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© 2023 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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.

Details

Title
Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis
Author
Kuang-Ming Liao 1   VIAFID ORCID Logo  ; Chung-Feng, Liu 2 ; Chia-Jung, Chen 3 ; Jia-Yih, Feng 4 ; Chin-Chung, Shu 5 ; Yu-Shan, Ma 2 

 Department of Internal Medicine, Chi Mei Medical Center, Chiali, Tainan 722013, Taiwan 
 Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan; [email protected] 
 Department of Information Systems, Chi Mei Medical Center, Tainan 710402, Taiwan 
 Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan; School of Medicine, National Yang-Ming University, Taipei 112304, Taiwan 
 Department of Internal Medicine, National Taiwan University Hospital, Taipei 100225, Taiwan; College of Medicine, National Taiwan University, Taipei 100233, Taiwan 
First page
1075
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20754418
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2791606483
Copyright
© 2023 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.