Machine learning models for early prediction of mortality risk in patients with burns: A single center experience
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Dosyalar
Tarih
2024
Yazarlar
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Yayıncı
Churchill Livingstone
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Mortality 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 Surgeons
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Anahtar Kelimeler
Artificial intelligence; Burns; Machine learning; Mortality, adult; 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 Studies
