1
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Dodek M, Miklovičová E. Estimation of process noise variances from the measured output sequence with application to the empirical model of type 1 diabetes. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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2
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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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3
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Mosquera-Lopez C, Wilson LM, El Youssef J, Hilts W, Leitschuh J, Branigan D, Gabo V, Eom JH, Castle JR, Jacobs PG. Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence. NPJ Digit Med 2023; 6:39. [PMID: 36914699 PMCID: PMC10011368 DOI: 10.1038/s41746-023-00783-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of 83.3%, false discovery rate of 16.6%, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose >180 mg/dL) by 10.8% (P = 0.04) and trends toward increasing time in range (70-180 mg/dL) by 9.1% compared with MPC. Time below range (glucose <70 mg/dL) is not significantly different between RAP and MPC.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Wade Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph Leitschuh
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Deborah Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Virginia Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jae H Eom
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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4
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Sanz R, García P, Romero-Vivó S, Díez JL, Bondia J. Near-optimal feedback control for postprandial glucose regulation in type 1 diabetes. ISA TRANSACTIONS 2023; 133:345-352. [PMID: 36116963 DOI: 10.1016/j.isatra.2022.06.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 04/19/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
This paper is focused on feedback control of postprandial glucose levels for patients with type 1 Diabetes Mellitus. There are two important limitations that make this a challenging problem. First, the slow subcutaneous insulin pharmacokinetics that introduces a significant lag into the control loop. Second, the positivity constraint on the control action, meaning that it is not possible to remove insulin from the body. In this paper, both issues are explicitly considered in the design process using the internal model control framework, to derive a near-optimal feedback controller. Optimality is understood here as minimizing the blood glucose peak after a meal intake and, at the same time, preventing glucose values below a prescribed threshold. It is shown how the proposed controller approaches the optimal closed-loop performance as a limit case. The theoretical results are supported by a numerical example and the feasibility of the overall strategy under uncertainties is illustrated using an extended version UVa/Padova metabolic simulator.
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Affiliation(s)
- R Sanz
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
| | - P García
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
| | - S Romero-Vivó
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - J L Díez
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - J Bondia
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
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5
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Sun X, Rashid M, Askari MR, Cinar A. Adaptive Personalized Prior-Knowledge-Informed Model Predictive Control for Type 1 Diabetes. CONTROL ENGINEERING PRACTICE 2023; 131:105386. [PMID: 36506413 PMCID: PMC9730892 DOI: 10.1016/j.conengprac.2022.105386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
This work considers the problem of adaptive prior-informed model predictive control (MPC) formulations that explicitly incorporate prior knowledge in the model development and is robust to missing data in the output measurements. The proposed prediction model is based on a latent variables model to extract glycemic dynamics from highly-correlated data and incorporates prior knowledge of exponential stability to improve the prediction ability. Missing data structures are formulated to enable model predictions when output measurements are missing for short periods of time. Based on the latent variables model, the MPC strategy and adaptive rules are developed to automatically tune the aggressiveness of the MPC. The adaptive prior-knowledge-informed MPC is evaluated with computer simulations for the control of blood glucose concentrations in people with Type 1 diabetes (T1D) using simulated virtual patients. Due to the variability among people with T1D, the hyperparameters of the prior-knowledge-informed model are personalized to individual subjects. The percentage of time spent in the target range is 76.48% when there are no missing data and 76.52% when there are missing data episodes lasting up to 30 mins (6 samples). Incorporating the adaptive rules further improves the percentage of time in target range to 84.58% and 84.88% for cases with no missing data and missing data, respectively. The proposed adaptive prior-informed MPC formulation provides robust, effective, and safe regulation of glucose concentration in T1D despite disturbances and missing measurements.
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Affiliation(s)
- Xiaoyu Sun
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, 60616, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, 60616, IL, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, 60616, IL, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, 60616, IL, USA
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, 60616, IL, USA
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6
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Rodríguez-Sarmiento DL, León-Vargas F, García-Jaramillo M. Artificial pancreas systems: experiences from concept to commercialisation. Expert Rev Med Devices 2022; 19:877-894. [DOI: 10.1080/17434440.2022.2150546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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7
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Viroonluecha P, Egea-Lopez E, Santa J. Evaluation of blood glucose level control in type 1 diabetic patients using deep reinforcement learning. PLoS One 2022; 17:e0274608. [PMID: 36099285 PMCID: PMC9469983 DOI: 10.1371/journal.pone.0274608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/30/2022] [Indexed: 11/18/2022] Open
Abstract
Diabetes mellitus is a disease associated with abnormally high levels of blood glucose due to a lack of insulin. Combining an insulin pump and continuous glucose monitor with a control algorithm to deliver insulin is an alternative to patient self-management of insulin doses to control blood glucose levels in diabetes mellitus patients. In this work, we propose a closed-loop control for blood glucose levels based on deep reinforcement learning. We describe the initial evaluation of several alternatives conducted on a realistic simulator of the glucoregulatory system and propose a particular implementation strategy based on reducing the frequency of the observations and rewards passed to the agent, and using a simple reward function. We train agents with that strategy for three groups of patient classes, evaluate and compare it with alternative control baselines. Our results show that our method is able to outperform baselines as well as similar recent proposals, by achieving longer periods of safe glycemic state and low risk.
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Affiliation(s)
- Phuwadol Viroonluecha
- Universidad Politecnica de Cartagena (UPCT), Department of Information Technologies and Communications, Cartagena, Spain
- * E-mail:
| | - Esteban Egea-Lopez
- Universidad Politecnica de Cartagena (UPCT), Department of Information Technologies and Communications, Cartagena, Spain
| | - Jose Santa
- Universidad Politecnica de Cartagena (UPCT), Department of Electronics, Computer Technology and Projects, Cartagena, Spain
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8
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Zahedifar R, Keymasi Khalaji A. Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm. Sci Rep 2022; 12:12228. [PMID: 35851835 PMCID: PMC9293929 DOI: 10.1038/s41598-022-16535-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 07/12/2022] [Indexed: 11/24/2022] Open
Abstract
In this study, an adaptive backstepping method is proposed to regulate the blood glucose induced by meals for type-1 diabetic patients. The backstepping controller is used to control the blood glucose level and an adaptive algorithm is utilized to compensate for the blood glucose induced by meals. Moreover, the effectiveness of the proposed method is evaluated by comparing results in two different case studies: in the presence of actuator faults and the loss of control input for a short while during treatment. Effects of unannounced meals three times a day are investigated for a nominal patient in every case. It is argued that adaptive backstepping is the preferred control method in either case. The Lyapunov theory is used to prove the stability of the proposed method. Obtained results, indicated that the adaptive backstepping controller is stable, and the desired level of glucose concentration is being tracked efficiently.
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Affiliation(s)
- Rasoul Zahedifar
- Department of Mechanical Engineering, Faculty of Engineering, Kharazmi University, Tehran, P.O.B. 15719-14911, Iran
| | - Ali Keymasi Khalaji
- Department of Mechanical Engineering, Faculty of Engineering, Kharazmi University, Tehran, P.O.B. 15719-14911, Iran.
