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Herrero P, Andorrà M, Babion N, Bos H, Koehler M, Klopfenstein Y, Leppäaho E, Lustenberger P, Peak A, Ringemann C, Glatzer T. Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App. J Diabetes Sci Technol 2024; 18:1014-1026. [PMID: 39158994 PMCID: PMC11418465 DOI: 10.1177/19322968241267818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
BACKGROUND Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations. METHODS The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226). RESULTS On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively. CONCLUSIONS The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.
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Affiliation(s)
- Pau Herrero
- Roche Diabetes Care Spain SL., Barcelona, Spain
| | | | - Nils Babion
- Roche Diabetes Care Deutschland GmbH, Mannheim, Germany
| | - Hendericus Bos
- IBM Client Innovation Center, Groningen, The Netherlands
| | | | | | | | | | | | | | - Timor Glatzer
- Roche Diabetes Care Deutschland GmbH, Mannheim, Germany
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2
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Kulzer B, Freckmann G, Ziegler R, Schnell O, Glatzer T, Heinemann L. Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring. J Diabetes Sci Technol 2024; 18:1052-1060. [PMID: 39158988 PMCID: PMC11418455 DOI: 10.1177/19322968241267823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.
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Affiliation(s)
- Bernhard Kulzer
- Research Institute Diabetes Academy Mergentheim, Bad Mergentheim, Germany
- Diabetes Center Mergentheim, Bad Mergentheim, Germany
- Department of Clinical Psychology and Psychotherapy, University of Bamberg, Bamberg, Germany
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Ralph Ziegler
- Diabetes Clinic for Children and Adolescents, Muenster, Germany
| | - Oliver Schnell
- Forschergruppe Diabetes e.V., Helmholtz Zentrum, Munich, Germany
| | | | - Lutz Heinemann
- Science Consulting in Diabetes GmbH, Düsseldorf, Germany
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Lubasinski N, Thabit H, Nutter PW, Harper S. Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review. Nutrients 2024; 16:2214. [PMID: 39064657 PMCID: PMC11280346 DOI: 10.3390/nu16142214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Revised: 07/06/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
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 caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components. METHOD A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy. RESULTS The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%). CONCLUSION The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.
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Affiliation(s)
- Nicole Lubasinski
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK; (P.W.N.); (S.H.)
| | - Hood Thabit
- Diabetes, Endocrine and Metabolism Centre, Manchester Royal Infirmary, Manchester University NHS, Manchester M13 9WL, UK;
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Science, The University of Manchester, Manchester M13 9NT, UK
| | - Paul W. Nutter
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK; (P.W.N.); (S.H.)
| | - Simon Harper
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK; (P.W.N.); (S.H.)
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4
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Yang G, Liu S, Li Y, He L. Short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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5
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Rodríguez-Rodríguez I, Campo-Valera M, Rodríguez JV, Frisa-Rubio A. Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073665. [PMID: 37050725 PMCID: PMC10099355 DOI: 10.3390/s23073665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/21/2023] [Accepted: 03/28/2023] [Indexed: 06/12/2023]
Abstract
Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.
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Affiliation(s)
| | - María Campo-Valera
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - José-Víctor Rodríguez
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Alberto Frisa-Rubio
- CIRCE—Centro Tecnológico (Research Centre for Energy Resources and Consumption), Av. Ranillas, Edf. Dinamiza 3D, 50018 Zaragoza, Spain
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Naveena S, Bharathi A. Weighted entropy deep features on hybrid RNN with LSTM for glucose level and diabetes prediction. Comput Methods Biomech Biomed Engin 2022; 26:1-25. [PMID: 36448678 DOI: 10.1080/10255842.2022.2149263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 11/15/2022] [Indexed: 12/05/2022]
Abstract
Glucose level regulation with essential advice regarding diabetes must be provided to the patients to maintain their diet for diabetes treatment. Therefore, the academic community has focused on implementing novel glucose prediction techniques for decision support systems. Recent computational techniques for diagnosing diabetes have certain limitations, and also they are not evaluated under various datasets obtained from the different people of various countries. This generates inefficiency in the prediction systems to apply it in real-time applications. This paper plans to suggest a hybrid deep learning model for diabetes prediction and glucose level classification. Two benchmark datasets are used in the data collection process for experimenting. Initially, the deep selected features were extracted by the Convolutional Neural Network (CNN). Further, weighted entropy deep features are extracted, where the tuning of weight is taken place by the Modified Escaping Energy-based Harris Hawks Optimization. These features are processed in the glucose level classification using the modified Fuzzy classifier for classifying the high-level and low-level glucose. Further, glucose prediction is done by the Hybrid Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) termed R-LSTM with parameter optimization. From the experimental result, In the dataset 2 analyses on SMAPE, the MEE-HHO-R-LSTM is 12.5%, 87.5%, 50%, 12.5%, and 2.5% better than SVM, LSTM, DNN, RNN, and RNN-LSTM, at the learning percentage of 75%. The analytical results enforce that the suggested methods attain enhanced prediction performance concerning the evaluation metrics compared to conventional prediction models.
