A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.

Journal: Journal of nephrology
Published Date: Apr 26, 2021

Abstract

BACKGROUND: Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions.

Authors

  • Francesca Alfieri
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Andrea Ancona
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Giovanni Tripepi
    Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, CNR-IFC, Nefrologia-Ospedali Riuniti, 89100, Reggio Calabria, Italy.
  • Dario Crosetto
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Vincenzo Randazzo
    Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy. Electronic address: vincenzo.randazzo@polito.it.
  • Annunziata Paviglianiti
    Department of Electronics and Telecomunications, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
  • Eros Pasero
    Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy. Electronic address: eros.pasero@polito.it.
  • Luigi Vecchi
    S.C. Nefrologia e Dialisi, Azienda Ospedaliera Di Terni, Viale Tristano Di Joannuccio, 05100, Terni, Italy.
  • Valentina Cauda
    Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy. Valentina.cauda@polito.it.
  • Riccardo Maria Fagugli
    S.C. Nefrologia e Dialisi, Azienda Ospedaliera Di Perugia, Piazzale Giorgio Menghini 1, 06129, Perugia, Italy.

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