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Early Prediction of Sepsis Based on Machine Learning Algorithm

Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance....

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Detalles Bibliográficos
Autores principales: Zhao, Xin, Shen, Wenqian, Wang, Guanjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526252/
https://www.ncbi.nlm.nih.gov/pubmed/34675971
http://dx.doi.org/10.1155/2021/6522633
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author Zhao, Xin
Shen, Wenqian
Wang, Guanjun
author_facet Zhao, Xin
Shen, Wenqian
Wang, Guanjun
author_sort Zhao, Xin
collection PubMed
description Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.
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spelling pubmed-85262522021-10-20 Early Prediction of Sepsis Based on Machine Learning Algorithm Zhao, Xin Shen, Wenqian Wang, Guanjun Comput Intell Neurosci Research Article Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis. Hindawi 2021-10-12 /pmc/articles/PMC8526252/ /pubmed/34675971 http://dx.doi.org/10.1155/2021/6522633 Text en Copyright © 2021 Xin Zhao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhao, Xin
Shen, Wenqian
Wang, Guanjun
Early Prediction of Sepsis Based on Machine Learning Algorithm
title Early Prediction of Sepsis Based on Machine Learning Algorithm
title_full Early Prediction of Sepsis Based on Machine Learning Algorithm
title_fullStr Early Prediction of Sepsis Based on Machine Learning Algorithm
title_full_unstemmed Early Prediction of Sepsis Based on Machine Learning Algorithm
title_short Early Prediction of Sepsis Based on Machine Learning Algorithm
title_sort early prediction of sepsis based on machine learning algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526252/
https://www.ncbi.nlm.nih.gov/pubmed/34675971
http://dx.doi.org/10.1155/2021/6522633
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