Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery

Wessel T. Stam, Erik W. Ingwersen, Mahsoem Ali, Jorik T. Spijkerman, Geert Kazemier, Emma R. J. Bruns, Freek Daams

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.
Original languageEnglish
Pages (from-to)1209-1215
Number of pages7
JournalSurgery Today
Volume53
Issue number10
Early online date2023
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Machine learning
  • Postoperative complications
  • Prediction

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