Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

Journal: Clinical chemistry and laboratory medicine
PMID: 33079698

Abstract

OBJECTIVES: The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative.

Authors

  • Federico Cabitza
    Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy.
  • Andrea Campagner
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy. Electronic address: a.campagner@campus.unimib.it.
  • Davide Ferrari
    Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.
  • Chiara Di Resta
    Vita-Salute San Raffaele University; Unit of Genomics for Human Disease Diagnosis, Division of Genetics and Cell Biology, Milan, Italy.
  • Daniele Ceriotti
    Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Eleonora Sabetta
    Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Alessandra Colombini
    IRCCS Istituto Ortopedico Galeazzi, Laboratory of Clinical Chemistry and Microbiology, Milan, Italy.
  • Elena De Vecchi
    IRCCS Istituto Ortopedico Galeazzi, Laboratory of Clinical Chemistry and Microbiology, Milan, Italy.
  • Giuseppe Banfi
    IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
  • Massimo Locatelli
    Laboratory Medicine Service, San Raffaele Hospital, Via Olgettina, 60, 20132, Milano, Italy.
  • Anna Carobene
    Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy.

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