A comparative study of explainable ensemble learning and logistic regression for predicting in-hospi...
A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department
About this item
Full title
Author / Creator
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this...
Alternative Titles
Full title
A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_96bf0bcd29e0447ab4ccdd067b9f0fc6
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_96bf0bcd29e0447ab4ccdd067b9f0fc6
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-54038-4
