IEEEEMBS-Boston Presents "Digital Twins for Diabetes: Modeling Glucose Regulation from Wearable Data"
Continuous glucose monitors, insulin pumps, and consumer wearables now produce uninterrupted streams of real-life physiological data, yet turning those streams into reliable physiological insight remains an open engineering problem. This talk follows a modeling program that moves from tightly controlled clinical studies into everyday outpatient conditions: oral glucose minimal models that estimate gastric retention, glucose rate of appearance, and insulin sensitivity from type 1 diabetes data collected in free living; nonlinear mixed effects models that quantify how dietary fat and protein reshape the postprandial glucose excursion; and mechanistic models of physical activity and glucose tolerance in type 2 diabetes built from CGM and smartwatch signals.
In this webinar, Dr. Edoardo Faggionato traces a modeling program that moves from tightly controlled clinical studies into everyday outpatient conditions.
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Edoardo
Digital Twins for Diabetes: Modeling Glucose Regulation from Wearable Data
Biography:
Dr. Edoardo Faggionato is a Senior Algorithm Research Engineer at Insulet B.V. in Utrecht, The Netherlands. He received his PhD in Information Engineering from the University of Padova. His postdoctoral work there was on glucose and glucagon kinetics, insulin absorption, and model predictive control, supported by NIH and Italian PRIN PNRR projects.