This is your Project description. Click on "Edit Text" or double click on the text box to start. This is your Project description. Click on "Edit Text" or double click on the text box to start. This is your Project description. Click on "Edit Text" or double click on the text box to start.

#Information #Healthcare #COVID-19 

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Automated image analysis, guided by AI or deep learning algorithms, must first obtain a manually classified data set to train and validate the machine learning models. These data sets are often painfully laborious to create and existing tools fall short of supporting scientific images that can be complex, noisy, and difficult to interpret.

Cancer phylogenetics [1] is an approach for understanding the evolutionary history of healthy cells that accumulate genetic mutations, develop into tumors, and spread to metastatic sites around the body. By analyzing these genetic changes, researchers can begin to make predictions on clinical outcomes such as tumor progression, resistance, and treatment response [2]. 

Working closely with NCSA’s Healthcare Innovation Program Office, the Visual Analytics group is part of an ongoing collaboration with researchers at the NIH National Institute of Allergy and Infectious Disease, Institute for Systems Biology and Illinois Department of Statistics.This interdisciplinary team is taking an “all-inclusive” approach in studying the disease pathogenesis of influenzae and, more recently, COVID-19. 

Our design strategies helped synthesize ideas across multiple disciplines
into a webpage tool for public officials. We presented data on AI-detected misinformation in a concise quick-glance communication style. We also worked alongside our collaborators to translate their newly developed response strategies into actionable steps that could be completed on the website. 

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