Novel pattern identification methodologies have been developed based upon mathematical methods used in theoretical physics. The resulting powerful algorithms can be utilized to find clusters in both numerical data tables of the attributes of things and of networks of connectivity among things. This research develops a transformative pattern identification algorithm to analyze COVID-19 patient data for cluster analysis in tabular numerical data tables, e.g., patient medical data files and in disease networks. A cloud-based clustering system is being designed and deployed to host multi-users’ large medical data submissions along with extensive user documentation and user tools for submission and resulting analysis. The initial prototype will be launched and tested in the fall of 2020 with extensive PRISMA COVID-19 personal medical encounter data seeking comorbidity and other regularities and patterns in the data.
To learn more about the webinar and our two guest speakers, visit the archived event page.