Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review.

Journal: Journal of critical care
Published Date: Jul 25, 2024

Abstract

INTRODUCTION: Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis.

Authors

  • Nasrin Nikravangolsefid
    Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Swetha Reddy
    Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Hong Hieu Truong
    Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Mariam Charkviani
    Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Jacob Ninan
    Department of Nephrology and Critical Care Medicine, MultiCare Capital Medical Center, Olympia, WA, USA. jacob.ninan@multicare.org.
  • Larry J Prokop
    Mayo Clinic Libraries, Mayo Clinic, Rochester, MN, USA.
  • Supawadee Suppadungsuk
    Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
  • Waryaam Singh
    Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Kianoush B Kashani
    Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Juan Pablo Domecq Garces
    Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Critical Care Medicine, Mayo Clinic Health System, Mankato, MN, USA. Electronic address: domecq.juan@mayo.edu.

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