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Langarica S, de la Vega D, Cariman N, Miranda M, Andrade DC, Núñez F, Rodriguez-Fernandez M. Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:467-475. [PMID: 38899015 PMCID: PMC11186642 DOI: 10.1109/ojemb.2024.3365290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 11/13/2023] [Accepted: 02/05/2024] [Indexed: 06/21/2024] Open
Abstract
Accurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictive models for glucose levels. This study harnesses physiological data collected from wearable sensors to construct a series of data-driven models based on deep learning approaches. We systematically compare these models to offer insights for practitioners and researchers venturing into glucose prediction using deep learning techniques. Key questions addressed in this work encompass the comparison of various deep learning architectures for this task, determining the optimal set of input variables for accurate glucose prediction, comparing population-wide, fine-tuned, and personalized models, and assessing the impact of an individual's data volume on model performance. Additionally, as part of our outcomes, we introduce a meticulously curated dataset inclusive of data from both healthy individuals and those with diabetes, recorded in free-living conditions. This dataset aims to foster research in this domain and facilitate equitable comparisons among researchers.
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Affiliation(s)
- Saúl Langarica
- Department of Electrical EngineeringPontificia Universidad Católica de ChileSantiago7820436Chile
| | - Diego de la Vega
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological SciencesPontificia Universidad Católica de ChileSantiago7820436Chile
| | - Nawel Cariman
- Department of Electrical EngineeringPontificia Universidad Católica de ChileSantiago7820436Chile
| | - Martín Miranda
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological SciencesPontificia Universidad Católica de ChileSantiago7820436Chile
| | - David C. Andrade
- Centro de Investigación en Fisiología y Medicina de Altura, Facultad de Ciencias de la SaludUniversidad de AntofagastaAntofagasta1271155Chile
| | - Felipe Núñez
- Department of Electrical EngineeringPontificia Universidad Católica de ChileSantiago7820436Chile
| | - Maria Rodriguez-Fernandez
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological SciencesPontificia Universidad Católica de ChileSantiago7820436Chile
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Chellamuthu Kalaimani S, Jeyakumar V. Hardware design for blood glucose control based on the Sorensen diabetic patient model using a robust evolving cloud-based controller. Comput Methods Biomech Biomed Engin 2023:1-22. [PMID: 37909209 DOI: 10.1080/10255842.2023.2275545] [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: 07/07/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
Diabetes Mellitus (DM) is the most hazardous public health challenge requiring engineering study to prevent disease complications. In this paper, a Sorensen-based diabetic model is presented in which the insulin-glucose process of a Type 1 patient is maintained by considering other factors such as physical characteristics and changes in mental aspects of the diabetic patient. The purpose of the research is to include a non-linear model of a patient with diabetes who is affected by stress, meals, exercise, and Insulin Sensitivity (IS), and a suitable RECCo controller is designed as a notable recent innovation that implements the concept of ANYA fuzzy rule-based system, which is an online adaptive type of controller that is used in this research work with an uncertainty case of the condition, where the blood glucose must be regulated. To ensure the performance of the proposed controller, a simple insulin pump is designed in a practical case, and a hardware experiment is conducted. The result of the hardware is analyzed and shows the success of the implementation of the controller in blood glucose regulation, thereby preventing complications such as hypoglycemia and hyperglycemia. The comparison analysis of RECCo was performed with other types of controllers, such as MPC and MRAC. The accuracy of the model was validated using the N-BEATS algorithm with a data-set collected from the simulated model, which is around 98%.
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Affiliation(s)
| | - Vijay Jeyakumar
- Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
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Emerson H, Guy M, McConville R. Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes. J Biomed Inform 2023; 142:104376. [PMID: 37149275 DOI: 10.1016/j.jbi.2023.104376] [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: 12/19/2022] [Revised: 03/23/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL for developing effective dosing policies without the need for potentially dangerous patient interaction during training. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of the 30 virtual patients available within the FDA-approved UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the total training samples required by online RL to achieve stable performance, this work shows that offline RL can significantly increase time in the healthy blood glucose range from 61.6±0.3% to 65.3±0.5% when compared to the strongest state-of-art baseline (p<0.001). This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging control scenarios such as incorrect bolus dosing, irregular meal timings and compression errors. The code for this work is available at: https://github.com/hemerson1/offline-glucose.
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Affiliation(s)
- Harry Emerson
- University of Bristol, 1 Cathedral Square, Bristol, BS1 5TS, United Kingdom.
| | - Matthew Guy
- University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, Hampshire, United Kingdom.
| | - Ryan McConville
- University of Bristol, 1 Cathedral Square, Bristol, BS1 5TS, United Kingdom.
