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OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or th...

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Autores principales: EL-Manzalawy, Yasser, Abbas, Mostafa, Hoaglund, Ian, Cerna, Alvaro Ulloa, Morland, Thomas B., Haggerty, Christopher M., Hall, Eric S., Fornwalt, Brandon K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118103/
https://www.ncbi.nlm.nih.gov/pubmed/33985483
http://dx.doi.org/10.1186/s12911-021-01517-7
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author EL-Manzalawy, Yasser
Abbas, Mostafa
Hoaglund, Ian
Cerna, Alvaro Ulloa
Morland, Thomas B.
Haggerty, Christopher M.
Hall, Eric S.
Fornwalt, Brandon K.
author_facet EL-Manzalawy, Yasser
Abbas, Mostafa
Hoaglund, Ian
Cerna, Alvaro Ulloa
Morland, Thomas B.
Haggerty, Christopher M.
Hall, Eric S.
Fornwalt, Brandon K.
author_sort EL-Manzalawy, Yasser
collection PubMed
description BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved. METHODS: We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS. RESULTS: Using a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores. CONCLUSIONS: Our results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01517-7.
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spelling pubmed-81181032021-05-14 OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality EL-Manzalawy, Yasser Abbas, Mostafa Hoaglund, Ian Cerna, Alvaro Ulloa Morland, Thomas B. Haggerty, Christopher M. Hall, Eric S. Fornwalt, Brandon K. BMC Med Inform Decis Mak Research Article BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved. METHODS: We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS. RESULTS: Using a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores. CONCLUSIONS: Our results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01517-7. BioMed Central 2021-05-13 /pmc/articles/PMC8118103/ /pubmed/33985483 http://dx.doi.org/10.1186/s12911-021-01517-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
EL-Manzalawy, Yasser
Abbas, Mostafa
Hoaglund, Ian
Cerna, Alvaro Ulloa
Morland, Thomas B.
Haggerty, Christopher M.
Hall, Eric S.
Fornwalt, Brandon K.
OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
title OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
title_full OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
title_fullStr OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
title_full_unstemmed OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
title_short OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality
title_sort oasis +: leveraging machine learning to improve the prognostic accuracy of oasis severity score for predicting in-hospital mortality
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118103/
https://www.ncbi.nlm.nih.gov/pubmed/33985483
http://dx.doi.org/10.1186/s12911-021-01517-7
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