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Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features
We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning model...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911952/ https://www.ncbi.nlm.nih.gov/pubmed/36765157 http://dx.doi.org/10.1038/s41598-023-28943-z |
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author | Barough, Siavash Shirzadeh Safavi-Naini, Seyed Amir Ahmad Siavoshi, Fatemeh Tamimi, Atena Ilkhani, Saba Akbari, Setareh Ezzati, Sadaf Hatamabadi, Hamidreza Pourhoseingholi, Mohamad Amin |
author_facet | Barough, Siavash Shirzadeh Safavi-Naini, Seyed Amir Ahmad Siavoshi, Fatemeh Tamimi, Atena Ilkhani, Saba Akbari, Setareh Ezzati, Sadaf Hatamabadi, Hamidreza Pourhoseingholi, Mohamad Amin |
author_sort | Barough, Siavash Shirzadeh |
collection | PubMed |
description | We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system. |
format | Online Article Text |
id | pubmed-9911952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99119522023-02-10 Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features Barough, Siavash Shirzadeh Safavi-Naini, Seyed Amir Ahmad Siavoshi, Fatemeh Tamimi, Atena Ilkhani, Saba Akbari, Setareh Ezzati, Sadaf Hatamabadi, Hamidreza Pourhoseingholi, Mohamad Amin Sci Rep Article We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system. Nature Publishing Group UK 2023-02-10 /pmc/articles/PMC9911952/ /pubmed/36765157 http://dx.doi.org/10.1038/s41598-023-28943-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . |
spellingShingle | Article Barough, Siavash Shirzadeh Safavi-Naini, Seyed Amir Ahmad Siavoshi, Fatemeh Tamimi, Atena Ilkhani, Saba Akbari, Setareh Ezzati, Sadaf Hatamabadi, Hamidreza Pourhoseingholi, Mohamad Amin Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features |
title | Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features |
title_full | Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features |
title_fullStr | Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features |
title_full_unstemmed | Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features |
title_short | Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features |
title_sort | generalizable machine learning approach for covid-19 mortality risk prediction using on-admission clinical and laboratory features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911952/ https://www.ncbi.nlm.nih.gov/pubmed/36765157 http://dx.doi.org/10.1038/s41598-023-28943-z |
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