Presentation
Enhancing Pandemic Preparedness: AI Models for Risk Assessment and Disease Progression
Presenter
DescriptionThis talk outlines the development of AI models for Risk Assessment and Disease Progression in Pandemic Preparedness. Leveraging advanced deep learning techniques, these models integrate diverse data sources like images, clinical 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-demand scenarios such as the COVID-19 pandemic.
The talk will further demonstrate a specific AI model from an SNSF-funded research project focused on the development of an AI-multiomics-based prognostic stratification of acute and chronic COVID-19 patients. Using chest CT scans and clinical data, two models, AssessNet-19 and ChronRisk-19, significantly enhance severity assessment and long-COVID prediction. Evaluated on diverse patient cohorts, the models outperform radiologists and single-class lesion models. The research underscores the potential of AI in improving COVID-19 patient care, offering insights for future clinical workflows.
The talk will further demonstrate a specific AI model from an SNSF-funded research project focused on the development of an AI-multiomics-based prognostic stratification of acute and chronic COVID-19 patients. Using chest CT scans and clinical data, two models, AssessNet-19 and ChronRisk-19, significantly enhance severity assessment and long-COVID prediction. Evaluated on diverse patient cohorts, the models outperform radiologists and single-class lesion models. The research underscores the potential of AI in improving COVID-19 patient care, offering insights for future clinical workflows.
TimeTuesday, June 416:00 - 16:30 CEST
LocationHG F 26.5
SessionMS4H - Synergizing AI and HPC for Pandemic Preparedness with Genomics and Clinical Risk Assessment
Session Chairs
Event Type
Minisymposium
Chemistry and Materials
Engineering
Life Sciences
Computational Methods and Applied Mathematics