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Sun X, Rashid M, Hobbs N, Askari MR, Brandt R, Shahidehpour A, Cinar A. Prior Informed Regularization of Recursively Updated Latent-Variables-Based Models with Missing Observations. CONTROL ENGINEERING PRACTICE 2021; 116:104933. [PMID: 34539101 PMCID: PMC8443145 DOI: 10.1016/j.conengprac.2021.104933] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Many data-driven modeling techniques identify locally valid, linear representations of time-varying or nonlinear systems, and thus the model parameters must be adaptively updated as the operating conditions of the system vary, though the model identification typically does not consider prior knowledge. In this work, we propose a new regularized partial least squares (rPLS) algorithm that incorporates prior knowledge in the model identification and can handle missing data in the independent covariates. This latent variable (LV) based modeling technique consists of three steps. First, a LV-based model is developed on the historical time series data. In the second step, the missing observations in the new incomplete data sample are estimated. Finally, the future values of the outputs are predicted as a linear combination of estimated scores and loadings. The model is recursively updated as new data are obtained from the system. The performance of the proposed rPLS and rPLS with exogenous inputs (rPLSX) algorithms are evaluated by modeling variations in glucose concentration (GC) of people with Type 1 diabetes (T1D) in response to meals and physical activities for prediction windows up to one hour, or 12 sampling instances, into the future. The proposed rPLS family of GC prediction models are evaluated with both in-silico and clinical experiment data and compared with the performance of recursive time series and kernel-based models. The root mean squared error (RMSE) with simulated subjects in the multivariable T1D simulator where physical activity effects are incorporated in GC variations are 2.52 and 5.81 mg/dL for 30 and 60 mins ahead predictions (respectively) when information for all meals and physical activities are used, increasing to 2.70 and 6.54 mg/dL (respectively) when meals and activities occurred, but the information is with-held from the modeling algorithms. The RMSE is 10.45 and 14.48 mg/dL for clinical study with prediction horizons of 30 and 60 mins, respectively. The low RMSE values demonstrate the effectiveness of the proposed rPLS approach compared to the conventional recursive modeling algorithms.
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Affiliation(s)
- Xiaoyu Sun
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Andrew Shahidehpour
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
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10
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Polisano F, Ryals AD, Pannocchia G, Landi A. MPC based optimization applied to treatment of HCV infections. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106383. [PMID: 34496321 DOI: 10.1016/j.cmpb.2021.106383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE The recent introduction of antivirals for the treatment of the hepatitis C virus opens new frontiers but also poses a significant burden on public health systems. This paper presents a simulation study in which model predictive control (MPC) is proposed for optimizing the therapy aiming to obtain a reduction of the costs of therapy, while maintaining the best pharmacological control of the infection. METHODS A dynamic model describing the evolution of hepatitis C is deployed as internal model for MPC implementation, using nominal values of parameters. Different closed-loop simulations are presented both in nominal and in mismatch conditions. In addition, a more easily implementable treatment is proposed, which is based on a discrete dosage approach, where days on/off therapy are considered instead of continuous therapy modulation. RESULTS Results show that therapy modulation allows one to achieve the same infection evolution as with full therapy, with a reduction of drug consumption between 10% and 40%. The alternative discrete dosage approach shows similar results achieved with therapy modulation, both in terms of therapy effectiveness and drug consumption reduction. CONCLUSIONS The proposed model predictive control therapy optimization strategies appear to be effective, implementable and robust to model errors. It therefore represents a potentially useful approach to alleviate the burden of HCV therapy cost on national health systems.
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Affiliation(s)
- Fabio Polisano
- Department of Information Engineering, University of Pisa, Italy
| | - Andrea Dan Ryals
- Department of Information Engineering, University of Pisa, Italy
| | | | - Alberto Landi
- Department of Information Engineering, University of Pisa, Italy
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11
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Gambo IP, Massenon R, Kolawole BA, Ikono R. Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2021. [DOI: 10.4018/ijhisi.289461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Engineering smart software that can monitor, predict, and control blood glucose is critical to improving patients' quality of treatments with type 1 Diabetic Mellitus (T1DM). However, ensuring a reasonable glycemic level in diabetic patients is quite challenging, as many methods do not adequately capture the complexities involved in glycemic control. This problem introduces a new level of complexity and uncertainty to the patient's psychological state, thereby making this problem nonlinear and unobservable. In this paper, we formulated a mathematical model using carbohydrate counting, insulin requirements, and the Harris-Benedict energy equations to establish the framework for predicting and controlling blood glucose level regulation in T1DM. We implemented the framework and evaluated its performance using root mean square error (RMSE) and mean absolute error (MAE) on a case study. Our framework had less error rate in terms of RMSE and MAE, which indicates a better fit with reasonable accuracy.
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Affiliation(s)
| | | | | | - Rhoda Ikono
- Obafemi Awolowo University, Ile-Ife, Nigeria
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12
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Syafiie S. H ∞ controller and observer synthesis with delay and nonlinear perturbation of double diabetes systems. ISA TRANSACTIONS 2021; 111:24-34. [PMID: 33309159 DOI: 10.1016/j.isatra.2020.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/20/2020] [Accepted: 11/13/2020] [Indexed: 06/12/2023]
Abstract
Patient having type 1 diabetes mellitus (having insulin resistance experience) cannot be treated solely by treatment procedure of type 1 patient nor does treatment employed for type 2 diabetes work for such patient. This type of diabetes patient needs a specific insulin injection procedure. For continuous insulin injection, the patient has to be classified as a different group from type 1 and type 2 patient. The patients experiencing both type 1 and 2 are called double diabetes mellitus (DDM) patient. Dynamic behavior of the patient was presented in delay differential equation (DDE). Based on the developed DDE of DDM model, controllers and observers fulfilling H∞ norm bound are designed for this specific group of diabetes mellitus patient. Also, a nominal controller and a nominal observer are synthesized to check the proposed controller's ability for disturbance rejection, which is the glucose intake. The performance of the designed controller and observer is evaluated for a population of simulated patients. It shows that controller and observer are able to regulate and estimate, respectively, glycaemic for population of double diabetes patients.
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Affiliation(s)
- S Syafiie
- Department of Chemical and Materials Engineering, Faculty of Engineering, King AbdulAziz University, Jeddah, 21911, Kingdom of Saudi Arabia.
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13
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Pinnaro C, Christensen GE, Curtis V. Modeling Ketogenesis for Use in Pediatric Diabetes Simulation. J Diabetes Sci Technol 2021; 15:303-308. [PMID: 31608650 PMCID: PMC8256079 DOI: 10.1177/1932296819882058] [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: 11/15/2022]
Abstract
BACKGROUND Simulation is being increasingly integrated into medical education. Diabetes simulation is well-received by trainees and has demonstrated improved clinical results, including reduced adult inpatient hyperglycemia. However, no pediatric-specific diabetes simulation programs exist for use in medical education. None of the existing diabetes models incorporate ketones as an input or an output, which is essential for use in teaching pediatric diabetes management. METHODS We created a pediatric diabetes simulation incorporating both blood sugar and urine ketones as output. Ketone output is implemented as a state variable but is obfuscated to simulate hospital experience. Blood sugar output is similar to other models and incorporates the current blood sugar, insulin on board (IOB) and carbohydrates on board (COB), and insulin and carbohydrate sensitivities. The program calculates all IOB and COB every 15 minutes based on user input and provides written summary feedback at the end of the simulation about inaccurate dosing and timing. RESULTS The simulation realistically incorporated both blood glucose and urine ketones in clinically valid and actionable formats. After completing this simulation, 16/17 pediatric residents indicated that they wanted more simulated diabetes cases integrated into their curriculum. CONCLUSION Implementing simulation into pediatric diabetes education was feasible and well-received. More work is needed to further study the role of simulation in pediatric diabetes education when used adjunctively or in lieu of lectures when time or resources are limited.
