Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Journal: Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
Published Date: Oct 1, 2020

Abstract

BACKGROUND: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems.

Authors

  • N Peiffer-Smadja
    National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France. Electronic address: n.peiffer-smadja@ic.ac.uk.
  • S Dellière
    Université de Paris, Laboratoire de Parasitologie-Mycologie, Groupe Hospitalier Saint-Louis-Lariboisière-Fernand-Widal, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.
  • C Rodriguez
    Department of Prevention, Diagnosis and Treatment of Infections, Henri-Mondor Hospital, APHP, Université Paris-Est Créteil, IMRB, INSERM U955, Créteil, France.
  • G Birgand
    National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK.
  • F-X Lescure
    French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France; Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France.
  • S Fourati
    Department of Prevention, Diagnosis and Treatment of Infections, Henri-Mondor Hospital, APHP, Université Paris-Est Créteil, IMRB, INSERM U955, Créteil, France.
  • E Ruppé
    Université de Paris, IAME, INSERM, F-75018 Paris, France. Electronic address: etienne.ruppe@inserm.fr.

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