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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8157228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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