Personalized machine learning approach to predict candidemia in medical wards.

Journal: Infection
PMID: 32740866

Abstract

PURPOSE: Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs).

Authors

  • Andrea Ripoli
    Imaging Department, Fondazione Gabriele Monasterio, Massa, Italy.
  • Emanuela Sozio
    Emergency Department, North-West District, Tuscany Health Care, Spedali Riuniti Livorno, Livorno, Italy.
  • Francesco Sbrana
    U.O. Lipoapheresis and Center for Inherited Dyslipidemias, Fondazione Toscana Gabriele Monasterio, Via Moruzzi,1, 56124, Pisa, Italy. francesco.sbrana@ftgm.it.
  • Giacomo Bertolino
    Pharmaceutical Department, ASSL Cagliari, Cagliari, Italy.
  • Carlo Pallotto
    UOC Malattie Infettive, Ospedale San Donato Arezzo, Sud-Est District, Tuscany Health Care, Arezzo, Italy.
  • Gianluigi Cardinali
    Department of Pharmaceutical Sciences-Microbiology, University of Perugia, Perugia, Italy.
  • Simone Meini
    Internal Medicine Unit, Santa Maria Annunziata Hospital, Florence, Italy.
  • Filippo Pieralli
    Intermediate Care Unit, Azienda Ospedaliera Universitaria Careggi, Florence, Italy.
  • Anna Maria Azzini
    Dipartimento Di Diagnostica E Sanità Pubblica, Sezione Di Malattie Infettive, Università Di Verona, Verona, Italy.
  • Ercole Concia
    Dipartimento Di Diagnostica E Sanità Pubblica, Sezione Di Malattie Infettive, Università Di Verona, Verona, Italy.
  • Bruno Viaggi
    Department of Anesthesia, Neuro Intensive Care Unit, Careggi Universital Hospital, Florence, Italy.
  • Carlo Tascini
    First Division of Infectious Diseases, Cotugno Hospital, Azienda Ospedaliera Dei Colli, Napoli, Italy.

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