Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

Journal: Applied clinical informatics
Published Date:

Abstract

OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital.

Authors

  • Keith E Morse
    Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
  • Conner Brown
    Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States.
  • Scott Fleming
    Department of Biomedical Data Science, Stanford University, Palo Alto, California, United States.
  • Irene Todd
    Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States.
  • Austin Powell
    Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States.
  • Alton Russell
    Harvard Medical School, Boston, Massachusetts, United States.
  • David Scheinker
    Department of Management Science and Engineering (D.S.), Stanford University, CA.
  • Scott M Sutherland
    Division of Nephrology, Department of Pediatrics, Stanford University, Stanford, California, United States.
  • Jonathan Lu
    Department of Biomedical Data Science, Stanford University, Palo Alto, California, United States.
  • Brendan Watkins
    Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States.
  • Nigam H Shah
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
  • Natalie M Pageler
    Division of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
  • Jonathan P Palma
    Division of Neonatology, Department of Pediatrics, Orlando Health, Orlando, Florida, United States.