Machine Learning Models predicting Decompensation in Cirrhosis.

Journal: Journal of gastrointestinal and liver diseases : JGLD
PMID: 40153826

Abstract

BACKGROUND AND AIMS: Decompensation of cirrhosis significantly decreases survival, thus, prevention of complications is paramount. We used machine learning techniques to identify parameters predicting decompensation.

Authors

  • Sophie Elisabeth Müller
    Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany; Institute of Medical Microbiology and Hygiene, Center for Infectious Diseases, Saarland University, Homburg, Germany. sophieelisabeth.mueller@uks.eu.
  • Markus Casper
    Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany markus.casper@uks.eu.
  • Cristina Ripoll
    Department of Internal Medicine IV, University Hospital Jena, Jena, Germany; Clinic for Internal Medicine I, University Hospital Halle, Halle, Germany. Cristina.Ripoll@med.uni-jena.de.
  • Alexander Zipprich
    Department of Internal Medicine IV, University Hospital Jena, Jena, Germany; Clinic for Internal Medicine I, University Hospital Halle, Halle, Germany. zipprich@gmx.de.
  • Paul Horn
    Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
  • Marcin Krawczyk
    Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany.
  • Frank Lammert
    Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany.
  • Matthias Christian Reichert
    Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany. mattreichert@gmx.de.

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