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Using machine learning to improve our understanding of COVID-19 infection in children
PURPOSE: Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not. METHODS: A total of 2572 chest...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931095/ https://www.ncbi.nlm.nih.gov/pubmed/36791067 http://dx.doi.org/10.1371/journal.pone.0281666 |
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author | Piparia, Shraddha Defante, Andrew Tantisira, Kelan Ryu, Julie |
author_facet | Piparia, Shraddha Defante, Andrew Tantisira, Kelan Ryu, Julie |
author_sort | Piparia, Shraddha |
collection | PubMed |
description | PURPOSE: Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not. METHODS: A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection. RESULTS: Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection. CONCLUSIONS: In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score. |
format | Online Article Text |
id | pubmed-9931095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99310952023-02-16 Using machine learning to improve our understanding of COVID-19 infection in children Piparia, Shraddha Defante, Andrew Tantisira, Kelan Ryu, Julie PLoS One Research Article PURPOSE: Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not. METHODS: A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection. RESULTS: Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection. CONCLUSIONS: In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score. Public Library of Science 2023-02-15 /pmc/articles/PMC9931095/ /pubmed/36791067 http://dx.doi.org/10.1371/journal.pone.0281666 Text en © 2023 Piparia 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 Piparia, Shraddha Defante, Andrew Tantisira, Kelan Ryu, Julie Using machine learning to improve our understanding of COVID-19 infection in children |
title | Using machine learning to improve our understanding of COVID-19 infection in children |
title_full | Using machine learning to improve our understanding of COVID-19 infection in children |
title_fullStr | Using machine learning to improve our understanding of COVID-19 infection in children |
title_full_unstemmed | Using machine learning to improve our understanding of COVID-19 infection in children |
title_short | Using machine learning to improve our understanding of COVID-19 infection in children |
title_sort | using machine learning to improve our understanding of covid-19 infection in children |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931095/ https://www.ncbi.nlm.nih.gov/pubmed/36791067 http://dx.doi.org/10.1371/journal.pone.0281666 |
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