levels.

Accurate prediction of blood glucose levels is essential for type 1 diabetes optimizing insulin therapy and minimizing complications in patients with type 1 diabetes. Using ensemble learning algorithms is a promising approach. In this regard, this study proposes an improved stacking ensemble learning algorithm for predicting blood glucose level, in which three improved long short-term memory network models are used as the base model, and an improved nearest neighbor propagation clustering algorithm is adaptively weighted to this ensemble model. The OhioT1DM dataset is used to train and evaluate the performance of the proposed model. This study evaluated the performance of the proposed model using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Matthews Correlation Coefficient (MCC) as the evaluation metrics. The experimental results demonstrate that the proposed model achieves an RMSE of 1.425 mg/dL, MAE of 0.721 mg/dL, and MCC of 0.982 mg/dL for a 30-minute prediction horizon(PH), RMSE of 3.212 mg/dL, MAE of 1.605 mg/dL, and MCC of 0.950 mg/dL for a 45-minute PH; and RMSE of 6.346 mg/dL, MAE of 3.232 mg/dL, and MCC of 0.930 mg/dL for a 60-minute PH. Compared with the best non-ensemble model StackLSTM, the RMSE and MAE were improved by up to 27.92% and 65.32%, respectively. Clarke Error Grid Analysis and critical difference diagram revealed that the model errors were within 10%. The model proposed in this study exhibits state-of-the-art predictive performance, making it suitable for clinical decision-making and of significant importance for the effective treatment of diabetes in patients.

Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study

IntroductionThere is a need for designing non-invasive methods to predict blood glucose levels to ensure timely diagnosis of Diabetes Mellitus. Needle anxiety and bleeding disorders preclude many from undertaking blood tests. ObjectivesThe primary objective of this study was to assess if biomarkers like saliva can be used to estimate blood glucose levels. The second objective was to develop and evaluate Machine Learning (ML) models to predict blood glucose levels based on salivary glucose and associated features. An insight into the patient's features, which was important for predicting blood glucose levels, was also required. MethodsA cross-sectional study was conducted, and blood and saliva samples, along with patient-related data, were collected from healthy and diabetic patients. ML techniques were applied to the data to develop a tool for predicting blood glucose levels using patient features. The prediction intervals were computed, clinical accuracy was assessed, and important features for the prediction were identified. ResultsThe Random Forest Regressor Model, with features identified using the wrapper method, was selected as the best, with an average RMSE of 43.28. The prediction intervals were computed for point estimate, MAE = 23.821, and coverage was 100 percent, the clinical accuracy was compared with that of glucometers and continuous monitoring systems. All predicted values are in Zones A and B of the Clarke error grid, and the bias was 6.41. The most important feature for predicting blood glucose level is salivary glucose level, followed by known risk factors like Family History, BMI, etc. The study found that salivary glucose levels are insufficient to classify blood glucose levels as high or normal. ConclusionThe study concluded that salivary glucose with associated patient features could be a potential non-invasive biomarker for predicting blood glucose levels in patients. The developed ML model could be deployed in a device that inputs patient features, analyzes salivary glucose, and can monitor blood glucose levels in a non-invasive manner. Further research is needed to validate the findings of this study and develop a proof of concept.

A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients.

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D) presents considerable challenges. Accordingly, we present a personalized multitasking framework aimed to forecast blood glucose levels in patients. The patient data was initially categorized according to gender and age and subsequently utilized as input for a modified GRU network model, creating five prediction sub-models. The model hyperparameters were optimized and tuned after introducing the decay factor and incorporating the TCN network and attention mechanism into the GRU model. This step was undertaken to improve the capability of feature extraction. The Ohio T1DM clinical dataset was used to train and evaluate the performance of the proposed model. The metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Clark Error Grid Analysis (EGA), were used to evaluate the performance. The results showed that the average RMSE and the MAE of the proposed model were 16.896 and 9.978 mg/dL, respectively, over the prediction horizon (PH) of 30 minutes. The average RMSE and the MAE were 28.881 and 19.347 mg/dL, respectively, over the PH of 60 min. The proposed model demonstrated excellent prediction accuracy. In addition, the EGA analysis showed that the proposed model accurately predicted 30-minute and 60-minute PH within zones A and B, demonstrating that the framework is clinically feasible. The proposed personalized multitask prediction model in this study offers robust assistance for clinical decision-making, playing a pivotal role in improving the outcomes of individuals with diabetes.

Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas.

A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study.

Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information. To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh. Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values. Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly. This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain

This paper focuses on exploring and comparing different machine learning algorithms in the context of diabetes management. The aim is to understand their characteristics, mathematical foundations, and practical implications specifically for predicting blood glucose levels. The study provides an overview of the algorithms, with a particular emphasis on deep learning techniques such as Long Short-Term Memory Networks. Efficiency is a crucial factor in practical machine learning applications, especially in the context of diabetes management. Therefore, the paper investigates the trade-off between accuracy, resource utilisation, time consumption, and computational power requirements, aiming to identify the optimal balance. By analysing these algorithms, the research uncovers their distinct behaviours and highlights their dissimilarities, even when their analytical underpinnings may appear similar.

