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TZID:America/New_York
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DTSTART:20241103T020000
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DESCRIPTION:Perioperative healthcare data is vast and heterogenous and pro
 vides a rich resource for the development of clinically meaningful predic
 tive models. Several data modalities exist - from structured electronic h
 ealth record data\, imaging\, and clinical notes. As we move toward desig
 ning predictive models in healthcare that incorporate multimodal data\, i
 t is important to understand different approaches in harmonizing data whi
 le maintaining scalability. The lecture will provide an overview of Gabri
 el's research in using AI to predict actionable outcomes for the intensiv
 e care unit\, acute pain services\, and preoperative optimization with an
 esthesia preoperative clinics. How we can turn these predictive models fr
 om academic work to EHR deployment will also be discussed.\n\nSpeaker:\nR
 odney Gabriel\, MD \n\nAdmission:\nFree\n\nFood:\nLunch\n\nDetails URL:\n
 https://medicine.yale.edu/event/bids-special-seminar-9-3/\n
DTEND;TZID=America/New_York:20260903T130000
DTSTAMP:20260907T150423Z
DTSTART;TZID=America/New_York:20260903T120000
LOCATION:ZOOM\, URL: https://yale.zoom.us/j/97037559157?from=addon
SEQUENCE:0
STATUS:Confirmed
SUMMARY:BIDS Special Seminar
UID:34c90520-aa71-443d-981d-52906fa016d1
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