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Affiliation(s)
- Catherina Pinnaro
- Stead Family Department of
Pediatrics, Department of Endocrinology and Diabetes, University of Iowa,
IA, USA
- Catherina Pinnaro, MD, Stead Family
Department of Pediatrics, Department of Endocrinology and Diabetes,
University of Iowa, 2015-20 Boyd Tower, 200 Hawkins Drive, Iowa City,
IA 52242, USA.
| | - Gary E. Christensen
- Department of Electrical and
Computer Engineering, University of Iowa, IA, USA
- Department of Radiation Oncology,
University of Iowa, IA, USA
| | - Vanessa Curtis
- Stead Family Department of
Pediatrics, Department of Endocrinology and Diabetes, University of Iowa,
IA, USA
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14
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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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15
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Ullah N, Muhammad AS. Novel algebraic meal disturbance estimation based adaptive robust control design for blood glucose regulation in type 1 diabetes patients. IET Syst Biol 2020; 14:200-210. [PMID: 32737278 PMCID: PMC8687270 DOI: 10.1049/iet-syb.2020.0002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 04/12/2020] [Accepted: 04/22/2020] [Indexed: 11/20/2022] Open
Abstract
This study designs a robust closed-loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back-stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman's minimal model. The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super-twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller's robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.
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Affiliation(s)
- Nasim Ullah
- Electrical Engineering Department, Taif University, Al-Hawiyah, Taif, P.O. box: 888, Kingdom of Saudi Arabia.
| | - Al-Sharef Muhammad
- Electrical Engineering Department, Taif University, Al-Hawiyah, Taif, P.O. box: 888, Kingdom of Saudi Arabia
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17
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Xie J, Wang Q. A Data-Driven Personalized Model of Glucose Dynamics Taking Account of the Effects of Physical Activity for Type 1 Diabetes: An In Silico Study. J Biomech Eng 2020; 141:2703963. [PMID: 30458503 DOI: 10.1115/1.4041522] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Indexed: 12/17/2022]
Abstract
This paper aims to develop a data-driven model for glucose dynamics taking into account the effects of physical activity (PA) through a numerical study. It intends to investigate PA's immediate effect on insulin-independent glucose variation and PA's prolonged effect on insulin sensitivity. We proposed a nonlinear model with PA (NLPA), consisting of a linear regression of PA and a bilinear regression of insulin and PA. The model was identified and evaluated using data generated from a physiological PA-glucose model by Dalla Man et al. integrated with the uva/padova Simulator. Three metrics were computed to compare blood glucose (BG) predictions by NLPA, a linear model with PA (LPA), and a linear model with no PA (LOPA). For PA's immediate effect on glucose, NLPA and LPA showed 45-160% higher mean goodness of fit (FIT) than LOPA under 30 min-ahead glucose prediction (P < 0.05). For the prolonged PA effect on glucose, NLPA showed 87% higher FIT than LPA (P < 0.05) for simulations using no previous measurements. NLPA had 25-37% and 31-54% higher sensitivity in predicting postexercise hypoglycemia than LPA and LOPA, respectively. This study demonstrated the following qualitative trends: (1) for moderate-intensity exercise, accuracy of BG prediction was improved by explicitly accounting for PA's effect; and (2) accounting for PA's prolonged effect on insulin sensitivity can increase the chance of early prediction of postexercise hypoglycemia. Such observations will need to be further evaluated through human subjects in the future.
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Affiliation(s)
- Jinyu Xie
- Mechanical and Nuclear Engineering, 315 Leonhard Building, Penn State University, University Park, PA 16802 e-mail:
| | - Qian Wang
- Mem. ASME Professor Mechanical Engineering, 325 Leonhard Building, Penn State University, University Park, PA 16802 e-mail:
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Rashid M, Samadi S, Sevil M, Hajizadeh I, Kolodziej P, Hobbs N, Maloney Z, Brandt R, Feng J, Park M, Quinn L, Cinar A. Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes. Comput Chem Eng 2019; 130:106565. [PMID: 32863472 PMCID: PMC7449052 DOI: 10.1016/j.compchemeng.2019.106565] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.
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Affiliation(s)
- Mudassir Rashid
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Sediqeh Samadi
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Mert Sevil
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Iman Hajizadeh
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Paul Kolodziej
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Nicole Hobbs
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Zacharie Maloney
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Rachel Brandt
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Jianyuan Feng
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Minsun Park
- College of Nursing, University of Illinois at Chicago, Chicago, IL, USA, 60612
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago, Chicago, IL, USA, 60612
| | - Ali Cinar
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
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19
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Resalat N, Hilts W, Youssef JE, Tyler N, Castle JR, Jacobs PG. Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs. J Diabetes Sci Technol 2019; 13:1044-1053. [PMID: 31595784 PMCID: PMC6835177 DOI: 10.1177/1932296819881467] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND People with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals' different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions. METHODS A new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient's insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control. RESULTS Incorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%. CONCLUSION Adapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic outcomes.
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Affiliation(s)
- Navid Resalat
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Wade Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR, USA
| | - Nichole Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R. Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR, USA
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Peter G. Jacobs, PhD, Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Ave, Mailstop: 13B, Portland, OR 97239, USA.
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Rogg S, Fuertinger DH, Volkwein S, Kappel F, Kotanko P. Optimal EPO dosing in hemodialysis patients using a non-linear model predictive control approach. J Math Biol 2019; 79:2281-2313. [PMID: 31630225 PMCID: PMC6858911 DOI: 10.1007/s00285-019-01429-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 09/06/2019] [Indexed: 12/19/2022]
Abstract
Anemia management with erythropoiesis stimulating agents is a challenging task in hemodialysis patients since their response to treatment varies highly. In general, it is difficult to achieve and maintain the predefined hemoglobin (Hgb) target levels in clinical practice. The aim of this study is to develop a fully personalizable controller scheme to stabilize Hgb levels within a narrow target window while keeping drug doses low to mitigate side effects. First in-silico results of this framework are presented in this paper. Based on a model of erythropoiesis we formulate a non-linear model predictive control (NMPC) algorithm for the individualized optimization of epoetin alfa (EPO) doses. Previous to this work, model parameters were estimated for individual patients using clinical data. The optimal control problem is formulated for a continuous drug administration. This is currently a hypothetical form of drug administration for EPO as it would require a programmable EPO pump similar to insulin pumps used to treat patients with diabetes mellitus. In each step of the NMPC method the open-loop problem is solved with a projected quasi-Newton method. The controller is successfully tested in-silico on several patient parameter sets. An appropriate control is feasible in the tested patients under the assumption that the controlled quantity is measured regularly and that continuous EPO administration is adjusted on a daily, weekly or monthly basis. Further, the controller satisfactorily handles the following challenging problems in simulations: bleedings, missed administrations and dosing errors.
