Predicting mortality among septic patients presenting to the emergency department-a cross sectional analysis using machine learning.

ABSTRACT:

Background

Sepsis is a life-threatening condition, causing almost one fifth of all deaths worldwide. The aim of the current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the presentation of septic patients arriving to the emergency department (ED) using machine learning.

Methods

Retrospective cross-sectional design, including all patients arriving to the ED at Södersjukhuset in Sweden during 2013 and discharged with an International Classification of Diseases (ICD)-10 code corresponding to sepsis. All predictions were made using a Balanced Random Forest Classifier and 91 variables reflecting ED presentation. An exhaustive search was used to remove unnecessary variables in the final model. A 10-fold cross validation was pe

SUBMITTER: Karlsson A 

PROVIDER: S-EPMC8276466 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature