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Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values
Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259985/ https://www.ncbi.nlm.nih.gov/pubmed/34161326 http://dx.doi.org/10.1371/journal.pcbi.1009121 |
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author | Cavallaro, Massimo Moiz, Haseeb Keeling, Matt J. McCarthy, Noel D. |
author_facet | Cavallaro, Massimo Moiz, Haseeb Keeling, Matt J. McCarthy, Noel D. |
author_sort | Cavallaro, Massimo |
collection | PubMed |
description | Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases. |
format | Online Article Text |
id | pubmed-8259985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82599852021-07-19 Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values Cavallaro, Massimo Moiz, Haseeb Keeling, Matt J. McCarthy, Noel D. PLoS Comput Biol Research Article Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases. Public Library of Science 2021-06-23 /pmc/articles/PMC8259985/ /pubmed/34161326 http://dx.doi.org/10.1371/journal.pcbi.1009121 Text en © 2021 Cavallaro 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 Cavallaro, Massimo Moiz, Haseeb Keeling, Matt J. McCarthy, Noel D. Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values |
title | Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values |
title_full | Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values |
title_fullStr | Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values |
title_full_unstemmed | Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values |
title_short | Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values |
title_sort | contrasting factors associated with covid-19-related icu admission and death outcomes in hospitalised patients by means of shapley values |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259985/ https://www.ncbi.nlm.nih.gov/pubmed/34161326 http://dx.doi.org/10.1371/journal.pcbi.1009121 |
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