A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

Journal: PloS one
Published Date: Jan 19, 2021

Abstract

INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.

Authors

  • William P T M van Doorn
    CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Patricia M Stassen
    Division of General Internal Medicine, Section Acute Medicine, Department of Internal Medicine, Maastricht University Medical Centre, Maastricht University, Maastricht, The Netherlands.
  • Hella F Borggreve
    Division of General Internal Medicine, Section Acute Medicine, Department of Internal Medicine, Maastricht University Medical Centre, Maastricht University, Maastricht, The Netherlands.
  • Maaike J Schalkwijk
    Division of General Internal Medicine, Section Acute Medicine, Department of Internal Medicine, Maastricht University Medical Centre, Maastricht University, Maastricht, The Netherlands.
  • Judith Stoffers
    Division of General Internal Medicine, Section Acute Medicine, Department of Internal Medicine, Maastricht University Medical Centre, Maastricht University, Maastricht, The Netherlands.
  • Otto Bekers
    CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Steven J R Meex
    CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.

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