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Affiliation(s)
- S Rogg
- Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany.
| | - D H Fuertinger
- Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany
| | - S Volkwein
- Department for Mathematics and Statistics, University of Konstanz, Konstanz, Germany
| | - F Kappel
- Institute for Mathematics and Scientific Computing, Karl-Franzens University of Graz, Graz, Austria
| | - P Kotanko
- Renal Research Institute, New York, NY, USA.,Icahn School of Medicine at Mount Sinai, New York, NY, USA
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21
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Garcia-Tirado J, Corbett JP, Boiroux D, Jørgensen JB, Breton MD. Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control. JOURNAL OF PROCESS CONTROL 2019; 80:202-210. [PMID: 32831483 PMCID: PMC7437946 DOI: 10.1016/j.jprocont.2019.05.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA
| | - Dimitri Boiroux
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- Danish Diabetes Academy, DK-5000 Odense, Denmark
| | - John Bagterp Jørgensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
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22
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Mandal S, Sutradhar A. Robust multi-objective blood glucose control in Type-1 diabetic patient. IET Syst Biol 2019; 13:136-146. [PMID: 31170693 PMCID: PMC8687400 DOI: 10.1049/iet-syb.2018.5093] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
In this study, an automatic robust multi‐objective controller has been proposed for blood glucose (BG) regulation in Type‐1 Diabetic Mellitus (T1DM) patient through subcutaneous route. The main objective of this work is to control the BG level in T1DM patient in the presence of unannounced meal disturbances and other external noises with a minimum amount of insulin infusion rate. The multi‐objective output‐feedback controller with H∞, H2 and pole‐placement constraints has been designed using linear matrix inequality technique. The designed controller for subcutaneous insulin delivery was tested on in silico adult and adolescent subjects of UVa/Padova T1DM metabolic simulator. The experimental results show that the closed‐loop system tracks the reference BG level very well and does not show any hypoglycaemia effect even during the long gap of a meal at night both for in silico adults and adolescent. In the presence of 50 gm meal disturbance, average adult experience normoglycaemia 92% of the total simulation time and hypoglycaemia 0% of total simulation time. The robustness of the controller has been tested in the presence of irregular meals and insulin pump noise and error. The controller yielded robust performance with a lesser amount of insulin infusion rate than the other designed controllers reported earlier.
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Affiliation(s)
- Sharmistha Mandal
- Electrical Engineering Department, Aliah University, Salt Lake, Kolkata, India.
| | - Ashoke Sutradhar
- Electrical Engineering Department, Indian Institute of Engineering Science and Technology, Howrah, India
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23
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Zavala E, Wedgwood KCA, Voliotis M, Tabak J, Spiga F, Lightman SL, Tsaneva-Atanasova K. Mathematical Modelling of Endocrine Systems. Trends Endocrinol Metab 2019; 30:244-257. [PMID: 30799185 PMCID: PMC6425086 DOI: 10.1016/j.tem.2019.01.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 01/23/2019] [Accepted: 01/25/2019] [Indexed: 12/12/2022]
Abstract
Hormone rhythms are ubiquitous and essential to sustain normal physiological functions. Combined mathematical modelling and experimental approaches have shown that these rhythms result from regulatory processes occurring at multiple levels of organisation and require continuous dynamic equilibration, particularly in response to stimuli. We review how such an interdisciplinary approach has been successfully applied to unravel complex regulatory mechanisms in the metabolic, stress, and reproductive axes. We discuss how this strategy is likely to be instrumental for making progress in emerging areas such as chronobiology and network physiology. Ultimately, we envisage that the insight provided by mathematical models could lead to novel experimental tools able to continuously adapt parameters to gradual physiological changes and the design of clinical interventions to restore normal endocrine function.
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Affiliation(s)
- Eder Zavala
- Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QD, UK; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter EX4 4QD, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK.
| | - Kyle C A Wedgwood
- Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QD, UK; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter EX4 4QD, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
| | - Margaritis Voliotis
- Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QD, UK; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter EX4 4QD, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
| | - Joël Tabak
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter EX4 4PS, UK
| | - Francesca Spiga
- Henry Wellcome Laboratories for Integrative Neuroscience and Endocrinology, University of Bristol, Bristol BS1 3NY, UK
| | - Stafford L Lightman
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QD, UK; Henry Wellcome Laboratories for Integrative Neuroscience and Endocrinology, University of Bristol, Bristol BS1 3NY, UK
| | - Krasimira Tsaneva-Atanasova
- Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QD, UK; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter EX4 4QD, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
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24
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Messori M, Toffanin C, Del Favero S, De Nicolao G, Cobelli C, Magni L. Model individualization for artificial pancreas. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 171:133-140. [PMID: 27424482 DOI: 10.1016/j.cmpb.2016.06.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 05/13/2016] [Accepted: 06/28/2016] [Indexed: 05/23/2023]
Abstract
BACKGROUND AND OBJECTIVE The inter-subject variability characterizing the patients affected by type 1 diabetes mellitus makes automatic blood glucose control very challenging. Different patients have different insulin responses, and a control law based on a non-individualized model could be ineffective. The definition of an individualized control law in the context of artificial pancreas is currently an open research topic. In this work we consider two novel identification approaches that can be used for individualizing linear glucose-insulin models to a specific patient. METHODS The first approach belongs to the class of black-box identification and is based on a novel kernel-based nonparametric approach, whereas the second is a gray-box identification technique which relies on a constrained optimization and requires to postulate a model structure as prior knowledge. The latter is derived from the linearization of the average nonlinear adult virtual patient of the UVA/Padova simulator. Model identification and validation are based on in silico data collected during simulations of clinical protocols designed to produce a sufficient signal excitation without compromising patient safety. The identified models are evaluated in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean square error. RESULTS Both identification approaches were used to identify a linear individualized glucose-insulin model for each adult virtual patient of the UVA/Padova simulator. The resulting model simulation performance is significantly improved with respect to the performance achieved by a linear average model. CONCLUSIONS The approaches proposed in this work have shown a good potential to identify glucose-insulin models for designing individualized control laws for artificial pancreas.
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Affiliation(s)
- Mirko Messori
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.
| | - Chiara Toffanin
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Simone Del Favero
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giuseppe De Nicolao
- Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Lalo Magni
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
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25
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Bhattacharjee A, Easwaran A, Leow MKS, Cho N. Design of an online-tuned model based compound controller for a fully automated artificial pancreas. Med Biol Eng Comput 2019; 57:1437-1449. [PMID: 30895514 DOI: 10.1007/s11517-019-01972-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 03/06/2019] [Indexed: 11/25/2022]
Abstract
This paper deals with the development of a control algorithm that can predict optimal insulin doses without patients' intervention in fully automated artificial pancreas system. An online-tuned model based compound controller comprising an online-tuned internal model control (IMC) algorithm and an enhanced IMC (eIMC) algorithm along with a meal detection module is proposed. Volterra models, used to develop IMC and eIMC algorithms, are developed online using recursive least squares (RLS) filter. The time domain kernels, computed online using RLS filter, are converted into frequency domain to obtain Volterra transfer function (VTF). VTFs are used to develop both IMC and eIMC algorithms. The compound controller is designed in such a way that eIMC predicts insulin doses when the glucose rate increase detector of meal detection module is positive, otherwise conventional IMC takes the control action. Experimental results show that the compound controller performs robustly in the presence of higher and irregular amounts of meal disturbances at random times, very high actuator and sensor noises and also with the variation in insulin sensitivity. The combination of compound control strategy and meal detection module compensates the shortcomings of both slow subcutaneous insulin action that causes postprandial hyperglycemia, and delayed peak of action that causes hypoglycaemia. Graphical Abstract A fully-automated artificial pancreas system containing glucose sensor, insulin pump and control algorithm. Block diagram showing the control algorithm i.e., online-tuned compound IMC comprising enhanced IMC, conventional IMC and meal detection module, developed in the present work.
