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Identifying Predictors of COVID-19 Mortality Using Machine Learning
(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to inv...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028639/ https://www.ncbi.nlm.nih.gov/pubmed/35455038 http://dx.doi.org/10.3390/life12040547 |
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author | Wan, Tsz-Kin Huang, Rui-Xuan Tulu, Thomas Wetere Liu, Jun-Dong Vodencarevic, Asmir Wong, Chi-Wah Chan, Kei-Hang Katie |
author_facet | Wan, Tsz-Kin Huang, Rui-Xuan Tulu, Thomas Wetere Liu, Jun-Dong Vodencarevic, Asmir Wong, Chi-Wah Chan, Kei-Hang Katie |
author_sort | Wan, Tsz-Kin |
collection | PubMed |
description | (1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84–0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality. |
format | Online Article Text |
id | pubmed-9028639 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90286392022-04-23 Identifying Predictors of COVID-19 Mortality Using Machine Learning Wan, Tsz-Kin Huang, Rui-Xuan Tulu, Thomas Wetere Liu, Jun-Dong Vodencarevic, Asmir Wong, Chi-Wah Chan, Kei-Hang Katie Life (Basel) Article (1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84–0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality. MDPI 2022-04-06 /pmc/articles/PMC9028639/ /pubmed/35455038 http://dx.doi.org/10.3390/life12040547 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wan, Tsz-Kin Huang, Rui-Xuan Tulu, Thomas Wetere Liu, Jun-Dong Vodencarevic, Asmir Wong, Chi-Wah Chan, Kei-Hang Katie Identifying Predictors of COVID-19 Mortality Using Machine Learning |
title | Identifying Predictors of COVID-19 Mortality Using Machine Learning |
title_full | Identifying Predictors of COVID-19 Mortality Using Machine Learning |
title_fullStr | Identifying Predictors of COVID-19 Mortality Using Machine Learning |
title_full_unstemmed | Identifying Predictors of COVID-19 Mortality Using Machine Learning |
title_short | Identifying Predictors of COVID-19 Mortality Using Machine Learning |
title_sort | identifying predictors of covid-19 mortality using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028639/ https://www.ncbi.nlm.nih.gov/pubmed/35455038 http://dx.doi.org/10.3390/life12040547 |
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