Machine learning models for early prediction of mortality risk in patients with burns: A single center experience

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dc.authorscopusid57210895405
dc.authorscopusid57217031781
dc.authorscopusid6603315114
dc.authorscopusid23468794300
dc.authorscopusid24076021900
dc.contributor.authorÇinar M.A.
dc.contributor.authorÖlmez Emre
dc.contributor.authorErkiliç A.
dc.contributor.authorBayramlar K.
dc.contributor.authorEr, Orhan
dc.date.accessioned2024-03-09T19:39:56Z
dc.date.available2024-03-09T19:39:56Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractMortality rate is considered as the most important outcome measure for assessing the severity of burn injury. A scale or model that accurately predicts burn mortality can be useful to determine the clinical course of burn injuries, discuss treatment options and rehabilitation with patients and their families, and evaluate novel, innovative interventions for the injuries. This study aimed to use machine learning models to predict the mortality risk of patients with burns after their first admission to the center and to compare the performances of these models. Overall, 1064 patients hospitalized in burn intensive care and burn service units between 2016 and 2022 were included in the study. In total, 40 parameters, including demographic characteristics and biochemical parameters of all patients, were analyzed in the study. Furthermore, the dataset was randomly divided into two clusters with 70% of the data used for artificial neural networks (ANNs) training and 30% for model success testing. The ANN model proposed in this study showed high success across all machine learning methods tried in different variants, with an accuracy of 95.92% in the test set. Machine learning models can be used to predict the mortality risk of patients with burns. This study may help validate the use of machine learning models for applications in clinical practice. Conducting multicenter studies will further contribute to the literature. © 2023 British Association of Plastic, Reconstructive and Aesthetic Surgeonsen_US
dc.description.sponsorshipNone. None.en_US
dc.identifier.doi10.1016/j.bjps.2023.11.048
dc.identifier.endpage20en_US
dc.identifier.issn1748-6815
dc.identifier.pmid38118361en_US
dc.identifier.scopus2-s2.0-85180576938en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage14en_US
dc.identifier.urihttps://doi.org/10.1016/j.bjps.2023.11.048
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1578
dc.identifier.volume89en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherChurchill Livingstoneen_US
dc.relation.ispartofJournal of Plastic, Reconstructive and Aesthetic Surgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligence; Burns; Machine learning; Mortalityen_US
dc.subjectadult; Article; artificial intelligence; artificial neural network; burn; burn unit; demographics; female; hospital admission; human; intensive care; k nearest neighbor; length of stay; linear support vector machine; machine learning; major clinical study; male; mortality risk; multicenter study; prediction; wound healing; hospitalization; retrospective study; Burn Units; Hospitalization; Humans; Machine Learning; Retrospective Studiesen_US
dc.titleMachine learning models for early prediction of mortality risk in patients with burns: A single center experienceen_US
dc.typeArticleen_US

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