Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality.

Journal: JAMA network open
Published Date: Nov 2, 2020

Abstract

IMPORTANCE: Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use.

Authors

  • Fasiha Kanwal
    Baylor College of Medicine, Houston, TX, USA.
  • Thomas J Taylor
    INFOTECH Soft, Inc., 1201 Brickell Ave. Suite 220, Miami, FL 33131, USA.
  • Jennifer R Kramer
    Clinical Epidemiology and Comparative Effectiveness Program, Center for Innovations in Quality, Effectiveness and Safety, Michael E. Debakey VA Medical Center, John P. McGovern Campus, 2450 Holcombe Blvd., Suite 01Y, Houston, TX, 77021, USA.
  • Yumei Cao
    Veterans Affairs (VA) Health Services Research and Development Service Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas.
  • Donna Smith
    Veterans Affairs (VA) Health Services Research and Development Service Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas.
  • Allen L Gifford
    Department of Medicine, VA Boston Healthcare System, Boston University, Boston, Massachusetts.
  • Hashem B El-Serag
    Baylor College of Medicine, Houston, TX, USA.
  • Aanand D Naik
    Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas.
  • Steven M Asch
    Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA; Division of General Medical Disciplines, Stanford University, Stanford, CA.

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