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Acharya D, Das DK. Extended Kalman filter state estimation–based nonlinear explicit model predictive control design for blood glucose regulation of type 1 diabetic patient. Med Biol Eng Comput 2022; 60:1347-1361. [DOI: 10.1007/s11517-022-02511-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/18/2022] [Indexed: 10/18/2022]
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Verma A, Agarwal G, Gupta AK. A novel generalized fuzzy intelligence-based ant lion optimization for internet of things based disease prediction and diagnosis. CLUSTER COMPUTING 2022; 25:3283-3298. [PMID: 35228830 PMCID: PMC8868039 DOI: 10.1007/s10586-022-03565-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 01/31/2022] [Accepted: 02/12/2022] [Indexed: 05/15/2023]
Abstract
In the modern healthcare system, the function of the Internet of Things (IoT) and the data mining methods with cloud computing plays an essential role in controlling a large number of big data for predicting and diagnosing various categories of diseases. However, when the patients suffer from more than one disease, the physician may not identify it properly. Therefore, in this research, the predictive method using the cloud with IoT-based database is proposed for forecasting the diseases that utilized the biosensors to estimate the constraints of patients. In addition, a novel Generalized Fuzzy Intelligence-based Ant Lion Optimization (GFIbALO) classifier along with a regression rule is proposed for predicting the diseases accurately. Initially, the dataset is filtered and feature extracted using the regression rule that data is processed on the proposed GFIbALO approach for classifying diseases. Moreover, suppose the patient has been affected by any diseases, in that case, the warning signal will be alerted to the patients via text or any other way, and the patients can get advice from doctors or any other medical support. The implementation of the proposed GFIbALO classifier is done with the use of the MATLAB tool. Subsequently, the results from the presented model are compared with state of the art techniques, and it shows that the presented method is more beneficial in diagnosis and disease forecast.
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Affiliation(s)
- Ankit Verma
- Department of Computer Science, Invertis University, Uttar Pradesh, Bareilly, 243123 India
- Department of Computer Applications, KIET Group of Institutions, Delhi-NCR, Ghaziabad, 201206 India
| | - Gaurav Agarwal
- Department of Computer Science, Invertis University, Uttar Pradesh, Bareilly, 243123 India
| | - Amit Kumar Gupta
- Department of Computer Science, Invertis University, Uttar Pradesh, Bareilly, 243123 India
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Acharya D, Das DK. An efficient nonlinear explicit model predictive control to regulate blood glucose in type-1 diabetic patient under parametric uncertainties. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Rahmanian F, Asemani MH, Dehghani M, Mobayen S. Robust dynamic output feedback control of blood glucose level in diabetic rat with robust descriptor Kalman filter. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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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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Rahmanian F, Dehghani M, Karimaghaee P, Mohammadi M, Abolpour R. Hardware-in-the-loop control of glucose in diabetic patients based on nonlinear time-varying blood glucose model. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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An Adaptive Control Scheme for Interleukin-2 Therapy. iScience 2020; 23:101663. [PMID: 33134893 PMCID: PMC7588844 DOI: 10.1016/j.isci.2020.101663] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/03/2020] [Accepted: 10/07/2020] [Indexed: 11/21/2022] Open
Abstract
Regulatory T cells (Treg) are suppressor cells that control self-reactive and excessive effector conventional T helper cell (Tconv) responses. Breakdown of the balance between Tregs and Tconvs is a hallmark of autoimmune and inflammatory diseases. Interleukin-2 (IL-2) is a growth factor for both populations and subtle leverage to restore the healthy immune balance in IL-2 therapy. By using a mechanistic mathematical model, we introduced an adaptive control strategy to design the minimal therapeutic IL-2 dosage required to increase and stabilize Treg population and restrict inflammatory response. This adaptive protocol allows for dose adjustments based on the feedback of the immune kinetics of the patient. Our simulation results showed that a minimal Treg population was required to restrict the transient side effect of IL-2 injections on the effector Tconv response. In silico results suggested that a combination of IL-2 and adoptive Treg transfer therapies can limit this side effect. An adaptive dosing strategy for IL-2 therapy is introduced and analyzed in silico IL-2 injections can be tuned to increase and stabilize regulatory T-cell numbers Immunosuppressive IL-2 therapy may transiently exacerbate effector T-cell responses Combined IL-2 and adoptive regulatory T-cell therapy can safely limit inflammation
