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Data-Driven Automated Classification Algorithms for Acute Health Conditions: Applying PheNorm to COVID-19 Disease

    Basic Details

    Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. In this study, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions.


    Joshua C Smith, Brian D Williamson, David J Cronkite, Daniel Park, Jill M Whitaker, Michael F McLemore, Joshua T Osmanski, Robert Winter, Arvind Ramaprasan, Ann Kelley, Mary Shea, Saranrat Wittayanukorn, Danijela Stojanovic, Yueqin Zhao, Sengwee Toh, Kevin B Johnson, David M Aronoff, David S Carrell

    Corresponding Author

    Joshua C. Smith; Vanderbilt University Medical Center, Department of Biomedical Informatics