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Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data

The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672, 000 confirmed cases and at least 36, 000 reported deaths by June 2020. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate...

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Autores principales: De Souza , Fernanda Sumika Hojo, Hojo-Souza , Natália Satchiko, Dos Santos , Edimilson Batista, Da Silva , Cristiano Maciel, Guidoni , Daniel Ludovico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427867/
https://www.ncbi.nlm.nih.gov/pubmed/34514377
http://dx.doi.org/10.3389/frai.2021.579931
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author De Souza , Fernanda Sumika Hojo
Hojo-Souza , Natália Satchiko
Dos Santos , Edimilson Batista
Da Silva , Cristiano Maciel
Guidoni , Daniel Ludovico
author_facet De Souza , Fernanda Sumika Hojo
Hojo-Souza , Natália Satchiko
Dos Santos , Edimilson Batista
Da Silva , Cristiano Maciel
Guidoni , Daniel Ludovico
author_sort De Souza , Fernanda Sumika Hojo
collection PubMed
description The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672, 000 confirmed cases and at least 36, 000 reported deaths by June 2020. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate treatment, and avoid overloading the healthcare system. Characteristics of patients such as age, comorbidities and varied clinical symptoms can help in classifying the level of infection severity, predict the disease outcome and the need for hospitalization. Here, we present a study to predict a poor prognosis in positive COVID-19 patients and possible outcomes using machine learning. The study dataset comprises information of 8, 443 patients concerning closed cases due to cure or death. Our experimental results show the disease outcome can be predicted with a Receiver Operating Characteristic AUC of 0.92, Sensitivity of 0.88 and Specificity of 0.82 for the best prediction model. This is a preliminary retrospective study which can be improved with the inclusion of further data. Conclusion: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems.
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spelling pubmed-84278672021-09-10 Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data De Souza , Fernanda Sumika Hojo Hojo-Souza , Natália Satchiko Dos Santos , Edimilson Batista Da Silva , Cristiano Maciel Guidoni , Daniel Ludovico Front Artif Intell Artificial Intelligence The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672, 000 confirmed cases and at least 36, 000 reported deaths by June 2020. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate treatment, and avoid overloading the healthcare system. Characteristics of patients such as age, comorbidities and varied clinical symptoms can help in classifying the level of infection severity, predict the disease outcome and the need for hospitalization. Here, we present a study to predict a poor prognosis in positive COVID-19 patients and possible outcomes using machine learning. The study dataset comprises information of 8, 443 patients concerning closed cases due to cure or death. Our experimental results show the disease outcome can be predicted with a Receiver Operating Characteristic AUC of 0.92, Sensitivity of 0.88 and Specificity of 0.82 for the best prediction model. This is a preliminary retrospective study which can be improved with the inclusion of further data. Conclusion: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems. Frontiers Media S.A. 2021-08-13 /pmc/articles/PMC8427867/ /pubmed/34514377 http://dx.doi.org/10.3389/frai.2021.579931 Text en Copyright © 2021 De Souza , Hojo-Souza , Dos Santos , Da Silva  and Guidoni . https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
De Souza , Fernanda Sumika Hojo
Hojo-Souza , Natália Satchiko
Dos Santos , Edimilson Batista
Da Silva , Cristiano Maciel
Guidoni , Daniel Ludovico
Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data
title Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data
title_full Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data
title_fullStr Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data
title_full_unstemmed Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data
title_short Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data
title_sort predicting the disease outcome in covid-19 positive patients through machine learning: a retrospective cohort study with brazilian data
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427867/
https://www.ncbi.nlm.nih.gov/pubmed/34514377
http://dx.doi.org/10.3389/frai.2021.579931
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