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Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis
BACKGROUND: Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. METHODS: Two ICU databases were employed: eICU Collaborative Research Da...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365981/ https://www.ncbi.nlm.nih.gov/pubmed/34399809 http://dx.doi.org/10.1186/s13040-021-00276-5 |
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author | Zeng, Zhixuan Yao, Shuo Zheng, Jianfei Gong, Xun |
author_facet | Zeng, Zhixuan Yao, Shuo Zheng, Jianfei Gong, Xun |
author_sort | Zeng, Zhixuan |
collection | PubMed |
description | BACKGROUND: Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. METHODS: Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. RESULTS: Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. CONCLUSIONS: The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13040-021-00276-5. |
format | Online Article Text |
id | pubmed-8365981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83659812021-08-17 Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis Zeng, Zhixuan Yao, Shuo Zheng, Jianfei Gong, Xun BioData Min Research BACKGROUND: Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. METHODS: Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. RESULTS: Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. CONCLUSIONS: The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13040-021-00276-5. BioMed Central 2021-08-16 /pmc/articles/PMC8365981/ /pubmed/34399809 http://dx.doi.org/10.1186/s13040-021-00276-5 Text en © The Author(s) 2021 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 Zeng, Zhixuan Yao, Shuo Zheng, Jianfei Gong, Xun Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis |
title | Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis |
title_full | Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis |
title_fullStr | Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis |
title_full_unstemmed | Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis |
title_short | Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis |
title_sort | development and validation of a novel blending machine learning model for hospital mortality prediction in icu patients with sepsis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365981/ https://www.ncbi.nlm.nih.gov/pubmed/34399809 http://dx.doi.org/10.1186/s13040-021-00276-5 |
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