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Article

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

1
Department of Anaesthesiology and Pain Medicine, College of Medicine, Hallym University, Chuncheon Sacred Heart Hospital, Chuncheon 24253, Korea
2
Institute of New Frontier Research Team, Hallym University, Chuncheon 24253, Korea
3
Department of Internal Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Dana Copot
J. Clin. Med. 2021, 10(10), 2172; https://doi.org/10.3390/jcm10102172
Received: 23 March 2021 / Revised: 14 May 2021 / Accepted: 15 May 2021 / Published: 18 May 2021
(This article belongs to the Section Anesthesiology)
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. View Full-Text
Keywords: machine learning; mechanical ventilation; mortality; prediction machine learning; mechanical ventilation; mortality; prediction
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MDPI and ACS Style

Kim, J.H.; Kwon, Y.S.; Baek, M.S. Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients. J. Clin. Med. 2021, 10, 2172. https://doi.org/10.3390/jcm10102172

AMA Style

Kim JH, Kwon YS, Baek MS. Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients. Journal of Clinical Medicine. 2021; 10(10):2172. https://doi.org/10.3390/jcm10102172

Chicago/Turabian Style

Kim, Jong H.; Kwon, Young S.; Baek, Moon S. 2021. "Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients" J. Clin. Med. 10, no. 10: 2172. https://doi.org/10.3390/jcm10102172

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