Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—Challenges, strengths, and opportunities in a global health emergency

Ferrari, Davide; Milic, Jovana; Tonelli, Roberto; Ghinelli, Francesco; Meschiari, Marianna; Volpi, Sara; Faltoni, Matteo; Franceschi, Giacomo; Iadisernia, Vittorio; Yaacoub, Dina; Ciusa, Giacomo; Bacca, Erica; Rogati, Carlotta; Tutone, Marco; Burastero, Giulia; Raimondi, Alessandro; Menozzi, Marianna; Franceschini, Erica; Cuomo, Gianluca; Corradi, Luca; Orlando, Gabriella; Santoro, Antonella; Digaetano, Margherita; Puzzolante, Cinzia; Carli, Federica; Borghi, Vanni; Bedini, Andrea; Fantini, Riccardo; Tabbì, Luca; Castaniere, Ivana; Busani, Stefano; Clini, Enrico; Girardis, Massimo; Sarti, Mario; Cossarizza, Andrea; Mussini, Cristina; Mandreoli, Federica; Missier, Paolo; Guaraldi, Giovanni

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—Challenges, strengths, and opportunities in a global health emergency

Davide Ferrari, Jovana Milic, Roberto Tonelli, Francesco Ghinelli, Marianna Meschiari, Sara Volpi, Matteo Faltoni, Giacomo Franceschi, Vittorio Iadisernia, Dina Yaacoub, Giacomo Ciusa, Erica Bacca, Carlotta Rogati, Marco Tutone, Giulia Burastero, Alessandro Raimondi, Marianna Menozzi, Erica Franceschini, Gianluca Cuomo, Luca Corradi, Gabriella Orlando, Antonella Santoro, Margherita Digaetano, Cinzia Puzzolante, Federica Carli, Vanni Borghi, Andrea Bedini, Riccardo Fantini, Luca Tabbì, Ivana Castaniere, Stefano Busani, Enrico Clini, Massimo Girardis, Mario Sarti, Andrea Cossarizza, Cristina Mussini, Federica Mandreoli, Paolo Missier and Giovanni Guaraldi

PLOS ONE, 2020, vol. 15, issue 11, 1-14

Abstract: Aims: The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods: This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients’ medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0239172

DOI: 10.1371/journal.pone.0239172

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