Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.

Journal: PeerJ
PMID: 38881861

Abstract

BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the disease may vary from individual to individual, that is, it is essential to identify the clinical parameters that indicate a good prognosis for the patient. Machine learning (ML) algorithms have been used for analyzing complex medical data and identifying prognostic indicators. However, there is still an urgent need for a model to elucidate the predictors related to patient outcomes. Therefore, this research aimed to verify, through ML, the variables involved in the discharge of patients admitted to the ICU due to COVID-19.

Authors

  • Lander Dos Santos
    State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil.
  • Lincoln Luis Silva
    Graduation Program in Health Sciences, State University of Maringa, Parana, Brazil.
  • Fernando Castilho Pelloso
    Department of Medicine, Federal University of Paraná, Curitiba, Paraná, Brazil.
  • Vinicius Maia
    Unicesumar, Maringá, Paraná, Brazil.
  • Constanza Pujals
    State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil.
  • Deise Helena Borghesan
    Union of Catholic Colleges of Mato Grosso, Cuiabá, Mato Grosso, Brazil.
  • Maria Dalva Carvalho
    State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil.
  • Raíssa Bocchi Pedroso
    State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil.
  • Sandra Marisa Pelloso
    State University of Maringá, Graduate Program in Health Sciences, Maringá, Paraná, Brazil.

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