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Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their co...

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Autores principales: Giotta, Massimo, Trerotoli, Paolo, Palmieri, Vincenzo Ostilio, Passerini, Francesca, Portincasa, Piero, Dargenio, Ilaria, Mokhtari, Jihad, Montagna, Maria Teresa, De Vito, Danila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602523/
https://www.ncbi.nlm.nih.gov/pubmed/36293594
http://dx.doi.org/10.3390/ijerph192013016
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author Giotta, Massimo
Trerotoli, Paolo
Palmieri, Vincenzo Ostilio
Passerini, Francesca
Portincasa, Piero
Dargenio, Ilaria
Mokhtari, Jihad
Montagna, Maria Teresa
De Vito, Danila
author_facet Giotta, Massimo
Trerotoli, Paolo
Palmieri, Vincenzo Ostilio
Passerini, Francesca
Portincasa, Piero
Dargenio, Ilaria
Mokhtari, Jihad
Montagna, Maria Teresa
De Vito, Danila
author_sort Giotta, Massimo
collection PubMed
description Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model’s performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.
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spelling pubmed-96025232022-10-27 Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19 Giotta, Massimo Trerotoli, Paolo Palmieri, Vincenzo Ostilio Passerini, Francesca Portincasa, Piero Dargenio, Ilaria Mokhtari, Jihad Montagna, Maria Teresa De Vito, Danila Int J Environ Res Public Health Article Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model’s performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes. MDPI 2022-10-11 /pmc/articles/PMC9602523/ /pubmed/36293594 http://dx.doi.org/10.3390/ijerph192013016 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
Giotta, Massimo
Trerotoli, Paolo
Palmieri, Vincenzo Ostilio
Passerini, Francesca
Portincasa, Piero
Dargenio, Ilaria
Mokhtari, Jihad
Montagna, Maria Teresa
De Vito, Danila
Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19
title Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19
title_full Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19
title_fullStr Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19
title_full_unstemmed Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19
title_short Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19
title_sort application of a decision tree model to predict the outcome of non-intensive inpatients hospitalized for covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602523/
https://www.ncbi.nlm.nih.gov/pubmed/36293594
http://dx.doi.org/10.3390/ijerph192013016
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