| 1. |
- Cescon, Marzia, et al.
(författare)
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Adaptive Subspace-based prediction of T1DM glycemia
- 2011
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Konferensbidrag (refereegranskat)abstract
- Blood glucose levels fluctuate widely in Type 1 diabetic patients expecially during stressful situations, intercurrent illness, exercise and changes in meal composition. Furthermore, inter- and intra-subject variability make the prediction of such fluctuations an even harder task. The paper deals with the application of online data-driven multi-step subspace-based patient-specific predictor models to T1DM glycemia prediction, exploiting the interplay between previously injected insulin, meal intake and eventually vital signs. When the unknown underlying model is changing over time we believe such an adaptive scheme may constitute a valuable step towards the development of an advisory tool capable of informing the patient at any time about the evolution of glycemia and possibly give advices on the most appropriate control action to take.
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| 2. |
- Cescon, Marzia, et al.
(författare)
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Glycemic Trend Prediction Using Empirical Model Identification
- 2009
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Ingår i: Proc. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (CDC2009 & CCC 2009). ; , s. 3501-3506
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Konferensbidrag (refereegranskat)abstract
- Using methods of system identification and prediction, we investigate near-future prediction of individual specific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX- and ARMAX-based predictors was done. Predictions over 30 minutes look-ahead were capable to track glucose variation even in sensible ranges for estimation data, but not on validation data.
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| 3. |
- Cescon, Marzia, et al.
(författare)
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Identification of Individualized Empirical Models of Carbohydrate and Insulin Effects on T1DM Blood Glucose Dynamics
- 2014
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Ingår i: International Journal of Control. - : Informa UK Limited. - 0020-7179 .- 1366-5820. ; 87:7, s. 1438-1453
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Cescon, Marzia, et al.
(författare)
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Impulsive Predictive Control of T1DM Glycemia: an In-Silico Study
- 2013
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Ingår i: Proc. ASME 2012 5th Annual Dynamic Systems and Control Conference & JSME 2012 11th Motion and Vibration Conference (DSCC2012-MOVIC 2012), Oct 17-19, 2012, Fort Lauderdale, Florida, USA.. ; , s. 319-326
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Konferensbidrag (refereegranskat)
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| 5. |
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| 6. |
- Cescon, Marzia, et al.
(författare)
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Individualized Empirical Models of Carbohydrate and Insulin Effects on T1DM Blood Glucose Dynamics
- 2013
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Ingår i: Proc. 2013 IEEE Multi-conference on Systems and Control (MSC2013).
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Konferensbidrag (refereegranskat)abstract
- ne of the main limiting factors in improving glucose control for T1DM subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing magnitude and duration of such effects would be useful not only for patients and physicians but also for the development of a controller targeting glycemia regulation. Therefore, in this paper we focus on estimating low-complexity yet physiologically sound and individualized 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 identified. The estimation strategy is data-driven and exploits a database in which meals and insulin boluses are separated in time, allowing the unique identification of the model parameters.
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| 7. |
- Cescon, Marzia
(författare)
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Linear Modeling and Prediction in Diabetes Physiology
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. The development of a prediction engine capable of personalized on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise would therefore be a valuable initiative towards an improved management of the desease. This thesis presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 minutes, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects. In order to address model-based control for blood glucose regulation, low-order, individualized, data-driven, stable, physiological relevant models were identified from a population of 9 T1DM patients data. Model structures include: autoregressive moving average with exogenous inputs (ARMAX) models and state-space models.ARMAX multi-step-ahead predictors were estimated by means of least-squares estimation; next regularization of the autoregressive coefficients was introduced. ARMAX-based predictors and zero-order hold were computed to allow comparison.Finally, preliminary results on subspace-based multi-step-ahead multivariate predictors is presented.
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| 8. |
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| 9. |
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| 10. |
- Cescon, Marzia
(författare)
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Modeling and Prediction in Diabetes Physiology
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction.
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