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1دورية أكاديمية
المؤلفون: Alvis Cabrera, Ernesto Estremera, Aleix Beneyto, Lyvia Biagi, Iván Contreras, Josep Antoni Martín-Fernández, Josep Vehí
المصدر: Mathematics, Vol 11, Iss 21, p 4517 (2023)
مصطلحات موضوعية: compositional data, decision support system, diabetes type 1, blood glucose prediction, Mathematics, QA1-939
الوصف: 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 predictions, we estimate the minimum and maximum glucose values to provide future glycemic status. The proposed methodology has been validated using a dataset of 226 real adult patients with type 1 diabetes (Replace BG (NCT02258373)). The obtained results show a median balanced accuracy and sensitivity of over 90% and 80%, respectively. A information system has been implemented and validated to update patients on their glycemic status and associated risks for the next few hours.
وصف الملف: electronic resource
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2دورية أكاديمية
المؤلفون: Alvis Cabrera, Lyvia Biagi, Aleix Beneyto, Ernesto Estremera, Iván Contreras, Marga Giménez, Ignacio Conget, Jorge Bondia, Josep Antoni Martín-Fernández, Josep Vehí
المصدر: Mathematics, Vol 11, Iss 5, p 1241 (2023)
مصطلحات موضوعية: compositional data, continuous glucose monitoring, prediction model, time in range, type 1 diabetes, Mathematics, QA1-939
الوصف: Glycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works presented a tool for the individual categorization of days and proposed a probabilistic transition model between categories, although validation has hitherto not been presented. In this study, we consider data from eight real adult patients with T1D obtained from continuous glucose monitoring (CGM) sensors and introduce a methodology based on compositional methods to validate the previously presented probability transition model. We conducted 5-fold cross-validation, with both the training and validation data being CoDa vectors, which requires developing new performance metrics. We design new accuracy and precision measures based on statistical error calculations. The results show that the precision for the entire model is higher than 95% in all patients. The use of a probabilistic transition model can help doctors and patients in diabetes treatment management and decision-making. Although the proposed method was tested with CoDa applied to T1D data obtained from CGM, the newly developed accuracy and precision measures apply to any other data or validation based on CoDa.
وصف الملف: electronic resource
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3دورية أكاديمية
المؤلفون: Lyvia Biagi, Arthur Bertachi, Marga Giménez, Ignacio Conget, Jorge Bondia, Josep Antoni Martín-Fernández, Josep Vehí
المصدر: Sensors, Vol 21, Iss 11, p 3593 (2021)
مصطلحات موضوعية: type 1 diabetes, compositional data analysis, decision support system, diabetes management, probabilistic model of transition, Chemical technology, TP1-1185
الوصف: The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies.
وصف الملف: electronic resource
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4دورية أكاديمية
المؤلفون: Arthur Bertachi, Clara Viñals, Lyvia Biagi, Ivan Contreras, Josep Vehí, Ignacio Conget, Marga Giménez
المصدر: Sensors, Vol 20, Iss 6, p 1705 (2020)
مصطلحات موضوعية: artificial neural network, hypoglycemia, machine learning, support vector machine, type 1 diabetes, multiple daily injections, continuous glucose monitoring, Chemical technology, TP1-1185
الوصف: (1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.
وصف الملف: electronic resource
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5دورية أكاديمية
المؤلفون: Lyvia Biagi, Arthur Bertachi, Carmen Quirós, Marga Giménez, Ignacio Conget, Jorge Bondia, Josep Vehí
المصدر: Biosensors, Vol 8, Iss 1, p 22 (2018)
مصطلحات موضوعية: continuous glucose monitoring, accuracy, exercise, physical activity, type 1 diabetes, Biotechnology, TP248.13-248.65
الوصف: Continuous glucose monitoring (CGM) plays an important role in treatment decisions for patients with type 1 diabetes under conventional or closed-loop therapy. Physical activity represents a great challenge for diabetes management as well as for CGM systems. In this work, the accuracy of CGM in the context of exercise is addressed. Six adults performed aerobic and anaerobic exercise sessions and used two Medtronic Paradigm Enlite-2 sensors under closed-loop therapy. CGM readings were compared with plasma glucose during different periods: one hour before exercise, during exercise, and four hours after the end of exercise. In aerobic sessions, the median absolute relative difference (MARD) increased from 9.5% before the beginning of exercise to 16.5% during exercise (p < 0.001), and then decreased to 9.3% in the first hour after the end of exercise (p < 0.001). For the anaerobic sessions, the MARD before exercise was 15.5% and increased without statistical significance to 16.8% during exercise realisation (p = 0.993), and then decreased to 12.7% in the first hour after the cessation of anaerobic activities (p = 0.095). Results indicate that CGM might present lower accuracy during aerobic exercise, but return to regular operation a few hours after exercise cessation. No significant impact for anaerobic exercise was found.
وصف الملف: electronic resource
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6دورية أكاديمية
المؤلفون: Lyvia Biagi, Charrise M. Ramkissoon, Andrea Facchinetti, Yenny Leal, Josep Vehi
المصدر: Sensors, Vol 17, Iss 6, p 1361 (2017)
مصطلحات موضوعية: continuous glucose monitor, artificial pancreas, type 1 diabetes, sensor error, measurement noise, calibration error, enlite sensor, Chemical technology, TP1-1185
الوصف: Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.