Correlational analysis between salivary and blood glucose levels in individuals with and without diabetes mellitus: a cross-sectional study

Objective To estimate the association of patient-related demographic, socioeconomic status, physical activity, stress, and dietary factors influencing the relationship between salivary and blood glucose levels in individuals with and without diabetes mellitus (DM). Method This cross-sectional study was conducted on 166 participants with and without DM. Saliva and blood were collected to estimate the glucose levels. Age, gender, occupation, socioeconomic and education level, BMI, hip to waist circumference, stress, dietary pattern, lifestyle, physical activity, family history of diabetes, and type of diabetes were recorded. The association of saliva to predict blood glucose levels was analysed using Spearman Rank Correlation and how these patient-related factors influence the correlation was estimated for future machine learning models. The difference in medians for various groups was calculated using the Mann-Whitney U Test or Kruskal Wallis Test. Results Blood glucose level is not significantly correlated to salivary glucose level. However, a statistically significant difference in the median blood glucose levels for diabetic participants (median = 137) compared to healthy controls (p-value < .05) was noted. The correlation between blood and salivary glucose was more positive for higher levels of glucose (Spearman 0.4). Age, alcohol consumption, monthly wages, intake of vegetables, and socioeconomic status affect blood glucose levels. Conclusion A correlation between saliva and blood glucose levels in healthy individuals was weak. Saliva should only be used as a monitoring tool rather than a diagnostic tool and is more reliable for patients with poorly controlled diabetes mellitus.

Electrochemical reservoir computing based on surface-functionalized carbon nanotubes

Reservoir computing, a type of neural network computing, is an energy-efficient and fast machine learning method, expected to realize low-power edge processing in sensor devices. This study constructs an electrochemical reservoir using carbon nanotube (CNT) thin-film electrodes on flexible plastic and proves the capability of the electrochemical functionalization of CNT thin films to improve the reservoir's performance. The performance of the time-series data prediction task, called the nonlinear autoregressive moving average (NARMA) task, can be improved using multiple CNT electrodes functionalized under different conditions. Based on the correlation coefficient analysis of the reservoir output, we illustrate that the reservoir dimensionality is enhanced by combining CNT electrodes with different functionalization conditions. Furthermore, the dimensionality and memory capacity of the reservoir can be improved using the displacement current of the electric double layer and the redox current in the electrochemical electrode. We demonstrate the time-series data prediction of blood glucose levels in patients with type 1 diabetes using a physical reservoir for the first time. The use of functionalized CNTs enables flexible and high-performance reservoir computing, leading to the low-power in-sensor edge processing of wearable sensors.

A self-management system for preventing hyperglycemia through blood glucose level prediction and nudge-based food amount reduction.

In recent years, the number of diabetic patients has been increasing rapidly all over the world. Diabetes cannot be completely cured once it develops, so it is important to prevent diabetes before it develops. To prevent the onset of diabetes, it is necessary to avoid prolonged hyperglycemia after meals. In this paper, we propose a self-management system to help users prevent diabetes. The system monitors blood glucose levels in real time, calculates foods to be reduced taking into account the user's preferences, and presents them to the user as soon as the system predicts that the planned diet will cause high blood glucose. We designed and conducted two experiments to show the effectiveness of the proposed system. Experiment 1 was to construct and evaluate a model for predicting blood glucose levels two hours later. Experiment 2 was to evaluate the degree of satisfaction with the food and recommendations, and the acceptability of the recommendations by participants who actually used the proposed system. The results of Experiment 1 showed that the constructed model was able to predict blood glucose levels with an RMSE of 7.66 and MAE of 4.66. As a result of Experiment 2, we found the recommended intake was more acceptable if it reflected the user's preferences.

Glucose Monitoring using Deep Learning

Abstract: Diabetes is a chronic disease that occurs when blood glucose levels are abnormal. Blood glucose is the carbohydrate in the blood that provides the body with energy to perform biological work. The carbohydrate levels in the blood change dynamically depending on various factors such as exercising and fasting. Blood glucose is crucial to healthy metabolic function within the human body and thus is essential to monitor the blood glucose levels of diabetic patients. Various methods of measurement are in use to report glucose levels ranging from the Yellow Spring Instruments 2300, the gold standard, to handheld glucose monitors, also called continuous glucose monitors (CGMs), a compact medical system that continuously monitors glucose levels in more or lessreal-time. Diabetes is a fast-growing global problem with huge social, health, and economic consequences. The estimates in 2019 showed that 77 million individuals had diabetes in India which is expected to rise to over 134 million by 2045. Considering these facts there is a need to build a better system to control diabetes. Monitoring the patient glucose levels daily and keeping him updated with their health every day will help them to come out of the danger even a little bit. Predicting blood glucose levels and displaying them on a website to help ease monitoring for diabetic patients, along with notifications and alerts about their health condition, diet and exercise recommendations, etc. The diabetic patient’s data is used to predict glucose levels using machine learning. The glucose levels are predicted continuously and displayed in a graphical form on the website. Depending on the glucose levels, necessary suggestions and pieces of advice are provided. By displaying the glucose levels, user will be able to know about their health status and be able to take sufficient measures to lead a happy life. The advanced features will be that include connections of patients with personal doctors, notifications and alerts about their condition, diet and exercise recommendations, etc.

