An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.

Journal: PloS one
Published Date: Jul 15, 2024

Abstract

INTRODUCTION: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement.

Authors

  • Mathias Aagaard Christensen
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
  • Arnór Sigurdsson
    Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Alexander Bonde
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
  • Simon Rasmussen
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Sisse R Ostrowski
    Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen Medical School, Copenhagen, Denmark.
  • Mads Nielsen
    Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark.
  • Martin Sillesen
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.

Keywords

External Resources

Popular Topics
Recent Journals