An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 32449766

Abstract

OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits.

Authors

  • Jihad S Obeid
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29425, United States.
  • Matthew Davis
    IBM Research - Australia, Carlton, VIC, Australia.
  • Matthew Turner
    Information Solutions, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Stéphane M Meystre
    Department of Biomedical Informatics, University of Utah, Salt Lake City, USA.
  • Paul M Heider
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
  • Edward C O'Bryan
    Department of Emergency Medicine, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Leslie A Lenert
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29425, United States.

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