BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Central BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210430T205659Z UID:338CD2A6-1F3E-4F48-9541-0E4E68B94283 DTSTART;TZID=Canada/Central:20210514T110000 DTEND;TZID=Canada/Central:20210514T115900 DESCRIPTION:The IEEE Ottawa Joint Chapter of Communications Society\, Consu mer Electronics Society\, and Broadcast Technology Society (ComSoc/CESoc/B TS)\, IEEE Toronto Chapter (ComSoc/BTS)\, IEEE ComSoc Montreal Chapter (Co mSoc)\, IEEE Ottawa Educational Activities (EA)\, IEEE Ottawa Women In Eng ineering (WIE)\, IEEE Ottawa Young Professionals (YP)\, and Algonquin Coll ege Student Branch (ACSB) in conjunction with School of Advanced Technolog y\, Algonquin College are inviting all interested IEEE members and other e ngineers\, technologists\, and students to ComSoc Distinguished Lecture (w ebinar) on\n\nAI to Enable Digital Medicine and Detect COVID-19\n\nGiorgio Quer\, PhD\, Dir. of AI\,\n\nScripps Research Translational Institute\, L a Jolla\, California\, USA\n\nDATE: Friday May 14\, 2021\n\nTIME: 11:00 a. m. – 12:00 p.m. ET\n\nPLACE: Webinar via Zoom\n\nREGISTRATION: https://e vents.vtools.ieee.org/m/236964.\n\nADMISSION: Free\, but the registration in advance is strongly encourage\n\nFor any additional information please contact: [Wahab Almuhtadi](mailto:Almuhtadi@ieee.org) or [Eman Hammad](mai lto:eman.hammad.ca@ieee.org?subject=DL%20event)\n\nAbstract\n\nDigitalize human beings using biosensors to track our complex physiologic system\, pr ocess the large amount of data generated with artificial intelligence (AI) and change clinical practice towards individualized medicine: these are t he goals of digital medicine. In this talk\, we discuss how to design AI s olutions in the clinical space and what are the key aspects to make a diff erence. We focus on two critical clinical topics that need AI: 1) atrial f ibrillation (AF)\, and 2) viral illnesses (COVID-19). AF is the most commo n sustained cardiac arrhythmia\, associated with stroke\, heart failure an d coronary artery disease. AF detection from single-lead electrocardiograp hy (ECG) recordings is still an open problem\, as AF events may be episodi c and the signal noisy. We conduct a thoughtful analysis of recent convolu tional neural network architectures developed in the computer vision field \, redesigned to be suitable for a one-dimensional signal\, and we evaluat e their performance in the detection of AF using 200 thousand seconds of E CG\, highlighting the potential and pitfall of this technology. We also di scuss how to explain (global and local post hoc explanations) this AI mode l for AF detection using features that are commonly used by a cardiologist .\n\nTo tackle the problem of COVID-19\, we start with an overview of cont inuous\, passively monitored vital signs from 200\,000 individuals wearing a Fitbit wearable device for 2 years. This large study provides the basel ine for DETECT\, our app-based\, nationwide clinical study enrolling indiv iduals who routinely use a smartwatch or other wireless devices to determi ne if individualized tracking of changes in heart rate\, activity and slee p can provide early diagnosis and self-monitoring for COVID-19. We analyze data from more than 36\,000 individuals\, showing how we can discriminate (on an individual level) between COVID-19 and other types of infections. We discuss how this can impact both the individual and public health\, and how the use of AI can be a game changer in this fight against the virus.\ n\nSpeaker’s Bio\n\nGiorgio Quer is the Director of Artificial Intellige nce at the Scripps Research Translational Institute\, where he is leading the Data Science and Analytics team within the All of Us Research Program ’s Participant Center (NIH).\n\nHis research focuses on artificial intel ligence and probabilistic modeling applied to heterogeneous data signals\, in order to extract key information and make predictions on future occurr ences based on past data. He is involved in several digital medicine initi atives within the Scripps Research Digital Trials Center. For the DETECT s tudy\, he is developing algorithms to predict COVID-19 and other viral inf ections from wearable sensor data. He is responsible for collaborations wi th several industry partners\, studying changes in heart rate and sleep da ta monitored by commercial wearable devices. He is also interested in the detection and modeling of atrial fibrillation from single-lead ECG signals . He is leading the collaboration with the Halicioglu Data Science Institu te at UC San Diego towards the development of new AI models for health dat a.\n\nHe received his Ph.D. degree in Information Engineering from the Uni versity of Padova\, Italy\, and he continued his studies as a Postdoctoral researcher with the Qualcomm Institute at the University of California Sa n Diego. He is a Senior Member of the IEEE and a Distinguished Lecturer fo r the IEEE Communications society.