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Affiliation(s)
| | | | - Melvin Khee-Shing Leow
- Nanyang Technological University, Singapore, Singapore.,Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore.,Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore.,Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore, Singapore.,Lee Kong Chian School of Medicine-Imperial College London, London, SW7 2DD, UK
| | - Namjoon Cho
- Nanyang Technological University, Singapore, Singapore
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26
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Basha AA, Vivekanandan S, Parthasarathy P. Blood Glucose Regulation for Post-Operative Patients with Diabetics and Hypertension Continuum: A Cascade Control-Based Approach. J Med Syst 2019; 43:95. [PMID: 30847581 DOI: 10.1007/s10916-019-1224-6] [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: 12/31/2018] [Accepted: 02/21/2019] [Indexed: 11/24/2022]
Abstract
Management of glycemic level in post-operative condition is critical for hypertensive patients and the post-operative stress may results in hyperglycemia, hyper insulin and osmotic diuresis. Recent medical research shows that diabetic and hypertension hands together in a significant overlap in its etiology and its disease mechanism. It is clear that there is a call for monitoring in the parameter and controlling the glucose level particularly in the presence of hypertension. This paper proposes the novel complex (cascade) control system to control the insulin infusion level particularly in the presence of hypertension. Based on the requirements the structure has been designed and the simulation results indicates that the proposed control strategy shows better results and may achieve potentially better glycemic control to the hypersensitive diabetic patients.
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Affiliation(s)
- A Alavudeen Basha
- School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India.
| | - S Vivekanandan
- School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India
| | - P Parthasarathy
- School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India
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27
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Yamanaka Y, Uchida K, Akashi M, Watanabe Y, Yaguchi A, Shimamoto S, Shimoda S, Yamada H, Yamashita M, Kimura H. Mathematical modeling of septic shock based on clinical data. Theor Biol Med Model 2019; 16:5. [PMID: 30841902 PMCID: PMC6404291 DOI: 10.1186/s12976-019-0101-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 02/11/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Mathematical models of diseases may provide a unified approach for establishing effective treatment strategies based on fundamental pathophysiology. However, models that are useful for clinical practice must overcome the massive complexity of human physiology and the diversity of patients' environmental conditions. With the aim of modeling a complex disease, we choose sepsis, which is highly complex, life-threatening systemic disease with high mortality. In particular, we focused on septic shock, a subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality. Our model includes cardiovascular, immune, nervous system models and a pharmacological model as submodels and integrates them to create a sepsis model based on pathological facts. RESULTS Model validation was done in two steps. First, we established a model for a standard patient in order to confirm the validity of our approach in general aspects. For this, we checked the correspondence between the severity of infection defined in terms of pathogen growth rate and the ease of recovery defined in terms of the intensity of treatment required for recovery. The simulations for a standard patient showed good correspondence. We then applied the same simulations to a patient with heart failure as an underlying disease. The model showed that spontaneous recovery would not occur without treatment, even for a very mild infection. This is consistent with clinical experience. We next validated the model using clinical data of three sepsis patients. The model parameters were tuned for these patients based on the model for the standard patient used in the first part of the validation. In these cases, the simulations agreed well with clinical data. In fact, only a handful parameters need to be tuned for the simulations to match with the data. CONCLUSIONS We have constructed a model of septic shock and have shown that it can reproduce well the time courses of treatment and disease progression. Tuning of model parameters for each patient could be easily done. This study demonstrates the feasibility of disease models, suggesting the possibility of clinical use in the prediction of disease progression, decisions on the timing of drug dosages, and the estimation of time of infection.
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Affiliation(s)
| | - Kenko Uchida
- Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Momoka Akashi
- Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Yuta Watanabe
- Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Arino Yaguchi
- Tokyo Women’s Medical University, Kawada-cho, Shinjuku-ku, Tokyo, Japan
| | - Shuji Shimamoto
- Tokyo Women’s Medical University, Kawada-cho, Shinjuku-ku, Tokyo, Japan
| | - Shingo Shimoda
- Institute of Physical and Chemical Research, Moriyama-ku, Nagoya, Japan
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Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 2018; 4:14. [PMID: 32232090 PMCID: PMC7098217 DOI: 10.1186/s42234-018-0015-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/08/2018] [Indexed: 12/28/2022] Open
Abstract
The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, P.O. Box 400888, Charlottesville, VA 22908 USA
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Beneyto A, Vehi J. Postprandial fuzzy adaptive strategy for a hybrid proportional derivative controller for the artificial pancreas. Med Biol Eng Comput 2018; 56:1973-1986. [PMID: 29725915 DOI: 10.1007/s11517-018-1832-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 04/19/2018] [Indexed: 11/24/2022]
Abstract
This paper presents a support fuzzy adaptive system for a hybrid proportional derivative controller that will refine its parameters during postprandial periods to enhance performance. Even though glucose controllers have improved over the last decade, tuning them and keeping them tuned are still major challenges. Changes in a patient's lifestyle, stress, exercise, or other activities may modify their blood glucose system, making it necessary to retune or change the insulin dosing algorithm. This paper presents a strategy to adjust the parameters of a proportional derivative controller using the so-called safety auxiliary feedback element loop for type 1 diabetic patients. The main parameters, such as the insulin on board limit and proportional gain are tuned using postprandial performance indexes and the information given by the controller itself. The adaptive and robust performance of the control algorithm was assessed "in silico" on a cohort of virtual patients under challenging realistic scenarios considering mixed meals, circadian variations, time-varying uncertainties, sensor errors, and other disturbances. The results showed that an adaptive strategy can significantly improve the performance of postprandial glucose control, individualizing the tuning by directly taking into account the intra-patient variability of type 1 patients. Graphical Abstract title: Postprandial glycaemia improvement via fuzzy adaptive control A fuzzy inference engine was implemented within a clinically tested artificial pancreas control system. The aim of the fuzzy system was to adapt controller parameters to improve postprandial blood glucose control while ensuring safety. Results show a significant improvement over time of the postprandial glucose response due to the adaptation, thus demonstrating the usefulness of the fuzzy adaptive system.
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Affiliation(s)
- Aleix Beneyto
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus de Montilivi, s/n, Edifici P4, 17071, Girona, Spain
| | - Josep Vehi
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus de Montilivi, s/n, Edifici P4, 17071, Girona, Spain. .,CIBERDEM, Girona, Spain.
