Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database.

Journal: Surgical endoscopy
Published Date: Jan 1, 2021

Abstract

BACKGROUND: Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance of currently available predictive models for these complications remains wanting, while machine learning has shown promise to improve on traditional modeling approaches. The purpose of this study was to compare the ability of two machine learning strategies, artificial neural networks (ANNs), and gradient boosting machines (XGBs) to conventional models using logistic regression (LR) in predicting leak and VTE after bariatric surgery.

Authors

  • Jacob Nudel
    Department of Surgery, Boston University School of Medicine, Boston, MA, USA.
  • Andrew M Bishara
    Department of Anesthesia, University of California, San Francisco, San Francisco, CA, USA.
  • Susanna W L de Geus
    Department of Surgery, Boston University School of Medicine, Boston, MA, USA.
  • Prasad Patil
    Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215.
  • Jayakanth Srinivasan
    Institute for Health System Innovation and Policy, Boston University, 601, 656 Beacon Street, Boston, MA, 02215, USA.
  • Donald T Hess
    Department of Surgery, Boston University School of Medicine, Boston, MA, USA.
  • Jonathan Woodson
    Institute for Health System Innovation and Policy, Boston University, 601, 656 Beacon Street, Boston, MA, 02215, USA. jwoodson@bu.edu.

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