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Pronostic précoce des infections au COVID-19 via l'apprentissage automatique [Translate: Early prognosis of COVID-19 infection through machine learning]

Grant number: unknown

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Key facts

  • Disease

    COVID-19

  • start year

    2020

  • Known Financial Commitments (USD)

    $0

  • Funder

    AXA

  • Principal Investigator

    Dr. Santiago Mazuelas

  • Research Location

    Spain

  • Lead Research Institution

    Centre basque de mathématiques appliqués (BCAM)

  • Research Priority Alignment

    N/A

  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Prognostic factors for disease severity

  • Special Interest Tags

    N/A

  • Study Type

    Unspecified

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

The 2020 COVID-19 outbreak revealed infections that have particularly varied outcomes: some patients remain asymptomatic during infection, others have moderate symptoms for a few weeks, while still others suffer from acute complications even critical. This range of results poses a major challenge for COVID-19-related containment, as the most effective protective measures when infections are detected vary significantly for each type of patient. To meet this challenge, Dr Santiago Mazuelas, AXA Research Fund Prize winner at the Basque Center for Applied Mathematics (BCAM) in Spain, will develop machine learning techniques for the early prognosis of COVID-19 infections that predict future severity infections using health data obtained at the time of infection detection.