Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery.

Journal: Surgical endoscopy
PMID: 37311893

Abstract

BACKGROUND: Postoperative gastrointestinal bleeding (GIB) is a rare but serious complication of bariatric surgery. The recent rise in extended venous thromboembolism regimens as well as outpatient bariatric surgery may increase the risk of postoperative GIB or lead to delay in diagnosis. This study seeks to use machine learning (ML) to create a model that predicts postoperative GIB to aid surgeon decision-making and improve patient counseling for postoperative bleeds.

Authors

  • Justin L Hsu
    Department of Surgery, University of North Carolina School of Medicine, 4001 Burnett-Womack CB#7050, Chapel Hill, NC, 27599, USA. Justin_Hsu@med.unc.edu.
  • Kevin A Chen
    Department of Surgery, University of North Carolina School of Medicine, 4001 Burnett-Womack CB#7050, Chapel Hill, NC, 27599, USA.
  • Logan R Butler
    University of North Carolina School of Medicine, Chapel Hill, NC, USA.
  • Anoosh Bahraini
    Department of Surgery, University of North Carolina School of Medicine, 4001 Burnett-Womack CB#7050, Chapel Hill, NC, 27599, USA.
  • Muneera R Kapadia
    Department of Surgery, University of North Carolina School of Medicine, 4001 Burnett-Womack CB#7050, Chapel Hill, NC, 27599, USA.
  • Shawn M Gomez
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Timothy M Farrell
    Department of Surgery, University of North Carolina School of Medicine, 4001 Burnett-Womack Building, CB #7050, Chapel Hill, NC, 27599, USA.

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