Another study showed that advanced age and presence of shock unrelated to sepsis were independently associated with mortality after multivariate analysis.įurthermore, we chose 36 feature variables, based on literature evidence and clinical experience, for these models. The independent risk factors of in-hospital mortality were severe hypoxemia and kidney injury. found that most patients with TB with acute respiratory failure were newly diagnosed patients, and had advanced lesions and hypoxemic type respiratory failure. Patients with TB who develop hepatitis during the treatment may need to change TB medications if the hepatitis is severe. For example, Ramappa and Aithal found that the TB medication that can cause hepatitis included isoniazid, rifampicin, and pyrazinamide. Therefore, when patients take these anti-TB medications, physicians need to monitor the patient’s liver enzymes and be aware of the risk of hepatitis. Most tuberculosis medications can be toxic to the liver and have the adverse effect of hepatitis. Patients infected with TB can be effectively treated with anti-TB medication, and the drug regimen, dosage, and length of treatment period depend on whether it is a drug-resistant strain, what comorbidities are present (diabetes, HIV, liver disease, renal disease, etc.), and where is the infection located in the body. Conclusions: Our AI models were good predictors and can provide clinicians with a valuable tool to detect the adverse prognosis in TB patients early. 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. 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. These algorithms included XGBoost, random forest, MLP, light GBM, logistic regression, and SVM. The data were randomly stratified into a training dataset for model building (70%) and a testing dataset for model validation (30%). Thirty-six feature variables were used to develop the predictive models with AI. 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. 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. Therefore, it is important to detect and predict these adverse effects early. However, anti-TB drug treatments may result in many adverse effects. Without treatment, the mortality rate of TB is approximately 50% with treatment, most patients with TB can be cured. Background: Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health.
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