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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-8956167 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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