Data-Driven Robust Control for a Closed-Loop Artificial Pancreas

Paoletti, Nicola; Liu, Kin Sum; Chen, Hongkai; Smolka, Scott A.; Lin, Shan

doi:10.1109/tcbb.2019.2912609

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…Proportional–integral–derivative (PID), fuzzy inference and model predictive control (MPC) are the most common approaches for AID systems, being the only ones with clinical validation so far [ 3 , 4 , 5 ]. MPC has been extensively used [ 6 , 7 , 8 , 9 ] and, when compared PID controllers, show better performance [ 10 , 11 ]. Nevertheless, Haidar [ 12 ] shows that fuzzy logic can still improve MPC performance and reduce the risk of hypoglycemia, which indicates the relevance of computational intelligence for the design of artificial pancreas.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

Farias

Bessa

2022

Bioengineering

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Type 1 diabetes mellitus is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes mellitus. Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values.

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Section: Introductionmentioning

confidence: 99%

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

Farias

Bessa

2022

Bioengineering

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“…The UVa/Padova simulator allows the incorporation of different meal scenarios for the virtual patient (VP) population, allowing researchers to analyze the effectiveness of a control algorithm [16][17][18][19][20][21][22], validate optimization and adaptation strategies for insulin delivery [23][24][25][26], develop disturbance detection algorithms for meals [27][28][29] and exercise [30], develop methods for mitigating the risks of hypoglycemia [31,32], and integrate machine learning algorithms into conventional diabetes therapy and bolus calculator for the treatment of T1D patients [33][34][35]. In the literature, the meal scenarios used for testing BG regulation effectiveness are based on typical values considering three meals per day [36][37][38][39][40][41][42][43][44][45][46][47]. However, in real life, the amount of carbohydrate intake and number of meals per day may vary patient to patient.…”

Section: Introductionmentioning

confidence: 99%

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

et al. 2021

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Preclinical testing and validation of therapeutic strategies developed for patients with type 1 diabetes (T1D) require a cohort of virtual patients (VPs). However, current simulators provide a limited number of VPs, lack real-life scenarios, and inadequately represent intra- and inter-day variability in insulin sensitivity and blood glucose (BG) profile. The generation of a realistic scenario was achieved by using the meal patterns, insulin profiles (basal and bolus), and exercise sessions estimated as disturbances using clinical data from a cohort of 14 T1D patients using the Medtronic 640G insulin pump provided by the Hospital Clínic de Barcelona. The UVa/Padova’s cohort of adult patients was used for the generation of a new cohort of VPs. Insulin model parameters were optimized and adjusted in a day-by-day fashion to replicate the clinical data to create a cohort of 75 VPs. All primary and secondary outcomes reflecting the BG profile of a T1D patient were analyzed and compared to the clinical data. The mean BG 166.3 versus 162.2 mg/dL ( = 0.19), coefficient of variation 32% versus 33% ( = 0.54), and percent of time in range (70 to 180 mg/dL) 59.6% versus 66.8% ( = 0.35) were achieved. The proposed methodology for generating a cohort of VPs is capable of mimicking the BG metrics of a real cohort of T1D patients from the Hospital Clínic de Barcelona. It can adopt the inter-day variations in the BG profile, similar to the observed clinical data, and thus provide a benchmark for preclinical testing of control techniques and therapy strategies for T1D patients.

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“…The population of people with diabetes reached 463 million in 2019 [1], and millions of them suffer from diabetes complications. Effective management can help to maintain the blood glucose (BG) levels of people with diabetes within a reasonable range and then prevent diabetes complications [2][3][4]. The continuous glucose monitoring (CGM) device is an effective tool to continuously monitor BG levels of users [5,6].…”

Section: Introductionmentioning

confidence: 99%

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

Xie

Yang

2021

Med Biol Eng Comput

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This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min.

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Data-Driven Robust Control for a Closed-Loop Artificial Pancreas

Cited by 11 publications

References 32 publications

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

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