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Orozco-López O, Rodríguez-Herrero A, Castañeda CE, García-Sáez G, Elena Hernando M. Method to generate a large cohort in-silico for type 1 diabetes. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105523. [PMID: 32442845 DOI: 10.1016/j.cmpb.2020.105523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 04/06/2020] [Accepted: 04/25/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE In the last decade, several technological solutions have been proposed as artificial pancreas systems able to treat type 1 diabetes; most often they are built based on a control algorithm that needs to be validated before it is used with real patients. Control algorithms are usually tested with simulation tools that integrate mathematical models related mainly to the glucose-insulin dynamics, but other variables can be considered as well. In general, the simulators have a limited set of subjects. The main goal of this paper is to propose a new computational method to increase the number of virtual subjects, with physiological characteristics, included in the original mathematical models. METHODS A subject is defined by a set of parameters given by a mathematical model. From the available reduced number of subjects in the model, the covariance of each parameter of every subject is obtained to establish a mathematical relationship. Then, new sets of parameters are calculated using linear regression methods; this generates larger cohorts, which allows for testing insulin therapies in open-loop or closed-loop scenarios. The new method proposed here increases the number of subjects in a virtual cohort using two versions of Hovorka's mathematical model. RESULTS Two covariant cohorts are obtained with linear regression. Both cohorts are clustered to avoid overlapping in the glucose-insulin dynamics and are compared in terms of their qualitative and quantitative behaviours in the normoglycemic range. As a result, there have been generated two larger cohorts (256 subjects) than the original population, which contributes to improving the variability in in-silico tests. In addition, for analysing the characteristics of the covariant generation method, two random cohorts have been generated, where the parameters are obtained individually and independently from each other, exhibiting only distribution limitations so that these cohorts do not have physiological subjects. CONCLUSIONS The proposed methodology has enabled the generation of a large cohort of 256 subjects, with different characteristics that are plausible in the T1DM population, significantly increasing the number of available subjects in existing mathematical models. The proposed methodology does not limit the number of subjects that can be generated and thus, it can be used to increase the number of cohorts provided by other mathematical models in diabetes, or even other scientific problems.
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Affiliation(s)
- Onofre Orozco-López
- Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Col Paseos de la Montaña Lagos de Moreno Jalisco MX. 47460, Mexico
| | - Agustín Rodríguez-Herrero
- Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Carlos E Castañeda
- Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Col Paseos de la Montaña Lagos de Moreno Jalisco MX. 47460, Mexico.
| | - Gema García-Sáez
- Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - M Elena Hernando
- Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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Belmon AP, Auxillia J. An adaptive technique based blood glucose control in type-1 diabetes mellitus patients. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3371. [PMID: 32453489 DOI: 10.1002/cnm.3371] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 05/08/2020] [Accepted: 05/13/2020] [Indexed: 06/11/2023]
Abstract
This study proposes Grasshopper Optimization Algorithm (GOA) based type 1 diabetes mellitus system utilizing the nonlinear Bergman minimal model with proportional integral derivative (PID) controller. GOA is the optimization algorithm, which is utilized for selecting the optimized tuning parameters of the PID controller also solves the nonlinear system parameter identification problem. The novelty of the proposed study is to stabilize the glucose level in blood for type 1 diabetic patients by infusion of insulin in reduced time with optimal quantity. Without any intervention to the normal activities of patients, the supply of insulin injection and glucose monitoring is performed automatically for type 1 diabetic patients using this controller. In between the measured variable and set point, the difference is calculated by the PID controller to evaluate an error values. In realistic patient oriented conditions, the control performance evaluation, control optimization, and advanced patient modelling should be highly concentrated during the research/analysis on blood glucose control. Evaluation is performed to analyze control performances and implementation is done on Simulink/MATLAB environment. The performance analysis of the type 1 diabetes mellitus system with GOA technique is also discussed and to improve the control performance, to optimize the controller parameters. The simulation results have proved the substantial improvement in the performance of proposed algorithm with the better results achieved than the other conventional controllers such as PSO-PID and EHO-PID.
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Affiliation(s)
- Anchana P Belmon
- Department of ECE, Maria College of Engineering & Technology, Attoor, India
| | - Jeraldin Auxillia
- Department of ECE, St. Xavier's Catholic College of Engineering, Chunkankadai, India
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Kapil S, Saini R, Wangnoo S, Dhir S. Artificial Pancreas System for Type 1 Diabetes—Challenges and Advancements. EXPLORATORY RESEARCH AND HYPOTHESIS IN MEDICINE 2020; 000:1-11. [DOI: 10.14218/erhm.2020.00028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Mirzaee A, Dehghani M, Mohammadi M. Robust LPV control design for blood glucose regulation considering daily life factors. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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