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Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to i...

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Autores principales: Kim, Jong Ho, Kwon, Young Suk, Baek, Moon Seong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157228/
https://www.ncbi.nlm.nih.gov/pubmed/34069799
http://dx.doi.org/10.3390/jcm10102172
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author Kim, Jong Ho
Kwon, Young Suk
Baek, Moon Seong
author_facet Kim, Jong Ho
Kwon, Young Suk
Baek, Moon Seong
author_sort Kim, Jong Ho
collection PubMed
description Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.
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spelling pubmed-81572282021-05-28 Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients Kim, Jong Ho Kwon, Young Suk Baek, Moon Seong J Clin Med Article Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients. MDPI 2021-05-18 /pmc/articles/PMC8157228/ /pubmed/34069799 http://dx.doi.org/10.3390/jcm10102172 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Jong Ho
Kwon, Young Suk
Baek, Moon Seong
Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients
title Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients
title_full Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients
title_fullStr Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients
title_full_unstemmed Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients
title_short Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients
title_sort machine learning models to predict 30-day mortality in mechanically ventilated patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157228/
https://www.ncbi.nlm.nih.gov/pubmed/34069799
http://dx.doi.org/10.3390/jcm10102172
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