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Predicting Prolonged Length of ICU Stay through Machine Learning
This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700580/ https://www.ncbi.nlm.nih.gov/pubmed/34943479 http://dx.doi.org/10.3390/diagnostics11122242 |
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author | Wu, Jingyi Lin, Yu Li, Pengfei Hu, Yonghua Zhang, Luxia Kong, Guilan |
author_facet | Wu, Jingyi Lin, Yu Li, Pengfei Hu, Yonghua Zhang, Luxia Kong, Guilan |
author_sort | Wu, Jingyi |
collection | PubMed |
description | This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medical Information Mart for Intensive Care (MIMIC) III database was used for external validation. We used four different ML methods (random forest, support vector machine, deep learning, and gradient boosting decision tree (GBDT)) to develop prediction models. The prediction performance of the four models were compared with the customized simplified acute physiology score (SAPS) II. The area under the receiver operation characteristic curve (AUROC), area under the precision-recall curve (AUPRC), estimated calibration index (ECI), and Brier score were used to measure performance. In internal validation, the GBDT model achieved the best overall performance (Brier score, 0.164), discrimination (AUROC, 0.742; AUPRC, 0.537), and calibration (ECI, 8.224). In external validation, the GBDT model also achieved the best overall performance (Brier score, 0.166), discrimination (AUROC, 0.747; AUPRC, 0.536), and calibration (ECI, 8.294). External validation showed that the calibration curve of the GBDT model was an optimal fit, and four ML models outperformed the customized SAPS II model. The GBDT-based pLOS-ICU prediction model had the best prediction performance among the five models on both internal and external datasets. Furthermore, it has the potential to assist ICU physicians to identify patients with pLOS-ICU risk and provide appropriate clinical interventions to improve patient outcomes. |
format | Online Article Text |
id | pubmed-8700580 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87005802021-12-24 Predicting Prolonged Length of ICU Stay through Machine Learning Wu, Jingyi Lin, Yu Li, Pengfei Hu, Yonghua Zhang, Luxia Kong, Guilan Diagnostics (Basel) Article This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medical Information Mart for Intensive Care (MIMIC) III database was used for external validation. We used four different ML methods (random forest, support vector machine, deep learning, and gradient boosting decision tree (GBDT)) to develop prediction models. The prediction performance of the four models were compared with the customized simplified acute physiology score (SAPS) II. The area under the receiver operation characteristic curve (AUROC), area under the precision-recall curve (AUPRC), estimated calibration index (ECI), and Brier score were used to measure performance. In internal validation, the GBDT model achieved the best overall performance (Brier score, 0.164), discrimination (AUROC, 0.742; AUPRC, 0.537), and calibration (ECI, 8.224). In external validation, the GBDT model also achieved the best overall performance (Brier score, 0.166), discrimination (AUROC, 0.747; AUPRC, 0.536), and calibration (ECI, 8.294). External validation showed that the calibration curve of the GBDT model was an optimal fit, and four ML models outperformed the customized SAPS II model. The GBDT-based pLOS-ICU prediction model had the best prediction performance among the five models on both internal and external datasets. Furthermore, it has the potential to assist ICU physicians to identify patients with pLOS-ICU risk and provide appropriate clinical interventions to improve patient outcomes. MDPI 2021-11-30 /pmc/articles/PMC8700580/ /pubmed/34943479 http://dx.doi.org/10.3390/diagnostics11122242 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Jingyi Lin, Yu Li, Pengfei Hu, Yonghua Zhang, Luxia Kong, Guilan Predicting Prolonged Length of ICU Stay through Machine Learning |
title | Predicting Prolonged Length of ICU Stay through Machine Learning |
title_full | Predicting Prolonged Length of ICU Stay through Machine Learning |
title_fullStr | Predicting Prolonged Length of ICU Stay through Machine Learning |
title_full_unstemmed | Predicting Prolonged Length of ICU Stay through Machine Learning |
title_short | Predicting Prolonged Length of ICU Stay through Machine Learning |
title_sort | predicting prolonged length of icu stay through machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700580/ https://www.ncbi.nlm.nih.gov/pubmed/34943479 http://dx.doi.org/10.3390/diagnostics11122242 |
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