Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort.

Journal: International journal of medical informatics
Published Date: Nov 25, 2022

Abstract

BACKGROUND: The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population.

Authors

  • Samir Hassoun
    Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, Palermo, Italy. Electronic address: samir.dipatre.hassoun@gmail.com.
  • Chiara Bruckmann
    Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, Palermo, Italy. Electronic address: bruckmannchiara@gmail.com.
  • Stefano Ciardullo
    Department of Medicine and Rehabilitation, Policlinico di Monza, Monza, Italy; Department of Medicine and Surgery, School of Medicine, University of Milano-Bicocca, Monza, Italy.
  • Gianluca Perseghin
    Department of Medicine and Rehabilitation, Policlinico di Monza, Monza, Italy; Department of Medicine and Surgery, School of Medicine, University of Milano-Bicocca, Monza, Italy.
  • Francesca Di Gaudio
    Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, Palermo, Italy.
  • Francesco Broccolo
    Department of Medicine and Surgery, School of Medicine, University of Milano-Bicocca, Monza, Italy; Cerba HealthCare Italia, Milan, Italy.

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