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Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population

The NICHD BPD Outcome Estimator uses clinical and demographic data to stratify respiratory outcomes of extremely preterm infants by risk. However, the Estimator does not have an option in its pull-down menu for infants of Asian descent. We hypothesize that respiratory outcomes in extreme prematurity...

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Autores principales: Patel, Monalisa, Sandhu, Japmeet, Chou, Fu-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9480997/
https://www.ncbi.nlm.nih.gov/pubmed/36112600
http://dx.doi.org/10.1371/journal.pone.0272709
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author Patel, Monalisa
Sandhu, Japmeet
Chou, Fu-Sheng
author_facet Patel, Monalisa
Sandhu, Japmeet
Chou, Fu-Sheng
author_sort Patel, Monalisa
collection PubMed
description The NICHD BPD Outcome Estimator uses clinical and demographic data to stratify respiratory outcomes of extremely preterm infants by risk. However, the Estimator does not have an option in its pull-down menu for infants of Asian descent. We hypothesize that respiratory outcomes in extreme prematurity among various racial/ethnic groups are interconnected and therefore the Estimator can still be used to predict outcomes in infants of Asian descent. Our goal was to apply a machine learning approach to assess whether outcome prediction for infants of Asian descent is possible with information hidden in the prediction results using White, Black, and Hispanic racial/ethnic groups as surrogates. We used the three racial/ethnic options in the Estimator to obtain the probabilities of BPD outcomes for each severity category. We then combined the probability results and developed three respiratory outcome prediction models at various postmenstrual age (PMA) by a random forest algorithm. We showed satisfactory model performance, with receiver operating characteristics area under the curve of 0.934, 0.850, and 0.757 for respiratory outcomes at PMA 36, 37, and 40 weeks, respectively, in the testing data set. This study suggested an interrelationship among racial/ethnic groups for respiratory outcomes among extremely preterm infants and showed the feasibility of extending the use of the Estimator to the Asian population.
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spelling pubmed-94809972022-09-17 Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population Patel, Monalisa Sandhu, Japmeet Chou, Fu-Sheng PLoS One Research Article The NICHD BPD Outcome Estimator uses clinical and demographic data to stratify respiratory outcomes of extremely preterm infants by risk. However, the Estimator does not have an option in its pull-down menu for infants of Asian descent. We hypothesize that respiratory outcomes in extreme prematurity among various racial/ethnic groups are interconnected and therefore the Estimator can still be used to predict outcomes in infants of Asian descent. Our goal was to apply a machine learning approach to assess whether outcome prediction for infants of Asian descent is possible with information hidden in the prediction results using White, Black, and Hispanic racial/ethnic groups as surrogates. We used the three racial/ethnic options in the Estimator to obtain the probabilities of BPD outcomes for each severity category. We then combined the probability results and developed three respiratory outcome prediction models at various postmenstrual age (PMA) by a random forest algorithm. We showed satisfactory model performance, with receiver operating characteristics area under the curve of 0.934, 0.850, and 0.757 for respiratory outcomes at PMA 36, 37, and 40 weeks, respectively, in the testing data set. This study suggested an interrelationship among racial/ethnic groups for respiratory outcomes among extremely preterm infants and showed the feasibility of extending the use of the Estimator to the Asian population. Public Library of Science 2022-09-16 /pmc/articles/PMC9480997/ /pubmed/36112600 http://dx.doi.org/10.1371/journal.pone.0272709 Text en © 2022 Patel 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
Patel, Monalisa
Sandhu, Japmeet
Chou, Fu-Sheng
Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population
title Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population
title_full Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population
title_fullStr Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population
title_full_unstemmed Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population
title_short Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population
title_sort developing a machine learning-based tool to extend the usability of the nichd bpd outcome estimator to the asian population
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9480997/
https://www.ncbi.nlm.nih.gov/pubmed/36112600
http://dx.doi.org/10.1371/journal.pone.0272709
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