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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....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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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. |
format | Online Article Text |
id | pubmed-8526252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhaoxin earlypredictionofsepsisbasedonmachinelearningalgorithm AT shenwenqian earlypredictionofsepsisbasedonmachinelearningalgorithm AT wangguanjun earlypredictionofsepsisbasedonmachinelearningalgorithm |