Utilizing deep neural network for web-based blood glucose level prediction system

Machine learning algorithms can be used to forecast future blood glucose (BG) levels for diabetes patients, according to recent studies. In this study, dataset from continuous glucose monitoring (CGM) system was used as the sole input for the machine learning models. To forecast blood glucose levels 15, 30, and 45 minutes in the future, we suggested deep neural network (DNN) and tested it on 7 patients with type 1 diabetes (T1D). The suggested prediction model was evaluated against a variety of machine learning models, such as k-nearest neighbor (KNN), support vector regression (SVR), decision tree (DT), adaptive boosting (AdaBoost), random forest (RF), and eXtreme gradient boosting (XGBoost). The experimental findings demonstrated that the proposed DNN model outperformed all other models, with average root mean square errors (RMSEs) of 17.295, 25.940, and 35.146 mg/dL over prediction horizons (PHs) of 15, 30, and 45 minutes, respectively. Additionally, we have included the suggested prediction model in web-based blood glucose level prediction tools. By using this web-based system, patients may readily acquire their future blood glucose levels, allowing for the generation of preventative alarms prior to crucial hypoglycemia or hyperglycemic situations.

Integration of the Vision of People With Diabetes Into the Development Process to Improve Self-management via Diabetes Apps: Qualitative Interview Study

Diabetes is a major global epidemic and serious public health problem. Diabetes self-management is a 24/7 challenge for people with type 1 diabetes that influences their quality of life (QoL). Certain apps can support the self-management of people with diabetes; however, current apps do not meet the needs of people with diabetes appropriately, and their safety is not ensured. Moreover, there are a multitude of hardware and software problems associated with diabetes apps and regulations. Clear guidelines are required to regulate medical care via apps. In Germany, apps must undergo 2 examination processes to be listed in the Digitale Gesundheitsanwendungen directory. However, neither examination process considers whether the medical use of the apps is sufficient for users' self-management. This study aims to contribute to the technology development process of diabetes apps by exploring individual perspectives on desired features and content of diabetes apps among people with diabetes. The vision assessment conducted is a first step toward creating a shared vision among all relevant stakeholders. To ensure adequate research and development processes for diabetes apps in the future, guiding visions from all relevant stakeholders are required. In a qualitative study, 24 semistructured interviews with patients with type 1 diabetes were conducted, among whom 10 (42%) were currently using an app. To clarify the perceptions of people with diabetes regarding the functions and content of diabetes apps, a vision assessment was conducted. People with diabetes have concrete ideas of features and content in apps to improve their QoL and allow them to live as comfortably as possible, such as informative predictions through artificial intelligence, improvements in signal loss and value delay through smartwatches, improved communication and information-sharing capabilities, reliable information sources, and user-friendly and discreet messaging options through smartwatches. In addition, according to people with diabetes, future apps should show improved sensors and app connectivity to avoid incorrect values being displayed. They also wish for an explicit indication that displayed values are delayed. In addition, personalized information was found to be lacking in apps. People with type 1 diabetes want future apps to improve their self-management and QoL and reduce stigma. Desired key features include personalized artificial intelligence predictions of blood glucose levels, improved communication and information sharing through chat and forum options, comprehensive information resources, and smartwatch alerts. A vision assessment is the first step in creating a shared vision among stakeholders to responsibly guide the development of diabetes apps. Relevant stakeholders include patient organizations, health care professionals, insurers, policy makers, device manufacturers, app developers, researchers, medical ethicists, and data security experts. After the research and development process, new apps must be launched while considering regulations regarding data security, liability, and reimbursement.

Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion.

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis's congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.

Analyzing the Performance of Transformers for the Prediction of the Blood Glucose Level Considering Imputation and Smoothing

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: XGBoost, a one-dimensional convolutional neural network (1D-CNN), and the Transformer model to predict future blood glucose levels for a 30-min horizon using a 60-min time series history in the OhioT1DM dataset. We also compare four methods of handling missing time series data during the model training: hourly mean, linear interpolation, cubic interpolation, and spline interpolation; and two smoothing techniques: Kalman smoothing and smoothing splines. Our experiments show that the Transformer performs better than XGBoost and 1D-CNN when only continuous glucose monitoring (CGM) is used as a predictor, and that it is very competitive against XGBoost when CGM and carbohydrate intake from the meal are used to predict blood glucose level. Overall, our results are more accurate than those appearing in the literature.