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DTSTART:20270314T030000
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BEGIN:VEVENT
DTSTAMP:20261112T212547Z
UID:1AD5A2F2-EFC5-4FC9-9CF3-FF234ACC08CB
DTSTART;TZID=America/New_York:20261112T120000
DTEND;TZID=America/New_York:20261112T130000
DESCRIPTION:Continuous glucose monitors\, insulin pumps\, and consumer wear
 ables now produce uninterrupted streams of real-life physiological data\, 
 yet turning those streams into reliable physiological insight remains an o
 pen engineering problem. This talk follows a modeling program that moves f
 rom tightly controlled clinical studies into everyday outpatient condition
 s: oral glucose minimal models that estimate gastric retention\, glucose r
 ate of appearance\, and insulin sensitivity from type 1 diabetes data coll
 ected in free living\; nonlinear mixed effects models that quantify how di
 etary fat and protein reshape the postprandial glucose excursion\; and mec
 hanistic models of physical activity and glucose tolerance in type 2 diabe
 tes built from CGM and smartwatch signals.\n\nIn this webinar\, Dr. Edoard
 o Faggionato traces a modeling program that moves from tightly controlled 
 clinical studies into everyday outpatient conditions.\n\nSpeaker(s): \, Ed
 oardo\n\nVirtual: https://events.vtools.ieee.org/m/591555
LOCATION:Virtual: https://events.vtools.ieee.org/m/591555
ORGANIZER:ieeebostonembs@gmail.com
SEQUENCE:35
SUMMARY:IEEEEMBS-Boston Presents "Digital Twins for Diabetes: Modeling Gluc
 ose Regulation from Wearable Data"
URL;VALUE=URI:https://events.vtools.ieee.org/m/591555
X-ALT-DESC:Description: <br /><p class="">Continuous glucose monitors\, ins
 ulin pumps\, and consumer wearables now produce uninterrupted streams of&n
 bsp\;real-life physiological data\, yet turning those streams into reliabl
 e physiological insight remains an open engineering problem. This talk fol
 lows a modeling program that moves from tightly controlled clinical studie
 s into everyday outpatient conditions: oral glucose minimal models that es
 timate gastric retention\, glucose rate of appearance\, and insulin sensit
 ivity from type 1 diabetes data collected in free living\; nonlinear mixed
  effects models that quantify how dietary fat and protein reshape the post
 prandial glucose excursion\; and mechanistic models of physical activity a
 nd glucose tolerance in type 2 diabetes built from CGM and smartwatch sign
 als.</p>\n<p class="">In this webinar\, Dr. Edoardo Faggionato traces a mo
 deling program that moves from tightly controlled clinical studies into ev
 eryday outpatient conditions.</p>
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