وصف الملف: electronic resource
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7دورية أكاديمية
المؤلفون: Arthur Bertachi, Lyvia Biagi, Aleix Beneyto, Josep Vehí
المصدر: Journal of Healthcare Engineering, Vol 2020 (2020)
مصطلحات موضوعية: Medicine (General), R5-920, Medical technology, R855-855.5
الوصف: The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.
العلاقة: http://dx.doi.org/10.1155/2020/1414597Test; https://doaj.org/toc/2040-2295Test; https://doaj.org/toc/2040-2309Test; https://doaj.org/article/f5a07eac4fbc433ab100b505105f7851Test
الإتاحة: https://doi.org/10.1155/2020/1414597Test
https://doaj.org/article/f5a07eac4fbc433ab100b505105f7851Test -
8Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning
المؤلفون: Lyvia Biagi, Silvia Oviedo, Arthur Bertachi, Josep Vehí, Ivan Contreras
المصدر: Health Informatics Journal. 26:703-718
مصطلحات موضوعية: Decision support system, 020205 medical informatics, medicine.medical_treatment, 030209 endocrinology & metabolism, Health Informatics, 02 engineering and technology, Disease, Machine learning, computer.software_genre, Machine Learning, 03 medical and health sciences, Patient safety, 0302 clinical medicine, Diabetes management, 0202 electrical engineering, electronic engineering, information engineering, Humans, Hypoglycemic Agents, Medicine, In patient, Type 1 diabetes, Artificial neural network, business.industry, Insulin, medicine.disease, Hypoglycemia, Diabetes Mellitus, Type 1, Quality of Life, Artificial intelligence, business, computer
الوصف: Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31fb018ca49ec97ca5c8c4ec66d471b0Test
https://doi.org/10.1177/1460458219850682Test -
9
المصدر: IFAC-PapersOnLine. 52:1006-1011
مصطلحات موضوعية: 0209 industrial biotechnology, Type 1 diabetes, Glucose control, 020208 electrical & electronic engineering, Linear model, 02 engineering and technology, medicine.disease, Coda, 020901 industrial engineering & automation, Categorization, Control and Systems Engineering, Diabetes mellitus, Statistics, 0202 electrical engineering, electronic engineering, information engineering, medicine, Compositional data, Cluster analysis, Mathematics
الوصف: Time spent in different glucose ranges indicate the occurrence of adverse events and measure the quality of glucose control in type one diabetes (T1D) patients. This work proposes a Compositional Data (CoDa) approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles limited to 6-h duration were analyzed at four different times of the day These glucose profiles were distributed into time spent in five glucose ranges, which determine the composition. The log-ratio coordinates of the compositions were categorized through a clustering algorithm, which later made possible the obtainment of a linear model that should be used to predict the category of a 6-h period in different times of day. Leave-one-out cross-validation was performed, achieving an average above 90% of correct classification. A probabilistic model of transition between the category of the past 6-h of glucose to the category of the future 6-h period was obtained. Results show that the CoDa approach not only works as new analysis tool and is suitable for the categorization of glucose profiles, but also is a complementary tool for the prediction of different categories of glucose control. This prediction could assist patients to take correction measures in advance to adverse situations.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::1e79f6c1b071602fec82290bb36cbfe6Test
https://doi.org/10.1016/j.ifacol.2019.06.194Test -
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المؤلفون: Lyvia Biagi, Ivan Contreras, Ignacio Conget, Marga Giménez, Arthur Bertachi, Clara Viñals, Josep Vehí
المساهمون: Ministerio de Economía y Competitividad (Espanya)
المصدر: Sensors
Volume 20
Issue 6
Sensors, Vol 20, Iss 6, p 1705 (2020)
Sensors, 2020, vol. 20, núm. 6, p. 1705
Articles publicats (D-EEEiA)
DUGiDocs – Universitat de Girona
instname
Sensors (Basel, Switzerland)مصطلحات موضوعية: Male, Pediatrics, Remote patient monitoring, type 1 diabetes, medicine.medical_treatment, Diabetes -- Treatment, lcsh:Chemical technology, Biochemistry, Analytical Chemistry, 0302 clinical medicine, Insulin, Diabetis -- Tractament, lcsh:TP1-1185, 030212 general & internal medicine, Instrumentation, Patient monitoring, education.field_of_study, Continuous glucose monitoring, Atomic and Molecular Physics, and Optics, machine learning, Multilayer perceptron, Female, continuous glucose monitoring, Adult, multiple daily injections, medicine.medical_specialty, Drug-Related Side Effects and Adverse Reactions, Population, Blood sugar, 030209 endocrinology & metabolism, Fitness Trackers, Hypoglycemia, Article, 03 medical and health sciences, Insulin Infusion Systems, medicine, Humans, Monitoratge de pacients, support vector machine, Electrical and Electronic Engineering, education, Exercise, Monitoring, Physiologic, Type 1 diabetes, business.industry, medicine.disease, Diabetes Mellitus, Type 1, Glucose, hypoglycemia, Glucèmia, Neural Networks, Computer, business, artificial neural network
الوصف: Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker This work has been partially funded by the Spanish Government (DPI2016-78831-C2-2-R) and the National Council of Technological and Scientific Development, CNPq—Brazil (202050/2015-7 and 207688/2014-1). C.V. is the recipient of a grant from the Hospital Clínic i Universitari of Barcelona (“Premi Fi de Residencia 2018–2019”)
وصف الملف: application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::350f901b67c043b006977a393d0ffe04Test
النص الكامل