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Affiliation(s)
- Somasundaram Naveena
- Assistant Professor Senior Grade, Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, India
| | - Ayyasamy Bharathi
- Professor, Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, India
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Schönenberger KA, Cossu L, Prendin F, Cappon G, Wu J, Fuchs KL, Mayer S, Herzig D, Facchinetti A, Bally L. Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia. Front Nutr 2022; 9:855223. [PMID: 35464035 PMCID: PMC9021863 DOI: 10.3389/fnut.2022.855223] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/14/2022] [Indexed: 11/29/2022] Open
Abstract
Postbariatric hypoglycemia (PBH) is an increasingly recognized late metabolic complication of bariatric surgery, characterized by low blood glucose levels 1-3 h after a meal, particularly if the meal contains rapid-acting carbohydrates. PBH can often be effectively managed through appropriate nutritional measures, which remain the cornerstone treatment today. However, their implementation in daily life continues to challenge both patients and health care providers. Emerging digital technologies may allow for more informed and improved decision-making through better access to relevant data to manage glucose levels in PBH. Examples include applications for automated food analysis from meal images, digital receipts of purchased food items or integrated platforms allowing the connection of continuously measured glucose with food and other health-related data. The resulting multi-dimensional data can be processed with artificial intelligence systems to develop prediction algorithms and decision support systems with the aim of improving glucose control, safety, and quality of life of PBH patients. Digital innovations, however, face trade-offs between user burden vs. amount and quality of data. Further challenges to their development are regulatory non-compliance regarding data ownership of the platforms acquiring the required data, as well as user privacy concerns and compliance with regulatory requirements. Through navigating these trade-offs, digital solutions could significantly contribute to improving the management of PBH.
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Affiliation(s)
- Katja A. Schönenberger
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Division of Clinical Pharmacy and Epidemiology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Luca Cossu
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Francesco Prendin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giacomo Cappon
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Jing Wu
- Institute of Computer Science, University of St. Gallen, St. Gallen, Switzerland
| | - Klaus L. Fuchs
- ETH AI Center, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
- Technology Studies, School of Humanities and Social Sciences, University of St. Gallen, St. Gallen, Switzerland
| | - Simon Mayer
- Institute of Computer Science, University of St. Gallen, St. Gallen, Switzerland
| | - David Herzig
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Lia Bally
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes. Med Biol Eng Comput 2021; 60:1-17. [PMID: 34751904 DOI: 10.1007/s11517-021-02437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/20/2021] [Indexed: 10/19/2022]
Abstract
Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-based glucose predictive models. We present the accuracy and clinical acceptability of nine different models coming from the literature, from standard autoregressive to more complex neural network-based models. These results are obtained on two different datasets, namely UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and Ohio Type-1 Diabetes Mellitus (OhioT1DM), featuring artificial and real type 1 diabetic patients respectively. By providing extensive details about the data flow as well as by providing the whole source code of the benchmarking process, we ensure the reproducibility of the results and the usability of the benchmark by the community. Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.
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Wenbo W, Yang S, Guici C. Blood glucose concentration prediction based on VMD-KELM-AdaBoost. Med Biol Eng Comput 2021; 59:2219-2235. [PMID: 34510372 DOI: 10.1007/s11517-021-02430-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 08/11/2021] [Indexed: 10/20/2022]
Abstract
The time series of blood glucose concentration in diabetic patients are time-varying, nonlinear, and non-stationary. In order to improve the accuracy of blood glucose prediction, a multi-scale combination short-term blood glucose prediction model was constructed by combining the variational mode decomposition (VMD) method, the kernel extreme learning machine (KELM), and the AdaBoost algorithm (VMD-ELM-AdaBoost). Firstly, the blood glucose concentration series were decomposed into a set of intrinsic modal functions (IMFs) with different scales by the VMD method. On this basis, the KELM neural network and AdaBoost algorithm are combined to predict each IMF component. Finally, the cumulative blood glucose concentration prediction value is obtained by accumulating the KELM-AdaBoost prediction results of each IMF. The time series of measured blood glucose concentration were used for experimental analysis; the experimental results show that the proposed VMD-KELM-AdaBoost method has higher prediction accuracy compared with the classical prediction models such as ELM, KELM, SVM, and LSTM. The proposed VMD-KELM-AdaBoost model can still achieve high prediction accuracy 60 min in advance (the mean values of RMSE, MAPE, and CC are about 10.1422, 4.8629%, and 0.8737 respectively); in Clarke error mesh analysis, the proportion of falling into A region is about 95.7%; the sensitivity and false alarm rate of early alarm of hypoglycemia were 94.8% and 7.7%, respectively. Graphical abstract We have proposed a new prediction model. In the first part, for reducing thenon-stationarity, the data of blood glucose concentration was decomposed as a series ofIMF by VMD. In the second part, a prediction model based KELM and Adaboost wasestablished. In the third part, the KELM-Adaboost model was used to predict each IMF,and the predicted values of all IMFS were superimposed to obtain the final predictionresult of blood glucose concentration.
