BEGIN:VCALENDAR
PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260819T074651Z
DTSTART:20260916T120000Z
DTEND:20260916T130000Z
SUMMARY:Webinar - Using digital health data to support early diagnosis of
  cardiovascular disease 
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}w4u-mszsg2s
 j-tz8chk
DESCRIPTION:In this Health Research from Home webinar\, Professor Allan L
 awrie will explore how digital health data can support earlier diagnosis
  of rare diseases\, specifically in the field of rare cardiovascular dis
 ease. \n\nBeginning with the use of electronic health records to identif
 y people at higher risk\, his research has expanded to include informati
 on collected from smartphones and wearable devices\, providing new insig
 hts into people's health in everyday life. \n\nDrawing on collaborations
  with Stanford University through the My Heart Counts app\, Allan will d
 iscuss the potential of combining continuous wearable data with healthca
 re records. He will share key lessons learned from model development and
  validation\, including why promising models may fail to generalise acro
 ss different populations and how differences in control groups can influ
 ence performance. \n\nAllan will also present current work being conduct
 ed in parallel in the United States and the United Kingdom\, including t
 he development of a cross-platform digital health research infrastructur
 e that integrates data from Android and iOS devices\, as well as wearabl
 e technologies such as Oura\, Fitbit\, and Withings. Through the inclusi
 on of ECG data\, raw sensor data and general PPG\, this research aims to
  develop some generalizable\, translatable models not just for early det
 ection but also remote monitoring. \n\nFinally\, this session will also 
 explore the next phase of the platform: using large language models and 
 behavioural science to deliver personalised\, adaptive health interventi
 ons. By combining prompt engineering\, reinforcement learning\, and real
 -time behavioural feedback\, the project aims to optimise "just-in-time"
  digital nudges that encourage healthier behaviours based on an individu
 al's characteristics and responses. 
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
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END:VCALENDAR