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Garcia-Tirado J, Zuluaga-Bedoya C, Breton MD. Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems. J Diabetes Sci Technol 2018; 12:937-952. [PMID: 30095007 PMCID: PMC6134618 DOI: 10.1177/1932296818788873] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Our aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D). METHODS Structural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion. RESULTS A reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours. CONCLUSION This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Christian Zuluaga-Bedoya
- Dynamic Processes Research Group KALMAN, Universidad Nacional de Colombia, Medellín, Antioquia, Colombia
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
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Kadota R, Sugita K, Uchida K, Yamada H, Yamashita M, Kimura H. A mathematical model of type 1 diabetes involving leptin effects on glucose metabolism. J Theor Biol 2018; 456:213-223. [PMID: 30098320 DOI: 10.1016/j.jtbi.2018.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 08/03/2018] [Accepted: 08/06/2018] [Indexed: 12/14/2022]
Abstract
Leptin, a hormone released from fat cells in adipose tissues, was recently found to be capable of normalizing glucose metabolism in animals. Clinical data on patients with lipodystrophy indicates that leptin may have a positive effect on glucose metabolism in individuals with diabetes. There are growing expectations that leptin can improve the current insulin treatment for patients with type 1 diabetes. We investigated this possibility through in silico experiments based on a mathematical model of diabetes, which is currently the only mode of research that eliminates human risk. A model of the brain-centered glucoregulatory system, in which leptin plays a central role, was constructed and integrated within a conventional model of insulin/glucose dynamics. The model has been validated using experimental data from animal studies. The in silico combination experiments showed excellent therapeutic performance over insulin monotherapy.
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Affiliation(s)
- Rei Kadota
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Kazuma Sugita
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Kenko Uchida
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Hitoshi Yamada
- TOYOTA MOTOR CORPORATION, 1, Toyota-cho, Toyota, Aichi, Japan
| | | | - Hidenori Kimura
- Faculty of Science and Engineering, Waseda University, Building 55S, Room 706A, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555, Japan.
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Bahremand S, Ko HS, Balouchzadeh R, Felix Lee H, Park S, Kwon G. Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system. Med Biol Eng Comput 2018; 57:177-191. [PMID: 30069675 DOI: 10.1007/s11517-018-1872-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Accepted: 07/09/2018] [Indexed: 10/28/2022]
Abstract
Artificial pancreas system (APS) is a viable option to treat diabetic patients. Researchers, however, have not conclusively determined the best control method for APS. Due to intra-/inter-variability of insulin absorption and action, an individualized algorithm is required to control blood glucose level (BGL) for each patient. To this end, we developed model predictive control (MPC) based on artificial neural networks (ANNs), which combines ANN for BGL prediction based on inputs and MPC for BGL control based on the ANN (NN-MPC). First, we developed a mathematical model for diabetic rats, which was used to identify individual virtual subjects by fitting to empirical data collected through an APS, including BGL data, insulin injection, and food intake. Then, the virtual subjects were used to generate datasets for training ANNs. The NN-MPC determines control actions (insulin injection) based on BGL predicted by the ANN. To evaluate the NN-MPC, we conducted experiments using four virtual subjects under three different scenarios. Overall, the NN-MPC maintained BGL within the normal range about 90% of the time with a mean absolute deviation of 4.7 mg/dl from a desired BGL. Our findings suggest that the NN-MPC can provide subject-specific BGL control in conjunction with a closed-loop APS. Graphical abstract ᅟ.
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Affiliation(s)
- Saeid Bahremand
- Department of Mechanical and Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA
| | - Hoo Sang Ko
- Department of Mechanical and Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA.
| | - Ramin Balouchzadeh
- Department of Mechanical and Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA
| | - H Felix Lee
- Department of Mechanical and Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA
| | - Sarah Park
- Research and Instructional Services, Duke University, Durham, NC, 27708, USA
| | - Guim Kwon
- Department of Pharmaceutical Sciences, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA
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33
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Artificial pancreas clinical trials: Moving towards closed-loop control using insulin-on-board constraints. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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34
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Rollins DK, Mei Y. A new feedback predictive control approach for processes with time delay in the manipulated variable. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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35
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Feng J, Hajizadeh I, Yu X, Rashid M, Turksoy K, Samadi S, Sevil M, Hobbs N, Brandt R, Lazaro C, Maloney Z, Littlejohn E, Philipson LH, Cinar A. Multi-level Supervision and Modification of Artificial Pancreas Control System. Comput Chem Eng 2018; 112:57-69. [PMID: 30287976 PMCID: PMC6166877 DOI: 10.1016/j.compchemeng.2018.02.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components. Many APs use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. The performance of model-based controllers depends on the accuracy of the models that is affected by large dynamic changes in glucose-insulin metabolism or equipment performance that may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. And the performance of the controller may vary at each sampling step and each period (meal, exercise, and sleep), and from day to day. Here we describe a multi-level supervision and controller modification (ML-SCM) module is developed to supervise the performance of the AP system and retune the controller. It supervises AP performance in 3 time windows: sample level, period level, and day level. At sample level, an online controller performance assessment sub-module will generate controller performance indexes to evaluate various components of the AP system and conservatively modify the controller. A sensor error detection and signal reconciliation module will detect sensor error and reconcile the CGM sensor signal at each sample. At period level, the controller performance is evaluated with information collected during a certain time period and the controller is tuned more aggressively. At the day level, the daily CGM ranges are further analyzed to determine the adjustable range of controller parameters used for sample level and period level. Thirty subjects in the UVa/Padova metabolic simulator were used to evaluate the performance of the ML-SCM module and one clinical experiment is used to illustrate its performance in a clinical environment. The results indicate that the AP system with an ML-SCM module has a safer range of glucose concentration distribution and more appropriate insulin infusion rate suggestions than an AP system without the ML-SCM module.
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Affiliation(s)
- Jianyuan Feng
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Xia Yu
- Department of Control Theory and Control Engineering, Northeastern University, Shenyang, Liaoning China
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kamuran Turksoy
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Caterina Lazaro
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | | | - Louis H Philipson
- Departments of Medicine and Pediatrics - Section of Endocrinology, University of Chicago, Chicago, IL, USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
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Avila LO, De Paula M, Martinez EC, Errecalde ML. Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.01.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Affiliation(s)
- Garry M Steil
- Division of Medicine Critical Care, Children's Hospital Boston , Boston, Massachusetts
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38
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Piemonte V. Predictive Models Control of the Artificial Pancreas: Compartmental or Neural Networks Models? Artif Organs 2018; 42:251-253. [DOI: 10.1111/aor.13104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 12/07/2017] [Indexed: 11/30/2022]
Affiliation(s)
- Vincenzo Piemonte
- Unit of Chemical-physics Fundamentals in Chemical Engineering, Department of Engineering; University Campus Bio-Medico di Roma, via Álvaro del Portillo 21; Rome 00128 Italy
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39
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Soylu S, Danisman K. In silico testing of optimized Fuzzy P+D controller for artificial pancreas. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.02.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Montaser E, Díez JL, Bondia J. Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas. J Diabetes Sci Technol 2017; 11:1124-1131. [PMID: 29039207 PMCID: PMC5951060 DOI: 10.1177/1932296817736074] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Linear empirical dynamic models have been widely used for glucose prediction. The extension of the concept of seasonality, characteristic of other domains, is explored here for the improvement of prediction accuracy. METHODS Twenty time series of 8-hour postprandial periods (PP) for a same 60g-carbohydrate meal were collected from a closed-loop controller validation study. A single concatenated time series was produced representing a collection of data from similar scenarios, resulting in seasonality. Variability in the resulting time series was representative of worst-case intrasubject variability. Following a leave-one-out cross-validation, seasonal and nonseasonal autoregressive integrated moving average models (SARIMA and ARIMA) were built to analyze the effect of seasonality in the model prediction accuracy. Further improvement achieved from the inclusion of insulin infusion rate as exogenous variable was also analyzed. Prediction horizons (PHs) from 30 to 300 min were considered. RESULTS SARIMA outperformed ARIMA revealing a significant role of seasonality. For a 5-h PH, average MAPE was reduced in 26.62%. Considering individual runs, the improvement ranged from 6.3% to 54.52%. In the best-performing case this reduction amounted to 29.45%. The benefit of seasonality was consistent among different PHs, although lower PHs benefited more, with MAPE reduction over 50% for PHs of 60 and 120 minutes, and over 40% for 180 min. Consideration of insulin infusion rate into the seasonal model further improved performance, with a 61.89% reduction in MAPE for 30-min PH and reductions over 20% for PHs over 180 min. CONCLUSIONS Seasonality improved model accuracy allowing for the extension of the PH significantly.