\n\nCo-sponsored by: IEEE Ottawa ComSoc/ CESoc/BTS\, IEEE ComSoc Montreal Chapter\, IEEE Ottawa Educational Activit ies\, IEEE Ottawa Women In Engineering (WIE)\, IEEE Ottawa Young Professio nals (YP)\, and Algonquin College Student Branch (ACSB) \n\nSpeaker(s): Dr . Giorgio Quer\, \n\nToronto\, Ontario\, Canada\, Virtual: https://events. vtools.ieee.org/m/271155 LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o rg/m/271155 ORGANIZER:eman.hammad.ca@ieee.org SEQUENCE:4 SUMMARY:ComSoc Distinguished Lecture: AI to Enable Digital Medicine and Det ect COVID-19 URL;VALUE=URI:https://events.vtools.ieee.org/m/271155 X-ALT-DESC:Description: <br /><p>The <strong>IEEE Ottawa Joint Chapter of C ommunications Society\, Consumer Electronics Society\, and Broadcast Techn ology Society</strong> (<strong>ComSoc/CESoc/BTS</strong>)\, <strong>IEEE Toronto Chapter (ComSoc/BTS)\, IEEE ComSoc Montreal Chapter (ComSoc)\, IEE E Ottawa Educational Activities (EA)\, IEEE Ottawa Women In Engineering (W IE)\, IEEE Ottawa Young Professionals (YP)\, and Algonquin College Student Branch (ACSB) </strong>in conjunction with School of Advanced Technology\ , Algonquin College are inviting all interested IEEE members and other eng ineers\, technologists\, and students to ComSoc Distinguished Lecture (web inar) on</p>\n<p><strong>AI to Enable Digital Medicine and Detect COVID-19 </strong></p>\n<p><strong>Giorgio Quer</strong><strong>\, PhD\, Dir. of AI \,</strong></p>\n<p><strong>Scripps Research Translational Institute\, La Jolla\, California\, USA</strong></p>\n<p>&nbsp\;</p>\n<p><strong>&nbsp\;< /strong></p>\n<p><strong>DATE: Friday May 14\, 2021 </strong></p>\n<p><str ong>TIME: 11:00 a.m. &ndash\; 12:00 p.m. ET</strong></p>\n<p><strong>&nbsp \;</strong></p>\n<p><strong>PLACE: </strong>Webinar via Zoom</p>\n<p>&nbsp \;</p>\n<p><strong>REGISTRATION</strong>: https://events.vtools.ieee.org/m /236964.</p>\n<p><strong>ADMISSION</strong>: Free\, but the registration i n advance is strongly encourage</p>\n<p>For any additional information ple ase contact: <a href="mailto:Almuhtadi@ieee.org">Wahab Almuhtadi</a> or <a href="mailto:eman.hammad.ca@ieee.org?subject=DL%20event">Eman Hammad</a>< /p>\n<p><br /><strong><u>Abstract </u></strong><strong>&nbsp\;</strong></p >\n<p>&nbsp\;</p>\n<p>Digitalize human beings using biosensors to track ou r complex physiologic system\, process the large amount of data generated with artificial intelligence (AI) and change clinical practice towards ind ividualized medicine: these are the goals of digital medicine. In this tal k\, we discuss how to design AI solutions in the clinical space and what a re the key aspects to make a difference. We focus on two critical clinical topics that need AI: 1) atrial fibrillation (AF)\, and 2) viral illnesses (COVID-19). AF is the most common sustained cardiac arrhythmia\, associat ed with stroke\, heart failure and coronary artery disease. AF detection f rom single-lead electrocardiography (ECG) recordings is still an open prob lem\, as AF events may be episodic and the signal noisy. We conduct a thou ghtful analysis of recent convolutional neural network architectures devel oped in the computer vision field\, redesigned to be suitable for a one-di mensional signal\, and we evaluate their performance in the detection of A F using 200 thousand seconds of ECG\, highlighting the potential and pitfa ll of this technology. We also discuss how to explain (global and local po st hoc explanations) this AI model for AF detection using features that ar e commonly used by a cardiologist.</p>\n<p>To tackle the problem of COVID- 19\, we start with an overview of continuous\, passively monitored vital s igns from 200\,000 individuals wearing a Fitbit wearable device for 2 year s. This large study provides the baseline for DETECT\, our app-based\, nat ionwide clinical study enrolling individuals who routinely use a smartwatc h or other wireless devices to determine if individualized tracking of cha nges in heart rate\, activity and sleep can provide early diagnosis and se lf-monitoring for COVID-19. We analyze data from more than 36\,000 individ uals\, showing how we can discriminate (on an individual level) between CO VID-19 and other types of infections. We discuss how this can impact both the individual and public health\, and how the use of AI can be a game cha nger in this fight against the virus.</p>\n<p>&nbsp\;</p>\n<p><strong><u>S peaker&rsquo\;s Bio </u></strong></p>\n<p>&nbsp\;</p>\n<p><strong>Giorgio Quer </strong>is the Director of Artificial Intelligence at the Scripps Re search Translational Institute\, where he is leading the Data Science and Analytics team within the All of Us Research Program&rsquo\;s Participant Center (NIH).</p>\n<p>His research focuses on artificial intelligence and probabilistic modeling applied to heterogeneous data signals\, in order to extract key information and make predictions on future occurrences based on past data. He is involved in several digital medicine initiatives withi n the Scripps Research Digital Trials Center. For the DETECT study\, he is developing algorithms to predict COVID-19 and other viral infections from wearable sensor data. He is responsible for collaborations with several i ndustry partners\, studying changes in heart rate and sleep data monitored by commercial wearable devices. He is also interested in the detection an d modeling of atrial fibrillation from single-lead ECG signals. He is lead ing the collaboration with the Halicioglu Data Science Institute at UC San Diego towards the development of new AI models for health data.</p>\n<p>& nbsp\;</p>\n<p>He received his Ph.D. degree in Information Engineering fro m the University of Padova\, Italy\, and he continued his studies as a Pos tdoctoral researcher with the Qualcomm Institute at the University of Cali fornia San Diego. He is a Senior Member of the IEEE and a Distinguished Le cturer for the IEEE Communications society.</p> END:VEVENT END:VCALENDAR