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Title
Predicting Hospital Ward Admission from the Emergency Department: A Systematic ReviewAuthor
xmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
https://ror.org/0023sah13https://ror.org/01a2wsa50
Version
http://purl.org/coar/version/c_970fb48d4fbd8a85
Rights
© 2023 The AuthorsAccess
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.3390/jpm13050849Published at
Journal of Personalized Medicine 2023. Vol. 13, n. 5, n. art. 849Publisher
MDPIKeywords
admission risk prediction modelemergency department
patients
admission
Abstract
Background: The emergency department (ED) is often overburdened, due to the high influx of patients and limited availability of attending physicians. This situation highlights the need for improvement ... [+]
Background: The emergency department (ED) is often overburdened, due to the high influx of patients and limited availability of attending physicians. This situation highlights the need for improvement in the management of, and assistance provided in the ED. A key point for this purpose is the identification of patients with the highest risk, which can be achieved using machine learning predictive models. The objective of this study is to conduct a systematic review of predictive models used to detect ward admissions from the ED. The main targets of this review are the best predictive algorithms, their predictive capacity, the studies’ quality, and the predictor variables. Methods: This review is based on PRISMA methodology. The information has been searched in PubMed, Scopus and Google Scholar databases. Quality assessment has been performed using the QUIPS tool. Results: Through the advanced search, a total of 367 articles were found, of which 14 were of interest that met the inclusion criteria. Logistic regression is the most used predictive model, achieving AUC values between 0.75–0.92. The two most used variables are the age and ED triage category. Conclusions: artificial intelligence models can contribute to improving the quality of care in the ED and reducing the burden on healthcare systems. [-]
xmlui.dri2xhtml.METS-1.0.item-oaire-funderName
Gobierno Vascoxmlui.dri2xhtml.METS-1.0.item-oaire-fundingStream
HAZITEK 2022xmlui.dri2xhtml.METS-1.0.item-oaire-awardNumber
ZL-2022/00571xmlui.dri2xhtml.METS-1.0.item-oaire-awardURI
Sin informaciónxmlui.dri2xhtml.METS-1.0.item-oaire-awardTitle
INURGECollections
- Articles - Engineering [639]
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