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Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis
Background: Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health. Without treatment, the mortality rate of TB is approximately 50%; with treatment, most patients with TB can be cured. However, anti-TB drug treatments may result in many adverse effects. Th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047137/ https://www.ncbi.nlm.nih.gov/pubmed/36980382 http://dx.doi.org/10.3390/diagnostics13061075 |
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author | Liao, Kuang-Ming Liu, Chung-Feng Chen, Chia-Jung Feng, Jia-Yih Shu, Chin-Chung Ma, Yu-Shan |
author_facet | Liao, Kuang-Ming Liu, Chung-Feng Chen, Chia-Jung Feng, Jia-Yih Shu, Chin-Chung Ma, Yu-Shan |
author_sort | Liao, Kuang-Ming |
collection | PubMed |
description | Background: Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health. Without treatment, the mortality rate of TB is approximately 50%; with treatment, most patients with TB can be cured. However, anti-TB drug treatments may result in many adverse effects. Therefore, it is important to detect and predict these adverse effects early. Our study aimed to build models using an artificial intelligence/machine learning approach to predict acute hepatitis, acute respiratory failure, and mortality after TB treatment. Materials and Methods: Adult patients (age ≥ 20 years) who had a TB diagnosis and received treatment from January 2004 to December 2021 were enrolled in the present study. Thirty-six feature variables were used to develop the predictive models with AI. The data were randomly stratified into a training dataset for model building (70%) and a testing dataset for model validation (30%). These algorithms included XGBoost, random forest, MLP, light GBM, logistic regression, and SVM. Results: A total of 2248 TB patients in Chi Mei Medical Center were included in the study; 71.7% were males, and the other 28.3% were females. The mean age was 67.7 ± 16.4 years. The results showed that our models using the six AI algorithms all had a high area under the receiver operating characteristic curve (AUC) in predicting acute hepatitis, respiratory failure, and mortality, and the AUCs ranged from 0.920 to 0.766, 0.884 to 0.797, and 0.834 to 0.737, respectively. Conclusions: Our AI models were good predictors and can provide clinicians with a valuable tool to detect the adverse prognosis in TB patients early. |
format | Online Article Text |
id | pubmed-10047137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100471372023-03-29 Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis Liao, Kuang-Ming Liu, Chung-Feng Chen, Chia-Jung Feng, Jia-Yih Shu, Chin-Chung Ma, Yu-Shan Diagnostics (Basel) Article Background: Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health. Without treatment, the mortality rate of TB is approximately 50%; with treatment, most patients with TB can be cured. However, anti-TB drug treatments may result in many adverse effects. Therefore, it is important to detect and predict these adverse effects early. Our study aimed to build models using an artificial intelligence/machine learning approach to predict acute hepatitis, acute respiratory failure, and mortality after TB treatment. Materials and Methods: Adult patients (age ≥ 20 years) who had a TB diagnosis and received treatment from January 2004 to December 2021 were enrolled in the present study. Thirty-six feature variables were used to develop the predictive models with AI. The data were randomly stratified into a training dataset for model building (70%) and a testing dataset for model validation (30%). These algorithms included XGBoost, random forest, MLP, light GBM, logistic regression, and SVM. Results: A total of 2248 TB patients in Chi Mei Medical Center were included in the study; 71.7% were males, and the other 28.3% were females. The mean age was 67.7 ± 16.4 years. The results showed that our models using the six AI algorithms all had a high area under the receiver operating characteristic curve (AUC) in predicting acute hepatitis, respiratory failure, and mortality, and the AUCs ranged from 0.920 to 0.766, 0.884 to 0.797, and 0.834 to 0.737, respectively. Conclusions: Our AI models were good predictors and can provide clinicians with a valuable tool to detect the adverse prognosis in TB patients early. MDPI 2023-03-13 /pmc/articles/PMC10047137/ /pubmed/36980382 http://dx.doi.org/10.3390/diagnostics13061075 Text en © 2023 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 Liao, Kuang-Ming Liu, Chung-Feng Chen, Chia-Jung Feng, Jia-Yih Shu, Chin-Chung Ma, Yu-Shan Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis |
title | Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis |
title_full | Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis |
title_fullStr | Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis |
title_full_unstemmed | Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis |
title_short | Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis |
title_sort | using an artificial intelligence approach to predict the adverse effects and prognosis of tuberculosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047137/ https://www.ncbi.nlm.nih.gov/pubmed/36980382 http://dx.doi.org/10.3390/diagnostics13061075 |
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