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  • [\'Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.\', \'Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia, Modena, Italy.\', \'Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy.\', \'Respiratory Diseases Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy.\', \'Department of Infectious Diseases, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy.\', \'Department of Anesthesia and Intensive Care Unit, Azienda Ospedaliero-Universitaria Policlinico of Modena, Modena, Italy.\', \'Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy.\', \'Clinical Microbiology, Ospedale Civile di Baggiovara, Modena, Italy.\', \'School of Computing, Newcastle University, Newcastle upon Tyne, United kingdom.\']
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  • 10.1371/journal.pone.0239172
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  • PloS one
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  • Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.
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  • 2020

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