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Affiliation(s)
- Wang Wenbo
- School of Science, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Shen Yang
- School of Science, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Chen Guici
- School of Science, Wuhan University of Science and Technology, Wuhan, 430065, China
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10
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Faccioli S, Facchinetti A, Sparacino G, Pillonetto G, Del Favero S. Linear Model Identification for Personalized Prediction and Control in Diabetes. IEEE Trans Biomed Eng 2021; 69:558-568. [PMID: 34347589 DOI: 10.1109/tbme.2021.3101589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Type-1 diabetes (T1D) is a metabolic disease, characterized by impaired blood glucose (BG) regulation, which forces patients to multiple daily therapeutic actions, the most critical of which is exogenous insulin administration. T1D management can considerably benefit of mathematical models enabling accurate BG predictions and effective/safe automated insulin delivery. In building these models, dealing with large inter- and intra-patient variability in glucose-insulin dynamics represents a major challenge. The aim of the present work is to assess linear black-box methods, including a novel non-parametric methodology, for learning individualized models of glucose response to insulin and meal, suitable for model-based prediction and control. METHODS We focus on data-driven techniques for linear model-learning and compare the state-of-art parametric pipeline, exploring all its degrees of freedom (including population vs. individualized parameter identification, model class chosen among ARX/ARMAX/ARIMAX/Box-Jenkins, model order selection criteria, etc.), with a novel non-parametric approach based on Gaussian regression and stable spline kernel. By using data collected in 11 T1D individuals, we evaluate effectiveness of the different models by measuring root mean squared error (RMSE), coefficient of determination (COD), and time gain of the associated BG predictors. RESULTS Among the tested approaches, the non-parametric technique results in the best prediction performance: median RMSE=29.8mg/dL, and median COD=57.4%, for a prediction horizon (PH) of 60 min. With respect to the state-of-the-art parametric techniques, the non-parametric approach grants a COD improvement of about 2%, 7%, 21%, and 41% for PH = 30, 60, 90, and 120 min (paired-sample t-test p 0.001, p=0.003, p=0.03, and p=0.07 respectively). CONCLUSION Non-parametric linear model-learning grants statistically significant improvement with respect to the state-of-art parametric approach. The higher PH, the more pronounced the improvement. SIGNIFICANCE The use of a linear non-parametric model-learning approach for model-based prediction and control could bring to a more prompt, safe and effective T1D management.
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11
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Data size considerations and hyperparameter choices in case-based reasoning approach to glucose prediction. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Wang W, Wang S, Wang X, Liu D, Geng Y, Wu T. A Glucose-Insulin Mixture Model and Application to Short-Term Hypoglycemia Prediction in the Night Time. IEEE Trans Biomed Eng 2020; 68:834-845. [PMID: 32776874 DOI: 10.1109/tbme.2020.3015199] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Insulin-induced hypoglycemia is recognized as a critical problem for diabetic patients, especially at night. To give glucose prediction and advance warning of hypoglycemia of at least 30 minutes, various glucose-insulin models have been proposed. Recognizing the complementary nature of the models, this research proposes a Glucose-Insulin Mixture (GIM) model to predict the glucose values for hypoglycemia detection, by optimally fusing different models with its adjusted parameters to address the inter- and intra-individual variability. METHODS Two types of classic glucose-insulin models, the Ruan model, with single-compartment glucose kinetics, and the Hovorka model, with two-compartment glucose kinetics, are selected as two candidate models. Based on Bayesian inference, GIM is introduced with quantified contributions from the models with the associated parameters. GIM is then applied to predict the glucose values and hypoglycemia events. RESULTS The proposed model is validated by the nocturnal glucose data collected from 12 participants with type 1 diabetes. The GIM model has promising fitting of RMSE within 0.3465 mmol/L and predicting of RMSE within 0.5571 mmol/L. According to the literature, the hypoglycemia is defined as 3.9 mmol/L, and the GIM model shows good short-term hypoglycemia prediction performance with the data collected within the last hour (accuracy: 95.97%, precision: 91.77%, recall: 95.60%). In addition, the probability of hypoglycemia event in 30 minutes is inferred. CONCLUSION GIM, by fusing various glucose-insulin models via Bayesian inference, has the promise to capture glucose dynamics and predict hypoglycemia. SIGNIFICANCE GIM based short-term hypoglycemia prediction has potential clinical utility for timely intervention.
