Identification of Fast Progressors Among Patients With Nonalcoholic Steatohepatitis Using Machine Learning.

Journal: Gastro hep advances
Published Date: Sep 15, 2023

Abstract

BACKGROUND AND AIMS: There is a high unmet need to develop noninvasive tools to identify nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) patients at risk of fast progression to end-stage liver disease (ESLD). This study describes the development of a machine learning (ML) model using data around the first clinical evidence of NAFLD/NASH to identify patients at risk of future fast progression.

Authors

  • Jörn M Schattenberg
    Metabolic Liver Research Program, I. Department of Medicine, University Medical Center, Mainz, Germany.
  • Maria-Magdalena Balp
    Novartis Pharma AG, Basel, Switzerland.
  • Brenda Reinhart
    ZS Associates, Zurich, Switzerland.
  • Sanchita Porwal
    ZS Associates, London, UK.
  • Andreas Tietz
    Novartis Pharma AG, Basel, Switzerland.
  • Marcos C Pedrosa
    Novartis Pharma AG, Basel, Switzerland.
  • Matt Docherty
    ZS Associates, Philadelphia, Pennsylvania.

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