Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD.

Journal: Hepatology (Baltimore, Md.)
Published Date: Apr 30, 2024

Abstract

BACKGROUND AND AIMS: Ensemble machine-learning methods, like the superlearner, combine multiple models into a single one to enhance predictive accuracy. Here we explore the potential of the superlearner as a benchmarking tool for clinical risk prediction, illustrating the approach to identifying significant liver fibrosis among patients with NAFLD.

Authors

  • Vivek Charu
    Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
  • Jane W Liang
    Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
  • Ajitha Mannalithara
    Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, California, USA.
  • Allison Kwong
    Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, California, USA.
  • Lu Tian
    Department of Health, Research & Policy, Stanford University, Stanford, CA, USA.
  • W Ray Kim
    Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, California, USA.

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