BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//USC Big Data Health Science Center - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:USC Big Data Health Science Center
X-ORIGINAL-URL:https://bigdata.sc.edu
X-WR-CALDESC:Events for USC Big Data Health Science Center
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20220323T110000
DTEND;TZID=UTC:20220323T120000
DTSTAMP:20260610T145742
CREATED:20220223T165853Z
LAST-MODIFIED:20220223T165853Z
UID:4343-1648033200-1648036800@bigdata.sc.edu
SUMMARY:Studying Substance use From Social Media Using Natural Language Processing and Machine Learning Methods
DESCRIPTION:Where: Virtual via Zoom (registration required) \nHow: Register at https://us02web.zoom.us/webinar/register/WN_f4s0gmlYTYO6h4S0sceSjw \nSeminar Description: In this talk\, I will outline the progress we have made over recent years in utilizing social media data via natural language processing (NLP) and machine learning methods for studying substance use. Substance use and the associated overdose epidemic has been continuing in the United States (US) for decades. Over 270 people on average die every day from substance-related overdoses. However\, surveillance mechanisms are laggy and we only get to know about the state of the epidemic once substantial damage has already been done. For example\, in February 2022\, we only have provisional estimates from early 2021. To address this lag\, we are working towards utilizing social media data for automatically tracking and estimating substance use trends (including opioids\, benzodiazepines\, and stimulants). The talk will mostly focus on our NIH/NIDA funded project that focuses on prescription drugs (R01DA046619). I will also present some updates from our research collaborations with the CDC\, which focus specifically on substance use trends during COVID-19. \nAbout the Speaker: Dr. Abeed Sarker is an assistant professor at the Department of Biomedical Informatics\, School of Medicine\, Emory University. He also serves as program faculty at the Department of Computer Science\, Emory University and Department of Biomedical Engineering\, Emory University and Georgia Institute of Technology.  His research interests lie at the intersection of natural language processing\, applied machine learning and social media. Much of his recent work has focused on utilizing social media big data to study substance use\, including nonmedical use of prescription medications.  He is currently leading a number of research projects in this space funded primarily by the National Institute on Drug Abuse of the National Institutes of Health\, the Centers for Disease Control and Prevention and Emory University.
URL:https://bigdata.sc.edu/event/studying-substance-use-from-social-media-using-natural-language-processing-and-machine-learning-methods/
END:VEVENT
END:VCALENDAR