Machine learning models compared to existing criteria for noninvasive prediction of endoscopic retrograde cholangiopancreatography-confirmed choledocholithiasis.

Journal: Liver research (Beijing, China)
Published Date: Oct 22, 2021

Abstract

BACKGROUND AND AIMS: Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics. We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography (ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models (MLMs).

Authors

  • Camellia Dalai
    UCLA-Olive View Internal Medicine Residency Program, Department of Medicine, Olive View-UCLA Medical Center, Sylmar, CA, USA.
  • John Azizian
    UCLA-Olive View Internal Medicine Residency Program, Department of Medicine, Olive View-UCLA Medical Center, Sylmar, CA, USA.
  • Harry Trieu
    Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Anand Rajan
    UCLA-Olive View Internal Medicine Residency Program, Department of Medicine, Olive View-UCLA Medical Center, Sylmar, CA, USA.
  • Formosa Chen
    Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
  • Tien Dong
    Tamar and Vatche Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
  • Simon Beaven
    Tamar and Vatche Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
  • James H Tabibian
    David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

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