A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil.

ABSTRACT: The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural

SUBMITTER: Fernandes FT 

PROVIDER: S-EPMC7870665 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature