Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor.

Journal: Critical care (London, England)
Published Date: Aug 16, 2019

Abstract

BACKGROUND: Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The AKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information, and accessible online. In order to evaluate its clinical value, the AKIpredictor was compared to physicians' predictions.

Authors

  • Marine Flechet
    Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.
  • Stefano Falini
    Department of Anesthesia and General Intensive Care, Humanitas Clinical and Research Center, via Manzoni 56, Rozzano, 20089, Milan, Italy.
  • Claudia Bonetti
    University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy.
  • Fabian Güiza
    Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.
  • Miet Schetz
    Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.
  • Greet Van den Berghe
    Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium.
  • Geert Meyfroidt
    Department of Intensive Care Medicine, University Hospitals Leuven, Herestraat 49, 3000, Louvain, Belgium.

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