A diabetes management system empowering patients to reach optimised glucose control: From monitor to advisor

Poulsen, Jens Ulrik; Avogaro, Angelo; Chauchard, Fabien; Cobelli, Claudio; Johansson, Rolf; Niţă, Lucian; Pogose, Mike; Re, Luigi del; Renard, Éric; Sampath, Sivananthan; Saudek, F; Skillen, Michael D.; Soendergaard, Jacob

doi:10.1109/iembs.2010.5626313

Cited by 24 publications

(13 citation statements)

References 5 publications

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“…CareLink Pro 3, Medtronic Diabetes' software for healthcare providers, offers detailed analyses and Pro is designed for retrospective data analysis by healthcare providers, other software is intended for real-time use by patients. The DIAdvisor, an investigational European system sponsored in part by Novo Nordisk, uses CGM data to predict glucose levels 20 min into the future and advise patients accordingly [27]. The most obvious application for such onboard predictive software will be in artificial pancreas products and their precursors.…”

Section: Analyzing Continuous Glucose Monitoring Datamentioning

confidence: 99%

Continuous Glucose Monitoring: Professional and Real Time

Zisser¹,

Lane²,

Shivers³

2014

Frontiers in Diabetes

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Continuous glucose monitors are a valuable tool to obtain tight glycemic control in people with diabetes. Since 1999 when the first continuous glucose monitor became commercially available, continuous glucose monitors have continued to evolve, becoming more accurate and user-friendly. Novel uses have also been discovered, including tracking glucose during sleep and finding trends in glucose levels that may lead to diet or exercise changes. While continuous glucose monitors are continually advancing in accuracy and reliability, their 'hassle factor' and invasiveness have investigators seeking alternative routes of glucose measurement. New models of continuous glucose monitors are being pursued including implantable, intraperitoneal, and fluorescence-based glucose sensors, as well as noninvasive models that are used on the surface of the skin. Regardless of system type, the ultimate goal is to find the continuous glucose monitor that is best-suited for use in systems that automate insulin delivery partially (e.g. low glucose suspend) or entirely (e.g. 'fully closed-loop' artificial pancreas).A continuous glucose monitor is a device that measures the concentration of glucose in the subcutaneous (interstitial) fluid, automatically and periodically. Compared to self-monitoring with a glucose meter, continuous glucose monitoring (CGM) measurements can give patients and healthcare providers a better understanding of glycemic trends and patterns. This is because CGM provides much more data than selfmonitoring and can be quite accurate in portraying the direction and speed of glucose changes. However, individual glucose measurements tend to be less accurate with

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Section: Analyzing Continuous Glucose Monitoring Datamentioning

confidence: 99%

Continuous Glucose Monitoring: Professional and Real Time

Zisser¹,

Lane²,

Shivers³

2014

Frontiers in Diabetes

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“…The performance of the current version of the algorithm has been demonstrated recently in the presentation of the project ''DIAdvisor.'' 15 An input for the algorithm consists of six CGM readings from the past 25 min. Note that in contrast to some other studies, 5,7,8 no data preprocessing/smoothing has been made before applying our basic glucose predictor.…”

Section: The Prediction-error Grid Analysismentioning

confidence: 99%

Assessment of Blood Glucose Predictors: The Prediction-Error Grid Analysis

Sampath

Naumova

Man

et al. 2011

Diabetes Technology & Therapeutics

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We propose a new CG-EGA, the PRED-EGA, for the assessment of glucose predictors. The presented analysis shows that, compared with the straightforward application of the CG-EGA, the PRED-EGA gives a significant reduction of the misclassification cases. A reduction by a factor of at least 4 was observed in the study. Moreover, the PRED-EGA is much more robust against uncertainty in the input and references.

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“…When a therapeutic action is suggested by the algorithm, the patient can accept or reject it, remaining firmly in the loop. The development of such decision support system was the focus of the major European project DIAdviso TM (DIadvisor, 2008;Poulsen et al, 2010;The DIAdvisor Consortium, 2012).…”

Section: Introduction 11 Diabetes Mellitusmentioning

confidence: 99%

Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics

Cescon

Johansson

Renard

et al. 2014

International Journal of Control

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One of the main limiting factors in improving glucose control for type 1 diabetes mellitus (T1DM) subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing the magnitude and duration of such effects would be useful not only for patients and physicians, but also for the development of a controller targeting glycaemia regulation. Therefore, in this paper we focus on estimating low-complexity yet physiologically sound and individualised multi-input single-output (MISO) models of the glucose metabolism in T1DM able to reflect the basic dynamical features of the glucose-insulin metabolic system in response to a meal intake or an insulin injection. The models are continuous-time second-order transfer functions relating the amount of carbohydrate of a meal and the insulin units of the accordingly administered dose (inputs) to plasma glucose evolution (output) and consist of few parameters clinically relevant to be estimated. The estimation strategy is continuous-time data-driven system identification and exploits a database in which meals and insulin boluses are separated in time, allowing the unique identification of the model parameters. © 2014 © 2014 Taylor & Francis

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A diabetes management system empowering patients to reach optimised glucose control: From monitor to advisor

Abstract: The DIAdvisor is an EC/FP7 funded project aiming at the development of a Blood Glucose prediction device which uses easily available information to optimise the therapy of patients with diabetes.

Cited by 24 publications

References 5 publications

Continuous Glucose Monitoring: Professional and Real Time

Continuous Glucose Monitoring: Professional and Real Time

Assessment of Blood Glucose Predictors: The Prediction-Error Grid Analysis

Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics

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