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DTSTART:20190310T030000
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BEGIN:VEVENT
DTSTAMP:20190817T144238Z
UID:74433342-6A3C-48EF-8F1E-0FAD779951CB
DTSTART;TZID=America/New_York:20190430T183000
DTEND;TZID=America/New_York:20190430T203000
DESCRIPTION:Computing continues to change the landscape of nearly all domai
 ns\, medicine included. For instance\, drug resistance is predicted and av
 oided via data mining applications\; radiological reading errors are detec
 ted and prevented via natural language processing\; and disease outbreak i
 s detected early via text mining techniques. These are but just some examp
 les where computing is reshaping medical practice. Specifically\, we descr
 ibe the monitoring of social media to detect disease outbreak and describe
  the implications of such surveillance schemes to healthcare planning for 
 a major children-focused hospital. We continue by describing how conventio
 nal mining approaches significantly improve urinary tract infection treatm
 ent plans as developed jointly with and for another major hospital. Finall
 y\, we describe automated means for the detection of differences in radiol
 ogical readings and describe how such detection schemes are used in yet a 
 third major hospital.\n\nCo-sponsored by: Society for Social Implications 
 of Technology Chapter \; Baltimore EMBS Chapter\; Wasj/NoVa EMBS Chapter\n
 \nSpeaker(s): Prof. Ophir Frieder\, \n\nAgenda: \n6:30 PM to 7:00 PM - Ref
 reshments and Networking\n\n7:00 PM - 7:05 PM - Chapter announcements and 
 Speaker Introduction\n\n7:05 PM - Talk\n\nRoom: Meeting Room 2\, Bldg: Dol
 ley Madison Library\, 1244 Oak Ridge Ave\, McLean\, Virginia\, United Stat
 es
LOCATION:Room: Meeting Room 2\, Bldg: Dolley Madison Library\, 1244 Oak Rid
 ge Ave\, McLean\, Virginia\, United States
ORGANIZER:murtyp@ieee.org
SEQUENCE:21
SUMMARY:Computing Medicine / National Capital Area Computer Society Chapter
  Distinguished Lecture
URL;VALUE=URI:https://events.vtools.ieee.org/m/194779
X-ALT-DESC:Description: <br /><p>Computing continues to change the landscap
 e of nearly all domains\, medicine included.&nbsp\; For instance\, drug re
 sistance is predicted and avoided via data mining applications\; radiologi
 cal reading errors are detected and prevented via natural language process
 ing\; and disease outbreak is detected early via text mining techniques.&n
 bsp\; These are but just some examples where computing is reshaping medica
 l practice.&nbsp\; Specifically\, we describe the monitoring of social med
 ia to detect disease outbreak and describe the implications of such survei
 llance schemes to healthcare planning for a major children-focused hospita
 l.&nbsp\; We continue by describing how conventional mining approaches sig
 nificantly improve urinary tract infection treatment plans as developed jo
 intly with and for another major hospital. Finally\, we describe automated
  means for the detection of differences in radiological readings and descr
 ibe how such detection schemes are used in yet a third major hospital.</p>
 <br /><br />Agenda: <br /><p>6:30 PM to 7:00 PM - Refreshments and Network
 ing</p>\n<p>7:00 PM - 7:05 PM - Chapter announcements and Speaker Introduc
 tion</p>\n<p>7:05 PM - Talk</p>
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