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Affiliation(s)
- Eslam Montaser
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - José-Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
- Jorge Bondia, PhD, Departamento de Ingeniería de Sistemas y Automática, Universitat Politècnica de València, C/ Camí de Vera, s/n, 46022 Valencia, Spain.
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41
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Asadollahi K, Asadollahi P, Azizi M, Abangah G. A self-assessment predictive model for type 2 diabetes or impaired fasting glycaemia derived from a population-based survey. Diabetes Res Clin Pract 2017; 131:219-229. [PMID: 28778049 DOI: 10.1016/j.diabres.2017.07.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 06/13/2017] [Accepted: 07/07/2017] [Indexed: 10/19/2022]
Abstract
AIMS There is no cure for diabetes and its prevention is interesting for both people and health policy makers. The aim of this study was to construct a simple scoring system to predict diabetes and suggest a self assessment predictive model for type 2 diabetes in Iran. METHODS This study was a part of a comprehensive population based survey performed in Ilam province during 2011-2012, including 2158 cases≥25years. All demographic and laboratory results were entered into the prepared sheets and were analysed using SPSS 16. By identification of relative risks of diabetes and IFG, a predictive model was constructed and proposed for these abnormalities. RESULTS Totally, 2158 people comprising 72% female, 60% from urban regions, mean age of 45.5±14years were investigated and the average height, weight, FBS and waist of participants were as follows respectively: 164±8.9cm, 68.4±12.3kg, 5.7±2.8mmol/l (102.6±49.9mg/dl) and 82.3±14.3cm. The prevalence of IFG, diabetes and hyperglycaemia among all participants were 7.8%, 11.8% and 19.6% respectively. Regression analysis revealed familial history of diabetes, place of life, age, hypertension, daily exercise, marital status, gender, waist size, smoking, and BMI as the most relevant risk factors for diabetes and hyperglycemia. CONCLUSION A self-assessment predictive model was constructed for general population living in the west of Iran. This is the first self-assessment predictive model for diabetes in Iran.
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Affiliation(s)
- Khairollah Asadollahi
- Department of Social Medicine, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran; Psychosocial Injuries Researches Centre, Ilam University of Medical Sciences, Ilam, Iran
| | - Parisa Asadollahi
- Department of Microbiology, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran
| | - Monire Azizi
- Department of Anatomy, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran
| | - Ghobad Abangah
- Department of Gastroenterology, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran.
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Guilhem I, Penet M, Paillard A, Carpentier M, Esvant A, Lefebvre MA, Poirier JY. Manual Closed-Loop Insulin Delivery Using a Saddle Point Model Predictive Control Algorithm: Results of a Crossover Randomized Overnight Study. J Diabetes Sci Technol 2017; 11:1007-1014. [PMID: 28677416 PMCID: PMC5951001 DOI: 10.1177/1932296817717503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The purpose was to assess the efficacy of a new closed-loop algorithm (Saddle Point Model Predictive Control, SP-MPC) in achieving nocturnal normoglycemia while reducing the risk of hypoglycemia in patients with type 1 diabetes. METHOD In this randomized crossover study, 10 adult patients (mean hemoglobin A1c 7.35 ± 1.04%) were assigned to be treated overnight by open loop using sensor-augmented pump therapy (open-loop SAP) or manual closed-loop delivery. During closed loop, insulin doses were calculated using the SP-MPC algorithm and administered as manual boluses every 15 minutes from 9:00 pm to 8:00 am. Patients consumed a self-selected meal (65-125 g of carbohydrates) at 7:00 pm accompanied by their usual prandial bolus. Blood glucose was measured every 30 minutes. The primary endpoints were the time spent in target (70-145 mg/dl) and time spent below 70 mg/dl from 11:00 pm to 8:00 am. RESULTS Time spent in target did not differ between closed-loop and open-loop SAP. The number of hypoglycemic events (<70 mg/dl) was reduced 2.8-fold in closed loop (n = 5, median = 0/patient/hour; interquartile range: 0-0.11) as compared to open-loop SAP (n = 14, median = 0.22/patient/hour, 0.02-0.22) ( P = .02). The area under the curve for sensor glucose values >145 mg/dl was significantly lower during closed-loop than during open-loop SAP ( P = .03) as well as HBGI ( P = .02). CONCLUSIONS This pilot study suggests that the use of the SP-MPC algorithm may improve mean overnight glucose control and reduce the number of hypoglycemic events as compared to SAP therapy.
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Affiliation(s)
- Isabelle Guilhem
- CHU de Rennes, Department of Endocrinology, Diabetes and Nutrition, Rennes, France
- CHU de Rennes, CIC INSERM 1414, Rennes, France
- Isabelle Guilhem, MD, MSc, service d’Endocrinologie-Diabétologie-Nutrition, CHU de Rennes, hôpital sud, 16 boulevard de Bulgarie, 35203 Rennes cedex, France.
