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Improved real-time surveillance of COVID-19 patients' electronic health records using transfer learning and ordinal regression

Grant number: unknown

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Key facts

  • Disease

    COVID-19

  • start year

    -99

  • Known Financial Commitments (USD)

    $0

  • Funder

    University of Michigan

  • Principal Investigator

    N/A

  • Research Location

    United States of America

  • Lead Research Institution

    N/A

  • Research Priority Alignment

    N/A

  • Research Category

    Epidemiological studies

  • Research Subcategory

    Impact/ effectiveness of control measures

  • Special Interest Tags

    Digital Health

  • Study Type

    Unspecified

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Led by Drs. Andrew Admon (Internal Medicine) and Christopher Gillies (Emergency Medicine), this team is using Machine Learning, a powerful data science tool, to build a real-time patient surveillance system. During times of unprecedented strain on healthcare personnel and clinical resources, this will help clinicians identify COVID-19 patients who need more intensive monitoring, closer nursing care, or urgent physician intervention.