Using the Super Learner algorithm to predict risk of 30-day readmission after bariatric surgery in the United States.

Journal: Surgery
Published Date: Mar 1, 2022

Abstract

BACKGROUND: Risk prediction models that estimate patient probabilities of adverse events are commonly deployed in bariatric surgery. The objective was to validate a machine learning (Super Learner) prediction model of 30-day readmission after bariatric surgery in comparison with a traditional logistic regression.

Authors

  • Matteo Torquati
    Boston College, Morrissey College of Arts & Sciences, Boston, MA.
  • Morgan Mendis
    Ayiti Analytics, Silver Spring, MD.
  • Huiwen Xu
    James P. Wilmot Cancer Center, University of Rochester Medical Center, NY, USA; Department of Surgery, Cancer Control, University of Rochester Medical Center, NY, USA.
  • Ajay A Myneni
    Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, NY.
  • Katia Noyes
    Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, NY. Electronic address: https://twitter.com/KatiaPhd.
  • Aaron B Hoffman
    Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, NY.
  • Philip Omotosho
    Department of Surgery, Rush University Medical Center, Chicago, IL.
  • Adan Z Becerra
    Department of Surgery, Rush University Medical Center, Chicago, IL. Electronic address: adan_becerra@rush.edu.

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