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Machine learning in the loop for tuberculosis diagnosis support

The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a...

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Autores principales: Orjuela-Cañón, Alvaro D., Jutinico, Andrés L., Awad, Carlos, Vergara, Erika, Palencia, Angélica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9362992/
https://www.ncbi.nlm.nih.gov/pubmed/35958865
http://dx.doi.org/10.3389/fpubh.2022.876949
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author Orjuela-Cañón, Alvaro D.
Jutinico, Andrés L.
Awad, Carlos
Vergara, Erika
Palencia, Angélica
author_facet Orjuela-Cañón, Alvaro D.
Jutinico, Andrés L.
Awad, Carlos
Vergara, Erika
Palencia, Angélica
author_sort Orjuela-Cañón, Alvaro D.
collection PubMed
description The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.
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spelling pubmed-93629922022-08-10 Machine learning in the loop for tuberculosis diagnosis support Orjuela-Cañón, Alvaro D. Jutinico, Andrés L. Awad, Carlos Vergara, Erika Palencia, Angélica Front Public Health Public Health The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited. Frontiers Media S.A. 2022-07-26 /pmc/articles/PMC9362992/ /pubmed/35958865 http://dx.doi.org/10.3389/fpubh.2022.876949 Text en Copyright © 2022 Orjuela-Cañón, Jutinico, Awad, Vergara and Palencia. 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 Public Health
Orjuela-Cañón, Alvaro D.
Jutinico, Andrés L.
Awad, Carlos
Vergara, Erika
Palencia, Angélica
Machine learning in the loop for tuberculosis diagnosis support
title Machine learning in the loop for tuberculosis diagnosis support
title_full Machine learning in the loop for tuberculosis diagnosis support
title_fullStr Machine learning in the loop for tuberculosis diagnosis support
title_full_unstemmed Machine learning in the loop for tuberculosis diagnosis support
title_short Machine learning in the loop for tuberculosis diagnosis support
title_sort machine learning in the loop for tuberculosis diagnosis support
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9362992/
https://www.ncbi.nlm.nih.gov/pubmed/35958865
http://dx.doi.org/10.3389/fpubh.2022.876949
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