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Montaser E, Diez JL, Rossetti P, Rashid M, Cinar A, Bondia J. Seasonal Local Models for Glucose Prediction in Type 1 Diabetes. IEEE J Biomed Health Inform 2019; 24:2064-2072. [PMID: 31796419 DOI: 10.1109/jbhi.2019.2956704] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Linear empirical dynamic models have been widely used for blood glucose prediction and risks prevention in people with type 1 diabetes. More accurate blood glucose prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent blood glucose changes with enough time to take corrective actions. In this study, a blood glucose prediction method is developed by integrating the predictions of a set of seasonal local models (each of them corresponding to different glucose profiles observed along historical data). In the modeling step, the number of sets and their corresponding glucose profiles characteristics are obtained by clustering techniques (Fuzzy C-Means). Then, Box-Jenkins methodology is used to identify a seasonal model for each set. Finally, blood glucose predictions of local models are integrated using different techniques. The proposed method is tested by using 18 60-h closed-loop experiments (including different exercise types and artificial pancreas strategies) and achieving mean absolute percentage error (MAPE) of 2.94%, 3.89%, 5.41%, 6.29% and 8.66% for 15-, 30-, 45-, 60-, and 90-min PHs, respectively.
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Juan CG, García H, Ávila-Navarro E, Bronchalo E, Galiano V, Moreno Ó, Orozco D, Sabater-Navarro JM. Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept. Med Biol Eng Comput 2019; 57:2389-2405. [PMID: 31473945 DOI: 10.1007/s11517-019-02030-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 08/10/2019] [Indexed: 01/05/2023]
Abstract
Self-management of blood glucose level is part and parcel of diabetes treatment, which involves invasive, painful, and uncomfortable methods. A proper non-invasive blood glucose monitor (NIBGM) is therefore desirable to deal better with it. Microwave resonators can potentially be used for such a purpose. Following the positive results from an in vitro previous work, a portable device based upon a microwave resonator was developed and assessed in a multicenter proof of concept. Its electrical response was analyzed when an individual's tongue was placed onto it. The study was performed with 352 individuals during their oral glucose tolerance tests, having four measurements per individual. The findings revealed that the accuracy must be improved before the diabetes community can make real use of the device. However, the relationship between the measuring parameter and the individual's blood glucose level is coherent with that from previous works, although with higher data dispersion. This is reflected in correlation coefficients between glycemia and the measuring magnitude consistently negative, although small, for the different datasets analyzed. Further research is proposed, focused on system improvements, individual calibration, and multitechnology approach. The study of the influence of other blood components different to glucose is also advised. Graphical abstract.
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Affiliation(s)
- Carlos G Juan
- Department of Systems Engineering and Automation, Miguel Hernández University, Elche, Spain
| | - Héctor García
- Department of Materials Science, Optics and Electronic Technology, Miguel Hernández University, Elche, Spain
| | - Ernesto Ávila-Navarro
- Department of Materials Science, Optics and Electronic Technology, Miguel Hernández University, Elche, Spain
| | - Enrique Bronchalo
- Department of Communications Engineering, Miguel Hernández University, Elche, Spain
| | - Vicente Galiano
- Department of Computer Engineering, Miguel Hernández University, Elche, Spain
| | - Óscar Moreno
- Department of Clinical Medicine, Miguel Hernández University, Elche, Spain
| | - Domingo Orozco
- Department of Clinical Medicine, Miguel Hernández University, Elche, Spain
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Glucose Concentration Measurement in Human Blood Plasma Solutions with Microwave Sensors. SENSORS 2019; 19:s19173779. [PMID: 31480415 PMCID: PMC6749577 DOI: 10.3390/s19173779] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/27/2019] [Accepted: 08/28/2019] [Indexed: 12/29/2022]
Abstract
Three microwave sensors are used to track the glucose level of different human blood plasma solutions. In this paper, the sensors are evaluated as glucose trackers in a context close to real human blood. Different plasma solutions sets were prepared from a human blood sample at several added glucose concentrations up to 10 wt%, adding also ascorbic acid and lactic acid at different concentrations. The experimental results for the different sensors/solutions combinations are presented in this work. The sensors show good performance and linearity as glucose level retrievers, although the sensitivities change as the rest of components vary. Different sensor behaviors depending upon the concentrations of glucose and other components are identified and characterized. The results obtained in terms of sensitivity are coherent with previous works, highlighting the contribution of glucose to the dielectric losses of the solution. The results are also consistent with the frequency evolution of the electromagnetic signature of glucose found in the literature, and are helpful for selecting frequency bands for sensing purposes and envisioning future approaches to the challenging measurement in real biological contexts. Discussion of the implications of the results and guidelines for further research and development of more accurate sensors is offered.
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