| | - Maxime Penet
- CentraleSupélec/I.E.T.R, Hybrid System Control Team, Cesson-Sévigné, France
| | - Anaïs Paillard
- CHU de Rennes, Department of Endocrinology, Diabetes and Nutrition, Rennes, France
- CHU de Rennes, CIC INSERM 1414, Rennes, France
| | - Marc Carpentier
- CHU de Rennes, Département d’Information Médicale, Rennes, France
| | - Annabelle Esvant
- CHU de Rennes, Department of Endocrinology, Diabetes and Nutrition, Rennes, France
- CHU de Rennes, CIC INSERM 1414, Rennes, France
| | | | - Jean-Yves Poirier
- CHU de Rennes, Department of Endocrinology, Diabetes and Nutrition, Rennes, France
- CHU de Rennes, CIC INSERM 1414, Rennes, France
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Piemonte V, Capocelli M, De Santis L, Maurizi AR, Pozzilli P. A Novel Three-Compartmental Model for Artificial Pancreas: Development and Validation. Artif Organs 2017; 41:E326-E336. [DOI: 10.1111/aor.12980] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 04/09/2017] [Accepted: 05/17/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Vincenzo Piemonte
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Mauro Capocelli
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Luca De Santis
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Anna Rita Maurizi
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
| | - Paolo Pozzilli
- Faculty of Engineering, University Campus Biomedico of Rome; Rome Italy
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Resalat N, El Youssef J, Reddy R, Jacobs PG. Design of a dual-hormone model predictive control for artificial pancreas with exercise model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:2270-2273. [PMID: 28324962 DOI: 10.1109/embc.2016.7591182] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The Artificial Pancreas (AP) is a new technology for helping people with type 1 diabetes to better control their glucose levels through automated delivery of insulin and optionally glucagon in response to sensed glucose levels. In a dual hormone AP, insulin and glucagon are delivered automatically to the body based on glucose sensor measurements using a control algorithm that calculates the amount of hormones to be infused. A dual-hormone MPC may deliver insulin continuously; however, it must avoid continuous delivery of glucagon because nausea can occur from too much glucagon. In this paper, we propose a novel dual-hormone (DH) switching model predictive control and compare it with a single-hormone (SH) MPC. We extended both MPCs by integrating an exercise model and compared performance with and without the exercise model included. Results were obtained on a virtual patient population undergoing a simulated exercise event using a mathematical glucoregulatory model that includes exercise. Time spent in hypoglycemia is significantly less with the DH-MPC than the SH-MPC (p=0.0022). Additionally, including the exercise model in the DH-MPC can help prevent hypoglycemia (p <; 0.001).
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Jallon P, Lachal S, Franco C, Charpentier G, Huneker E, Doron M, Franc S, Benhamou PY, Borot S, Guerci B, Hanaire HLN, Jeandidier N, Penfornis A, Renard E, Reznik Y, Schaepelynck P, Simon C. Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:1453-1456. [PMID: 29060152 DOI: 10.1109/embc.2017.8037108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Artificial Pancreas (AP) are developed for patients with Type 1 diabetes. This medical device system consists in the association of a subcutaneous continuous glucose monitor (CGM) providing a proxy of the patient's glycaemia and a control algorithm offering the real-time modification of the insulin delivery with an automatic command of the subcutaneous insulin pump. The most complex algorithms are based on a compartmental model of the glucoregulatory system of the patient coupled to an approach of MPC (Model-Predictive-Control) for the command. The automatic and unsupervised control of insulin regulation constitutes a major challenge in AP projects. A given model with its parameterization on the shelf will not directly represent the patient's data behavior and the personalization of the model is a prerequisite before using it in a MPC. The present paper focuses on the personalization of a compartmental showing a method where taking into account the estimation of the patient's state in addition to the parameter estimation improves the results in terms of mean quadratic error.
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Evaluation of model complexity in model predictive control within an exercise-enabled artificial pancreas. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.ifacol.2017.08.2270] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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MohammadRidha T, Ait-Ahmed M, Chaillous L, Krempf M, Guilhem I, Poirier JY, Moog CH. Model Free iPID Control for Glycemia Regulation of Type-1 Diabetes. IEEE Trans Biomed Eng 2017; 65:199-206. [PMID: 28459682 DOI: 10.1109/tbme.2017.2698036] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE The objective is to design a fully automated glycemia controller of Type-1 Diabetes (T1D) in both fasting and postprandial phases on a large number of virtual patients. METHODS A model-free intelligent proportional-integral-derivative (iPID) is used to infuse insulin. The feasibility of iPID is tested in silico on two simulators with and without measurement noise. The first simulator is derived from a long-term linear time-invariant model. The controller is also validated on the UVa/Padova metabolic simulator on 10 adults under 25 runs/subject for noise robustness test. RESULTS It was shown that without measurement noise, iPID mimicked the normal pancreatic secretion with a relatively fast reaction to meals as compared to a standard PID. With the UVa/Padova simulator, the robustness against CGM noise was tested. A higher percentage of time in target was obtained with iPID as compared to standard PID with reduced time spent in hyperglycemia. CONCLUSION Two different T1D simulators tests showed that iPID detects meals and reacts faster to meal perturbations as compared to a classic PID. The intelligent part turns the controller to be more aggressive immediately after meals without neglecting safety. Further research is suggested to improve the computation of the intelligent part of iPID for such systems under actuator constraints. Any improvement can impact the overall performance of the model-free controller. SIGNIFICANCE The simple structure iPID is a step for PID-like controllers since it combines the classic PID nice properties with new adaptive features.
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Laguna Sanz AJ, Doyle FJ, Dassau E. An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions. J Diabetes Sci Technol 2017; 11:537-544. [PMID: 28745095 PMCID: PMC5505428 DOI: 10.1177/1932296816680632] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors. METHOD Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure. RESULTS Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered. CONCLUSIONS The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.
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Affiliation(s)
- Alejandro J. Laguna Sanz
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Eyal Dassau, PhD, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29, Oxford St, Office 317, Cambridge, MA, 02138, USA.
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Weimer J, Chen S, Peleckis A, Rickels MR, Lee I. Physiology-Invariant Meal Detection for Type 1 Diabetes. Diabetes Technol Ther 2016; 18:616-624. [PMID: 27704875 PMCID: PMC6528748 DOI: 10.1089/dia.2015.0266] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Fully automated artificial pancreas systems require meal detectors to supplement blood glucose level regulation, where false meal detections can cause unnecessary insulin delivery with potentially fatal consequences, and missed detections may cause the patient to experience extreme hyperglycemia. Most existing meal detectors monitor various measures of glucose rate-of-change to detect meals where varying physiology and meal content complicate balancing detector sensitivity versus specificity. METHODS We developed a novel meal detector based on a minimal glucose-insulin metabolism model and show that the detector is, by design, invariant to patient-specific physiological parameters in the minimal model. Our physiological parameter-invariant (PAIN) detector achieves a near-constant false alarm rate across all individuals and is evaluated against three other major existing meal detectors on a clinical type 1 diabetes data set. RESULTS In the clinical evaluation, the PAIN-based detector achieves an 86.9% sensitivity for an average false alarm rate of two alarms per day. In addition, for all false alarm rates, the PAIN-based detector performance is significantly better than three other existing meal detectors. In addition, the evaluation results show that the PAIN-based detector uniquely (as compared with the other meal detectors) has low variance in detection and false alarm rates across all patients, without patient-specific personalization. CONCLUSIONS The PAIN-based meal detector has demonstrated better detection performance than existing meal detectors, and it has the unique strength of achieving a consistent performance across a population with varying physiology without any individual-level parameter tuning or training.
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Affiliation(s)
- James Weimer
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sanjian Chen
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
- Address correspondence to: Sanjian Chen, PhD, Department of Computer and Information Science, University of Pennsylvania, 3330 Walnut Street, Levine 302, Philadelphia, PA 19104
| | - Amy Peleckis
- Division of Endocrinology, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael R. Rickels
- Division of Endocrinology, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Michael R. Rickels, MD, MS, Division of Endocrinology, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, 12-134 Smilow Center for Translational Research, 3400 Civic Center Boulevard, Philadelphia, PA 19104
| | - Insup Lee
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
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Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas. Processes (Basel) 2016. [DOI: 10.3390/pr4040035] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
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