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Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques
We conducted a statistical study and developed a machine learning model to triage COVID-19 patients affected during the height of the COVID-19 pandemic in Hong Kong based on their medical records and test results (features) collected during their hospitalization. The correlation between the values o...
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/PMC9689804/ https://www.ncbi.nlm.nih.gov/pubmed/36359571 http://dx.doi.org/10.3390/diagnostics12112728 |
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author | Lodato, Ivano Iyer, Aditya Varna To, Isaac Zachary Lai, Zhong-Yuan Chan, Helen Shuk-Ying Leung, Winnie Suk-Wai Tang, Tommy Hing-Cheung Cheung, Victor Kai-Lam Wu, Tak-Chiu Ng, George Wing-Yiu |
author_facet | Lodato, Ivano Iyer, Aditya Varna To, Isaac Zachary Lai, Zhong-Yuan Chan, Helen Shuk-Ying Leung, Winnie Suk-Wai Tang, Tommy Hing-Cheung Cheung, Victor Kai-Lam Wu, Tak-Chiu Ng, George Wing-Yiu |
author_sort | Lodato, Ivano |
collection | PubMed |
description | We conducted a statistical study and developed a machine learning model to triage COVID-19 patients affected during the height of the COVID-19 pandemic in Hong Kong based on their medical records and test results (features) collected during their hospitalization. The correlation between the values of these features is studied against discharge status and disease severity as a preliminary step to identify those features with a more pronounced effect on the patient outcome. Once identified, they constitute the inputs of four machine learning models, Decision Tree, Random Forest, Gradient and RUSBoosting, which predict both the Mortality and Severity associated with the disease. We test the accuracy of the models when the number of input features is varied, demonstrating their stability; i.e., the models are already highly predictive when run over a core set of (6) features. We show that Random Forest and Gradient Boosting classifiers are highly accurate in predicting patients’ Mortality (average accuracy ∼99%) as well as categorize patients (average accuracy ∼91%) into four distinct risk classes (Severity of COVID-19 infection). Our methodical and broad approach combines statistical insights with various machine learning models, which paves the way forward in the AI-assisted triage and prognosis of COVID-19 cases, which is potentially generalizable to other seasonal flus. |
format | Online Article Text |
id | pubmed-9689804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96898042022-11-25 Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques Lodato, Ivano Iyer, Aditya Varna To, Isaac Zachary Lai, Zhong-Yuan Chan, Helen Shuk-Ying Leung, Winnie Suk-Wai Tang, Tommy Hing-Cheung Cheung, Victor Kai-Lam Wu, Tak-Chiu Ng, George Wing-Yiu Diagnostics (Basel) Article We conducted a statistical study and developed a machine learning model to triage COVID-19 patients affected during the height of the COVID-19 pandemic in Hong Kong based on their medical records and test results (features) collected during their hospitalization. The correlation between the values of these features is studied against discharge status and disease severity as a preliminary step to identify those features with a more pronounced effect on the patient outcome. Once identified, they constitute the inputs of four machine learning models, Decision Tree, Random Forest, Gradient and RUSBoosting, which predict both the Mortality and Severity associated with the disease. We test the accuracy of the models when the number of input features is varied, demonstrating their stability; i.e., the models are already highly predictive when run over a core set of (6) features. We show that Random Forest and Gradient Boosting classifiers are highly accurate in predicting patients’ Mortality (average accuracy ∼99%) as well as categorize patients (average accuracy ∼91%) into four distinct risk classes (Severity of COVID-19 infection). Our methodical and broad approach combines statistical insights with various machine learning models, which paves the way forward in the AI-assisted triage and prognosis of COVID-19 cases, which is potentially generalizable to other seasonal flus. MDPI 2022-11-08 /pmc/articles/PMC9689804/ /pubmed/36359571 http://dx.doi.org/10.3390/diagnostics12112728 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 Lodato, Ivano Iyer, Aditya Varna To, Isaac Zachary Lai, Zhong-Yuan Chan, Helen Shuk-Ying Leung, Winnie Suk-Wai Tang, Tommy Hing-Cheung Cheung, Victor Kai-Lam Wu, Tak-Chiu Ng, George Wing-Yiu Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques |
title | Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques |
title_full | Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques |
title_fullStr | Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques |
title_full_unstemmed | Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques |
title_short | Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques |
title_sort | prognostic model of covid-19 severity and survival among hospitalized patients using machine learning techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689804/ https://www.ncbi.nlm.nih.gov/pubmed/36359571 http://dx.doi.org/10.3390/diagnostics12112728 |
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