Modeling, Identification and Control of Physiological Systems
Members
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Bertoldo, Alessandra
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DallaMan, Chiara
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DelFavero, Simone
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Facchinetti, Andrea
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Monaro, Marco
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Piccinini, Francesca
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Schiavon, Michele
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Sparacino, Giovanni
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Toffolo, Gianna Maria
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Vettoretti, Martina
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Visentin, Roberto
Description
MATHEMATICAL MODELS OF GLUCOSE METABOLISM
- “Minimal” models of the glucose-insulin system to measure signals and parameters (such insulin sensitivity, beta-cell function, hepatic insulin extraction, hepatic glucose production, glucose utilization and rate of appearance of ingested glucose) from intravenous/oral tolerance tests.
- “Maximal” models of the glucose-insulin system for in silico simulation
- Models of the glucose-insulin system for use in control applications (artificial pancreas)
- Models for quantitative assessment of glucose kinetics at organ level (skeletal muscle)
IDENTIFICATION & PARAMETER ESTIMATION TECHNIQUES
- Parameter estimation techniques at both the individual and population level (nonlinear-mixed effects)
- Deconvolution techniques for biomedical signals analysis
SMART SENSORS FOR CONTINUOUS GLUCOSE MONITORING (CGM)
Denoising algorithms for signal-to-noise ratio improvement
- Fault-detection strategies for real-time detection of CGM sensor artefacts
- On-line (and off-line) algorithms for CGM calibration and accuracy enhancement
- Algorithms for the short-time prediction of future glucose levels (time-series approaches, neural networks)
GLUCOSE VARIABILITY IN DIABETES
- Indices for the quantification of glucose variability
- Parsimonious description of glucose variability
- Classification of patient's risk