Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

Abstract The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were...

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Autores principales:	Bongjin Lee, Kyunghoon Kim, Hyejin Hwang, You Sun Kim, Eun Hee Chung, Jong-Seo Yoon, Hwa Jin Cho, June Dong Park
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4da96a73bc7b43adbfc21a0103820d24
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spelling	oai:doaj.org-article:4da96a73bc7b43adbfc21a0103820d242021-12-02T14:12:42ZDevelopment of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission10.1038/s41598-020-80474-z2045-2322https://doaj.org/article/4da96a73bc7b43adbfc21a0103820d242021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80474-zhttps://doaj.org/toc/2045-2322Abstract The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.Bongjin LeeKyunghoon KimHyejin HwangYou Sun KimEun Hee ChungJong-Seo YoonHwa Jin ChoJune Dong ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bongjin Lee Kyunghoon Kim Hyejin Hwang You Sun Kim Eun Hee Chung Jong-Seo Yoon Hwa Jin Cho June Dong Park Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
description	Abstract The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.
format	article
author	Bongjin Lee Kyunghoon Kim Hyejin Hwang You Sun Kim Eun Hee Chung Jong-Seo Yoon Hwa Jin Cho June Dong Park
author_facet	Bongjin Lee Kyunghoon Kim Hyejin Hwang You Sun Kim Eun Hee Chung Jong-Seo Yoon Hwa Jin Cho June Dong Park
author_sort	Bongjin Lee
title	Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
title_short	Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
title_full	Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
title_fullStr	Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
title_full_unstemmed	Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
title_sort	development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4da96a73bc7b43adbfc21a0103820d24
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Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

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