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Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to est...

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Autores principales: Yan, Yongjie, Chen, Chongyuan, Liu, Yunyu, Zhang, Zuyue, Xu, Lin, Pu, Kexue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739804/
https://www.ncbi.nlm.nih.gov/pubmed/35004599
http://dx.doi.org/10.3389/fpubh.2021.800549
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author Yan, Yongjie
Chen, Chongyuan
Liu, Yunyu
Zhang, Zuyue
Xu, Lin
Pu, Kexue
author_facet Yan, Yongjie
Chen, Chongyuan
Liu, Yunyu
Zhang, Zuyue
Xu, Lin
Pu, Kexue
author_sort Yan, Yongjie
collection PubMed
description Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data. Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores. Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P < 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance. Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function.
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spelling pubmed-87398042022-01-08 Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin Yan, Yongjie Chen, Chongyuan Liu, Yunyu Zhang, Zuyue Xu, Lin Pu, Kexue Front Public Health Public Health Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data. Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores. Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P < 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance. Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function. Frontiers Media S.A. 2021-12-24 /pmc/articles/PMC8739804/ /pubmed/35004599 http://dx.doi.org/10.3389/fpubh.2021.800549 Text en Copyright © 2021 Yan, Chen, Liu, Zhang, Xu and Pu. 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
Yan, Yongjie
Chen, Chongyuan
Liu, Yunyu
Zhang, Zuyue
Xu, Lin
Pu, Kexue
Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin
title Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin
title_full Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin
title_fullStr Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin
title_full_unstemmed Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin
title_short Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin
title_sort application of machine learning for the prediction of etiological types of classic fever of unknown origin
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739804/
https://www.ncbi.nlm.nih.gov/pubmed/35004599
http://dx.doi.org/10.3389/fpubh.2021.800549
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