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DTSTAMP:20240612T161928Z
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DTSTART;TZID=America/Los_Angeles:20240611T173000
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DESCRIPTION:Mobile sensors are often used in health to track and monitor he
 alth\, ranging from daily activities to diagnosing life-threatening condit
 ions\; however\, they are underutilized for substance use and its disorder
 s. Our work is focused on developing digital biomarkers from the physiolog
 ical data captured from wearable devices for addiction. Specifically\, we 
 build models that combine the multimodal sensor data from wearable devices
  to detect drug administration\, predict drug-induced mental states such a
 s drug craving and euphoria. We further show that integrating drug pharmac
 okinetics into these data-driven models enhances the accuracy of drug moni
 toring\, thereby increasing the generalizability and trust. A consistent p
 attern observed among these models was bias based on drug-usage history\; 
 therefore\, we develop a model that screens users and distinguishes opioid
  misusers from prescription users\, which would allow for more accurate pr
 escription of opioids\, minimizing the risk of addiction.\n\nCo-sponsored 
 by: Media Partner: Open Research Institute (ORI)\n\nSpeaker(s): Bhanu T. G
 ullapalli\, \n\nAgenda: \n- Invited talk from [Bhanu Teja Gullapalli](http
 s://www.linkedin.com/in/bhanu-teja-gullapalli/)\, PhD Candidate at the Uni
 versity of California San Diego.\n- Q/A Session\n\nVirtual: https://events
 .vtools.ieee.org/m/422258
LOCATION:Virtual: https://events.vtools.ieee.org/m/422258
ORGANIZER:upalmahbub@yahoo.com
SEQUENCE:37
SUMMARY:Harnessing Digital Biomarkers of Substance Use and Addiction with L
 arge-Scale Mobile Sensor Data
URL;VALUE=URI:https://events.vtools.ieee.org/m/422258
X-ALT-DESC:Description: <br /><p>Mobile sensors are often used in health to
  track and monitor health\, ranging from daily activities to diagnosing li
 fe-threatening conditions\; however\, they are underutilized for substance
  use and its disorders. Our work is focused on developing digital biomarke
 rs from the physiological data captured from wearable devices for addictio
 n. Specifically\, we build models that combine the multimodal sensor data 
 from wearable devices to detect drug administration\, predict drug-induced
  mental states such as drug craving and euphoria. We further show that int
 egrating drug pharmacokinetics into these data-driven models enhances the 
 accuracy of drug monitoring\, thereby increasing the generalizability and 
 trust. A consistent pattern observed among these models was bias based on 
 drug-usage history\; therefore\, we develop a model that screens users and
  distinguishes opioid misusers from prescription users\, which would allow
  for more accurate prescription of opioids\, minimizing the risk of addict
 ion.&nbsp\;</p><br /><br />Agenda: <br /><div class="heading">\n<ul>\n<li>
 Invited talk from&nbsp\;<a href="https://www.linkedin.com/in/bhanu-teja-gu
 llapalli/">Bhanu Teja Gullapalli</a>\, PhD Candidate at the University of 
 California San Diego.&nbsp\;</li>\n<li>Q/A Session</li>\n</ul>\n</div>
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