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  • [\'Núcleo de Astrofísica e Cosmologia, Universidade Federal do Espírito Santo, Vitória, ES, Brazil.\', \'Departamento de Física, Universidade Federal do Espírito Santo, Vitória, ES, Brazil.\', \'Department of Electrical and Computer Engineering, University of California Los Angeles, Los Angeles, CA, USA.\', \'Department of Engineering Science, University of Oxford, Oxford, UK.\', \'The Alan Turing Institute, London, UK.\', \'Department of Medicine, University of Cambridge, Cambridge, UK.\', \'Cambridge Centre for Artificial Intelligence in Medicine, Cambridge, UK.\', \'Department of Electrical and Computer Engineering, University of California Los Angeles, Los Angeles, CA, USA. mihaela@ee.ucla.edu.\', \'The Alan Turing Institute, London, UK. mihaela@ee.ucla.edu.\', \'Cambridge Centre for Artificial Intelligence in Medicine, Cambridge, UK. mihaela@ee.ucla.edu.\', \'Department of Applied Mathematics and Theoretical Physics and Department of Population Health, University of Cambridge, Cambridge, UK. mihaela@ee.ucla.edu.\']
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  • -1
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  • 10.1038/s41598-021-95004-8
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  • Scientific reports
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?:pmid
  • 34341397
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  • 1.533
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  • 122
?:title
  • Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.
?:type
?:year
  • 2021

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