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Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder

BACKGROUND: Early prediction of noninvasive ventilation failure is of great significance for critically ill ICU patients to escalate or change treatment. Because clinically collected data are highly time-series correlated and have imbalanced classes, it is difficult to accurately predict the efficac...

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Autores principales: Liang, Na, Wang, Chengliang, Duan, Jun, Xie, Xin, Wang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805397/
https://www.ncbi.nlm.nih.gov/pubmed/35101003
http://dx.doi.org/10.1186/s12911-022-01767-z
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author Liang, Na
Wang, Chengliang
Duan, Jun
Xie, Xin
Wang, Yu
author_facet Liang, Na
Wang, Chengliang
Duan, Jun
Xie, Xin
Wang, Yu
author_sort Liang, Na
collection PubMed
description BACKGROUND: Early prediction of noninvasive ventilation failure is of great significance for critically ill ICU patients to escalate or change treatment. Because clinically collected data are highly time-series correlated and have imbalanced classes, it is difficult to accurately predict the efficacy of noninvasive ventilation for severe patients. This paper aims to precisely predict the failure probability of noninvasive ventilation before or in the early stage (1–2 h) of using it on patients and to explain the correlation of the predicted results. METHODS: In this paper, we proposed a SMSN model (stacking and modified SMOTE algorithm of prediction of noninvasive ventilation failure). In the feature generation stage, we used an autoencoder algorithm based on long short-term memory (LSTM) to automatically extract time series features. In the modelling stage, we adopted a modified SMOTE algorithm to address imbalanced classes, and three classifiers (logistic regression, random forests, and Catboost) were combined with the stacking ensemble algorithm to achieve high prediction accuracy. RESULTS: Data from 2495 patients were used to train the SMSN model. Among them, 80% of 2495 patients (1996 patients) were randomly selected as the training set, and 20% of these patients (499 patients) were chosen as the testing set. The F1 of the proposed SMSN model was 79.4%, and the accuracy was 88.2%. Compared with the traditional logistic regression algorithm, the F1 and accuracy were improved by 4.7% and 1.3%, respectively. CONCLUSIONS: Through SHAP analysis, oxygenation index, pH and H1FIO(2) collected after 1 h of noninvasive ventilation were the most relevant features affecting the prediction.
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spelling pubmed-88053972022-02-03 Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder Liang, Na Wang, Chengliang Duan, Jun Xie, Xin Wang, Yu BMC Med Inform Decis Mak Research BACKGROUND: Early prediction of noninvasive ventilation failure is of great significance for critically ill ICU patients to escalate or change treatment. Because clinically collected data are highly time-series correlated and have imbalanced classes, it is difficult to accurately predict the efficacy of noninvasive ventilation for severe patients. This paper aims to precisely predict the failure probability of noninvasive ventilation before or in the early stage (1–2 h) of using it on patients and to explain the correlation of the predicted results. METHODS: In this paper, we proposed a SMSN model (stacking and modified SMOTE algorithm of prediction of noninvasive ventilation failure). In the feature generation stage, we used an autoencoder algorithm based on long short-term memory (LSTM) to automatically extract time series features. In the modelling stage, we adopted a modified SMOTE algorithm to address imbalanced classes, and three classifiers (logistic regression, random forests, and Catboost) were combined with the stacking ensemble algorithm to achieve high prediction accuracy. RESULTS: Data from 2495 patients were used to train the SMSN model. Among them, 80% of 2495 patients (1996 patients) were randomly selected as the training set, and 20% of these patients (499 patients) were chosen as the testing set. The F1 of the proposed SMSN model was 79.4%, and the accuracy was 88.2%. Compared with the traditional logistic regression algorithm, the F1 and accuracy were improved by 4.7% and 1.3%, respectively. CONCLUSIONS: Through SHAP analysis, oxygenation index, pH and H1FIO(2) collected after 1 h of noninvasive ventilation were the most relevant features affecting the prediction. BioMed Central 2022-01-31 /pmc/articles/PMC8805397/ /pubmed/35101003 http://dx.doi.org/10.1186/s12911-022-01767-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liang, Na
Wang, Chengliang
Duan, Jun
Xie, Xin
Wang, Yu
Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder
title Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder
title_full Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder
title_fullStr Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder
title_full_unstemmed Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder
title_short Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder
title_sort efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805397/
https://www.ncbi.nlm.nih.gov/pubmed/35101003
http://dx.doi.org/10.1186/s12911-022-01767-z
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