Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis.

Journal: Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
PMID: 38556418

Abstract

BACKGROUND: Machine learning (ML) approaches have become increasingly popular in predicting surgical outcomes. However, it is unknown whether they are superior to traditional statistical methods such as logistic regression (LR). This study aimed to perform a systematic review and meta-analysis to compare the performance of ML vs LR models in predicting postoperative outcomes for patients undergoing gastrointestinal (GI) surgery.

Authors

  • Jane Wang
    Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA.
  • Francesca Tozzi
    Department of General, HPB Surgery and Liver Transplantation, Ghent University Hospital, Ghent, Belgium.
  • Amir Ashraf Ganjouei
    Department of Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Fernanda Romero-Hernandez
    Department of Surgery, University of California San Francisco, San Francisco, California, USA.
  • Jean Feng
    Department of Biostatistics, University of Washington, Seattle, WA.
  • Lucia Calthorpe
    Department of Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Maria Castro
    Department of Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Greta Davis
    Department of Surgery, Division of Plastic and Reconstructive Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Jacquelyn Withers
    Department of Surgery, Division of Plastic and Reconstructive Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Connie Zhou
    Department of Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Zaim Chaudhary
    University of California, Berkeley, Berkeley, California, United States.
  • Mohamed Adam
    Department of Surgery, University of California, San Francisco, San Francisco, California, United States.
  • Frederik Berrevoet
    Department of General and HPB Surgery and Liver Transplantation, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium. Electronic address: Frederik.Berrevoet@ugent.be.
  • Adnan Alseidi
    Department of Surgery, University of California, San Francisco, CA, USA.
  • Nikdokht Rashidian
    Department of General, HPB Surgery and Liver Transplantation, Ghent University Hospital, Ghent, Belgium. Electronic address: nikdokht.rashidian@ugent.be.

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