Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study.

ABSTRACT:

Introduction

Bacteremia is a common but life-threatening infectious disease. However, a well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is lacking. The aim of this study is to establish a predictive model for bacteremia in septic patients using available big data in the emergency department (ED) through logistic regression and other machine learning (ML) methods.

Material and methods

We conducted a retrospective cohort study at the ED of National Cheng Kung University Hospital in Taiwan from January 2015 to December 2019. ED adults (≥18 years old) with systemic inflammatory response syndrome and receiving blood cultures during the ED stay were included. Models I and II were established based on logistic regression, both of which were d

SUBMITTER: Goh V 

PROVIDER: S-EPMC9600599 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature