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

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.

Methods

A single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a de

SUBMITTER: van Doorn WPTM 

PROVIDER: S-EPMC7815112 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature