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TECHNOLOGY AND CODE article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1495074
This article is part of the Research Topic Outbreak Oracles: How AI's Journey through COVID-19 Shapes Future Epidemic Strategy View all 4 articles

Artificial Intelligence in Triage of COVID-19 Patients

Provisionally accepted
Yuri Oliveira Yuri Oliveira 1*Iêda Rios Iêda Rios 2Paula Araújo Paula Araújo 2Alinne Macambira Alinne Macambira 3Marcos Guimarães Marcos Guimarães 4Lucia Sales Lucia Sales 5Marcos Rosa Júnior Marcos Rosa Júnior 6André Nicola André Nicola 1Mauro Nakayama Mauro Nakayama 7Hermeto Paschoalick Hermeto Paschoalick 7Francisco Nascimento Francisco Nascimento 1Carlos Castillo-Salgado Carlos Castillo-Salgado 8Vania Moraes Ferreira Vania Moraes Ferreira 1Hervaldo Carvalho Hervaldo Carvalho 1
  • 1 University of Brasilia, Brasilia, Brazil
  • 2 Hospital Universitário de Brasília, Brasília, Brazil
  • 3 Federal University of Tocantins, Palmas, Tocantins, Brazil
  • 4 Universidade Federal do Vale do São Francisco, Petrolina, Petrolina, Pernambuco, Brazil
  • 5 Federal University of Pará, Belém, Pará, Brazil
  • 6 Federal University of Espirito Santo, Vitória, Espirito Santo, Brazil
  • 7 Federal University of Grande Dourados, Dourados, Mato Grosso do Sul, Brazil
  • 8 Johns Hopkins University, Baltimore, Maryland, United States

The final, formatted version of the article will be published soon.

    In 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work, we evaluated the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19, using clinical data from hospital admission alone. This data was collected during a prospective, multicenter cohort that followed patients with respiratory syndrome during the pandemic. We aimed to predict which patients would present mild cases of COVID-19 and which would develop severe cases. Severe cases were defined as those requiring access to the Intensive Care Unit, endotracheal intubation, or even progressing to death. The system achieved an accuracy of 80%, with Area Under Receiver Operating Characteristic Curve (AUC) of 91%, Positive Predictive Value of 87% and Negative Predictive Value of 82%. Considering that only data from hospital admission was used, and that this data came from low-cost clinical examination and laboratory testing, the low false positive rate and acceptable accuracy observed shows that it is feasible to implement prediction systems based on artificial intelligence as an effective triage method.

    Keywords: artificial intelligence, machine learning, Clinical data, COVID-19, outcome prediction, prediction algorithms, Triage

    Received: 12 Sep 2024; Accepted: 27 Nov 2024.

    Copyright: © 2024 Oliveira, Rios, Araújo, Macambira, Guimarães, Sales, Rosa Júnior, Nicola, Nakayama, Paschoalick, Nascimento, Castillo-Salgado, Ferreira and Carvalho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yuri Oliveira, University of Brasilia, Brasilia, Brazil

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.