Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.

Journal: Gastroenterology
Published Date: Jan 1, 2020

Abstract

BACKGROUND & AIMS: Scoring systems are suboptimal for determining risk in patients with upper gastrointestinal bleeding (UGIB); these might be improved by a machine learning model. We used machine learning to develop a model to calculate the risk of hospital-based intervention or death in patients with UGIB and compared its performance with other scoring systems.

Authors

  • Dennis L Shung
    Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, USA. dennis.shung@yale.edu.
  • Benjamin Au
    Yale School of Medicine Section of Digestive Diseases, P.O. Box 208019, New Haven, CT, 06520-8019, USA.
  • Richard Andrew Taylor
    Yale Center for Medical Informatics, Yale University.
  • J Kenneth Tay
    Stanford University, Palo Alto, California.
  • Stig B Laursen
    Odense University Hospital, Odense, Denmark.
  • Adrian J Stanley
    Glasgow Royal Infirmary, Glasgow, United Kingdom.
  • Harry R Dalton
    Royal Cornwall Hospital, Cornwall, United Kingdom.
  • Jeffrey Ngu
    Christchurch Hospital, Christchurch, New Zealand.
  • Michael Schultz
    Geoinformatics of University of Heidelberg, Heidelberg, Germany. schultz.consulting@outlook.com.
  • Loren Laine
    Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, USA.

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