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DTSTAMP:20250822T115808Z
LOCATION:Room 6.0D13
DTSTART;TZID=Europe/Stockholm:20250617T160000
DTEND;TZID=Europe/Stockholm:20250617T163000
UID:submissions.pasc-conference.org_PASC25_sess107_msa283@linklings.com
SUMMARY:Data-Driven Agent Based Modeling for Precision Public Health
DESCRIPTION:Heidi Hanson (Oak Ridge National Laboratory)\n\nHigh-quality, 
 accurate, and real-time information about disease spread is critical for r
 apid response to a biothreat. Electronic health data is collected by appro
 ximately 90% of all physicians in the United States. However, its broad us
 e for public health surveillance and monitoring is inhibited by a lack of 
 organization around common and high-quality information. We use AI to auto
 matically code unstructured clinical documents. During a biothreat scenari
 o, these tools will help synthesize data across multiple medical records s
 ystems and enable the rapid identification of vulnerable populations. In a
 ddition to extracting important information from clinical records, we are 
 developing an autonomous biothreat agent that scans reputable public healt
 h surveillance reports and identifies emerging threats. Once an emerging t
 hreat is identified, our AI agents will be able to search existing scienti
 fic literature to extract the disease specific parameters for epidemiologi
 cal modeling. AI is helping us develop the necessary infrastructure for ne
 ar real-time situational readiness, so that we will be prepared during the
  next Covid-like event.\n\nDomain: Applied Social Sciences and Humanities,
  Life Sciences, Computational Methods and Applied Mathematics\n\nSession C
 hair: Adam Spannaus (Oak Ridge National Laboratory)\n\n
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