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LOCATION:HG F 26.5
DTSTART;TZID=Europe/Stockholm:20240604T160000
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UID:submissions.pasc-conference.org_PASC24_sess122_msa224@linklings.com
SUMMARY:Enhancing Pandemic Preparedness: AI Models for Risk Assessment and
  Disease Progression
DESCRIPTION:Minisymposium\n\nAlexander Poellinger (University of Bern)\n\n
 This talk outlines the development of AI models for Risk Assessment and Di
 sease Progression in Pandemic Preparedness. Leveraging advanced deep learn
 ing techniques, these models integrate diverse data sources like images, c
 linical features, and laboratory data to characterize disease subtypes and
  personalize risk assessment. Such AI-driven Clinical Risk Assessment aids
  healthcare professionals in resource allocation, especially in high-deman
 d scenarios such as the COVID-19 pandemic.\nThe talk will further demonstr
 ate a specific AI model from an SNSF-funded research project focused on th
 e development of an  AI-multiomics-based prognostic stratification of acut
 e and chronic COVID-19 patients. Using chest CT scans and clinical data, t
 wo models, AssessNet-19 and ChronRisk-19, significantly enhance severity a
 ssessment and long-COVID prediction. Evaluated on diverse patient cohorts,
  the models outperform radiologists and single-class lesion models. The re
 search underscores the potential of AI in improving COVID-19 patient care,
  offering insights for future clinical workflows.\n\nDomain: Chemistry and
  Materials, Engineering, Life Sciences, Computational Methods and Applied 
 Mathematics\n\nSession Chairs: John Anderson Garcia Henao (University of B
 ern, ARTORG Center for Biomedical Engineering Research) and Kary Ann del C
 armen Ocaña Gautherot (LNCC)
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