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Identification of high-risk COVID-19 patients using machine learning

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  • Mario A Quiroz-Juárez
  • Armando Torres-Gómez
  • Irma Hoyo-Ulloa
  • Roberto de J León-Montiel
  • Alfred B U’Ren

Abstract

The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.

Suggested Citation

  • Mario A Quiroz-Juárez & Armando Torres-Gómez & Irma Hoyo-Ulloa & Roberto de J León-Montiel & Alfred B U’Ren, 2021. "Identification of high-risk COVID-19 patients using machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
    • Handle: RePEc:plo:pone00:0257234
    DOI: 10.1371/journal.pone.0257234
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    References listed on IDEAS

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    1. Avila-Ponce de León, Ugo & Pérez, Ángel G.C. & Avila-Vales, Eric, 2020. "An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Mohamed Abd Elaziz & Khalid M Hosny & Ahmad Salah & Mohamed M Darwish & Songfeng Lu & Ahmed T Sahlol, 2020. "New machine learning method for image-based diagnosis of COVID-19," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
    3. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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