Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective.

Journal: PloS one
Published Date: Mar 26, 2019

Abstract

BACKGROUND & AIMS: Current non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinical routine. In the current study, we developed a novel machine learning approach to overcome the caveats of existing approaches.

Authors

  • Ali Canbay
    Department of Gastroenterology and Hepatology, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
  • Julia Kälsch
    Department of Gastroenterology and Hepatology, University Hospital, University Duisburg-Essen, Essen, Germany.
  • Ursula Neumann
    Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
  • Monika Rau
    Division of Hepatology, Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.
  • Simon Hohenester
    Department of Medicine II, University Hospital, LMU Munich, Munich, Germany.
  • Hideo A Baba
    Institute for Pathology, University Hospital, University Duisburg-Essen, Essen, Germany.
  • Christian Rust
    Center for Nutritional Medicine and Prevention, Department of Medicine I, Hospital Barmherzige Brüder, Munich, Germany.
  • Andreas Geier
    Department of Internal Medicine II, Division of Hepatology, University Hospital Würzburg, Würzburg, Germany.
  • Dominik Heider
    Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
  • Jan-Peter Sowa
    Department of Gastroenterology, Hepatology, and Infectious Diseases, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.

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