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SUMMARY:BSU Seminar: "Uncertainty Quantification in the Digital Medicine E
 ra" - Marcos Matabuena\, Harvard University 
DTSTART:20240307T140000Z
DTEND:20240307T150000Z
UID:TALK211684@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:The widespread use of wearable devices and smartphones has rev
 olutionized the field of digital medicine\, enabling non-invasive\, contin
 uous monitoring of various physiological parameters. This technological ad
 vancement is crucial for personalized medical and public health interventi
 ons. The data collected in free-living conditions are continuous (function
 al)\, collected at second or minute level resolution\, and are multilevel\
 , reflecting daily\, weekly\, and monthly patterns. This data complexity n
 ecessitates innovative statistical methods to address challenges like unce
 rtainty quantification in predicting future patient trajectories.\n\nThis 
 presentation aims to achieve two primary objectives. First\, it examines t
 he impact of digital medicine on routine medical tasks\, including disease
  diagnosis\, management\, and treatment prescription\, with a special focu
 s on diabetes care and physical activity interventions. These areas exempl
 ify the transformative effect of digital medicine. Second\, the presentati
 on introduces new statistical approaches developed by the author for quant
 ifying uncertainty in outcome prediction. Tailored to the challenges of di
 gital medicine\, these algorithms are based on conformal prediction and Ba
 yesian methods. Their practical applications are demonstrated using data f
 rom continuous glucose monitors and accelerometers\, showcasing their effe
 ctiveness in various modeling tasks such as determining diagnosis\, charac
 terization of disease phenotypes\, creation of new rules for monitoring ph
 ysical activity profiles\, and establishment of new biomarkers.
LOCATION:MRC Biostatistics Unit\, East Forvie Building\, Forvie Site Robin
 son Way Cambridge CB2 0SR.
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