BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Vancouver BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20201101T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20201216T033522Z UID:F2DFEBC5-A6A5-4F6B-9A45-2F9F3E10FED6 DTSTART;TZID=America/Vancouver:20201215T180000 DTEND;TZID=America/Vancouver:20201215T193000 DESCRIPTION:ABSTRACT:\nThe availability of large longitudinal data opens ne w opportunities exploiting statistical learning and deep convolutional neu ral networks in healthcare. We will overview our large study with data col lected from wireless devices from 200\,000 individuals for 2 years. We wil l observe how sleep changes significantly among this large population and is related to age and body mass index. We will also show how a person’s resting heart rate may be a uniquely individualized measure of their healt h\, with potential value for early detection of important physiologic chan ges.\nThis large study provides the baseline for DETECT\, our app-based\, nationwide clinical study enrolling individuals who routinely use a smartw atch or other wireless devices to determine if individualized tracking of changes in heart rate\, activity and sleep can provide early diagnosis and self-monitoring for COVID-19. In this talk\, we discuss how this program has been implemented and which insights for the individual and for public health are obtained by analyzing data from more than 36\,000 individuals. We show our recent results on the validation of this algorithm\, proving t hat it can identify COVID-19 positive cases by analyzing both self-reporte d symptoms and wearable sensor data.\n\nSpeaker(s): Giorgio Quer\, Ph.D.\, \n\nAgenda: \nABSTRACT:\nThe availability of large longitudinal data open s new opportunities exploiting statistical learning and deep convolutional neural networks in healthcare. We will overview our large study with data collected from wireless devices from 200\,000 individuals for 2 years. We will observe how sleep changes significantly among this large population and is related to age and body mass index. We will also show how a person ’s resting heart rate may be a uniquely individualized measure of their health\, with potential value for early detection of important physiologic changes.\nThis large study provides the baseline for DETECT\, our app-bas ed\, nationwide clinical study enrolling individuals who routinely use a s martwatch or other wireless devices to determine if individualized trackin g of changes in heart rate\, activity and sleep can provide early diagnosi s and self-monitoring for COVID-19. In this talk\, we discuss how this pro gram has been implemented and which insights for the individual and for pu blic health are obtained by analyzing data from more than 36\,000 individu als. We show our recent results on the validation of this algorithm\, prov ing that it can identify COVID-19 positive cases by analyzing both self-re ported symptoms and wearable sensor data.\n\nBellevue\, Washington\, Unite d States\, 98005\, Virtual: https://events.vtools.ieee.org/m/248181 LOCATION:Bellevue\, Washington\, United States\, 98005\, Virtual: https://e vents.vtools.ieee.org/m/248181 ORGANIZER:anewton@ieee.org SEQUENCE:3 SUMMARY:DETECT: Wearable Sensor Data to Predict COVID-19 and Viral Illnesse s URL;VALUE=URI:https://events.vtools.ieee.org/m/248181 X-ALT-DESC:Description: <br /><p>ABSTRACT:<br />The availability of large l ongitudinal data opens new opportunities exploiting statistical learning a nd deep convolutional neural networks in healthcare. We will overview our large study with data collected from wireless devices from 200\,000 indivi duals for 2 years. We will observe how sleep changes significantly among t his large population and is related to age and body mass index. We will al so show how a person&rsquo\;s resting heart rate may be a uniquely individ ualized measure of their health\, with potential value for early detection of important physiologic changes.<br />This large study provides the base line for DETECT\, our app-based\, nationwide clinical study enrolling indi viduals who routinely use a smartwatch or other wireless devices to determ ine if individualized tracking of changes in heart rate\, activity and sle ep can provide early diagnosis and self-monitoring for COVID-19. In this t alk\, we discuss how this program has been implemented and which insights for the individual and for public health are obtained by analyzing data fr om more than 36\,000 individuals. We show our recent results on the valida tion of this algorithm\, proving that it can identify COVID-19 positive ca ses by analyzing both self-reported symptoms and wearable sensor data.</p> <br /><br />Agenda: <br /><p>ABSTRACT:<br />The availability of large long itudinal data opens new opportunities exploiting statistical learning and deep convolutional neural networks in healthcare. We will overview our lar ge study with data collected from wireless devices from 200\,000 individua ls for 2 years. We will observe how sleep changes significantly among this large population and is related to age and body mass index. We will also show how a person&rsquo\;s resting heart rate may be a uniquely individual ized measure of their health\, with potential value for early detection of important physiologic changes.<br />This large study provides the baselin e for DETECT\, our app-based\, nationwide clinical study enrolling individ uals who routinely use a smartwatch or other wireless devices to determine if individualized tracking of changes in heart rate\, activity and sleep can provide early diagnosis and self-monitoring for COVID-19. In this talk \, we discuss how this program has been implemented and which insights for the individual and for public health are obtained by analyzing data from more than 36\,000 individuals. We show our recent results on the validatio n of this algorithm\, proving that it can identify COVID-19 positive cases by analyzing both self-reported symptoms and wearable sensor data.</p> END:VEVENT END:VCALENDAR