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Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments

Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as ‘Refined Lab-score’ or ‘clinical prediction rule’ have been...

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Autores principales: Lee, Bongjin, Chung, Hyun Jung, Kang, Hyun Mi, Kim, Do Kyun, Kwak, Young Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956167/
https://www.ncbi.nlm.nih.gov/pubmed/35333881
http://dx.doi.org/10.1371/journal.pone.0265500
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author Lee, Bongjin
Chung, Hyun Jung
Kang, Hyun Mi
Kim, Do Kyun
Kwak, Young Ho
author_facet Lee, Bongjin
Chung, Hyun Jung
Kang, Hyun Mi
Kim, Do Kyun
Kwak, Young Ho
author_sort Lee, Bongjin
collection PubMed
description Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as ‘Refined Lab-score’ or ‘clinical prediction rule’ have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children <6 years of age who visited Emergency departments (EDs) of 3 different tertiary hospitals from 2016 to 2018. The SBI prediction model was trained with a derivation cohort (data from two hospitals) and externally tested with a validation cohort (data from a third hospital). A total of 11,973 and 2,858 patient records were included in the derivation and validation cohorts, respectively. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) of the RF model was 0.964 (95% confidence interval [CI], 0.943–0.986), and the area under the precision-recall curve (AUPRC) was 0.753 (95% CI, 0.681–0.824). The conventional LR (CLR) model showed corresponding values of 0.902 (95% CI, 0.894–0.910) and 0.573 (95% CI, 0.560–0.586), respectively. In the validation cohort, the AUROC (95% CI) of the RF model was 0.950 (95% CI, 0.945–0.956), the AUPRC was 0.605 (95% CI, 0.593–0.616), and the CLR presented corresponding values of 0.815 (95% CI, 0.789–0.841) and 0.586 (95% CI, 0.553–0.619), respectively. We developed a machine learning-driven prediction model for SBI among febrile children, which works robustly despite missing values. And it showed superior performance compared to CLR in both internal validation and external validation.
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spelling pubmed-89561672022-03-26 Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments Lee, Bongjin Chung, Hyun Jung Kang, Hyun Mi Kim, Do Kyun Kwak, Young Ho PLoS One Research Article Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as ‘Refined Lab-score’ or ‘clinical prediction rule’ have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children <6 years of age who visited Emergency departments (EDs) of 3 different tertiary hospitals from 2016 to 2018. The SBI prediction model was trained with a derivation cohort (data from two hospitals) and externally tested with a validation cohort (data from a third hospital). A total of 11,973 and 2,858 patient records were included in the derivation and validation cohorts, respectively. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) of the RF model was 0.964 (95% confidence interval [CI], 0.943–0.986), and the area under the precision-recall curve (AUPRC) was 0.753 (95% CI, 0.681–0.824). The conventional LR (CLR) model showed corresponding values of 0.902 (95% CI, 0.894–0.910) and 0.573 (95% CI, 0.560–0.586), respectively. In the validation cohort, the AUROC (95% CI) of the RF model was 0.950 (95% CI, 0.945–0.956), the AUPRC was 0.605 (95% CI, 0.593–0.616), and the CLR presented corresponding values of 0.815 (95% CI, 0.789–0.841) and 0.586 (95% CI, 0.553–0.619), respectively. We developed a machine learning-driven prediction model for SBI among febrile children, which works robustly despite missing values. And it showed superior performance compared to CLR in both internal validation and external validation. Public Library of Science 2022-03-25 /pmc/articles/PMC8956167/ /pubmed/35333881 http://dx.doi.org/10.1371/journal.pone.0265500 Text en © 2022 Lee et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lee, Bongjin
Chung, Hyun Jung
Kang, Hyun Mi
Kim, Do Kyun
Kwak, Young Ho
Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments
title Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments
title_full Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments
title_fullStr Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments
title_full_unstemmed Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments
title_short Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments
title_sort development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956167/
https://www.ncbi.nlm.nih.gov/pubmed/35333881
http://dx.doi.org/10.1371/journal.pone.0265500
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