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

Journal: Surgery today
Published Date:

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.

Authors

  • Wessel T Stam
    Department of Gastrointestinal Surgery, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands.
  • Erik W Ingwersen
    Department of Gastrointestinal Surgery, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands.
  • Mahsoem Ali
    Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
  • Jorik T Spijkerman
    Independent Consultant in Computational Intelligence, Amsterdam, The Netherlands.
  • Geert Kazemier
    Department of Surgery, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands.
  • Emma R J Bruns
    Department of Gastrointestinal Surgery, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands.
  • Freek Daams
    Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, VU University, Amsterdam, The Netherlands.