A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries

Asad, Muhammad; Mahmood, Azhar; Usman, Muhammad

doi:10.1016/j.tube.2020.101944

Cited by 15 publications

(14 citation statements)

References 22 publications

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“…As more data is collected increasing the number of cases with the outcomes of interest, it would be interesting to include the subtype of TB as a random effect for instance. While previous analyses have leveraged TB Portals data to predict treatment outcome using machine learning approaches [18,19], they predicted multiple treatment outcomes that may be challenging for machine learning approaches to delineate (e.g. cured versus failure versus died).…”

Section: Plos Onementioning

confidence: 99%

Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases

et al. 2021

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The TB Portals program provides a publicly accessible repository of TB case data containing multi-modal information such as case clinical characteristics, pathogen genomics, and radiomics. The real-world resource contains over 3400 TB cases, primarily drug resistant cases, and CT images with radiologist annotations are available for many of these cases. The breadth of data collected offers a patient-centric view into the etiology of the disease including the temporal context of the available imaging information. Here, we analyze a cohort of new TB cases with available radiologist observations of CTs taken around the time of initial registration of the case into the database and with available follow up to treatment outcome of cured or died. Follow up ranged from 5 weeks to a little over 2 years consistent with the longest treatment regimens for drug resistant TB and cases were registered within the years 2008 to 2019. The radiologist observations were incorporated into machine learning pipelines to test various class balancing strategies on the performance of predictive models. The modeling results support that the radiologist observations are predictive of treatment outcome. Moreover, inferential statistical analysis identifies markers of TB disease spread as having an association with poor treatment outcome including presence of radiologist observations in both lungs, swollen lymph nodes, multiple cavities, and large cavities. While the initial results are promising, further data collection is needed to incorporate methods to mitigate potential confounding such as including additional model covariates or matching cohorts on covariates of interest (e.g. demographics, BMI, comorbidity, TB subtype, etc.). Nonetheless, the preliminary results highlight the utility of the resource for hypothesis generation and exploration of potential biomarkers of TB disease severity and support these additional data collection efforts.

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Section: Plos Onementioning

confidence: 99%

Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases

et al. 2021

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“…These algorithms include the support vector machine [SVM] algorithms that have been used to predict treatment outcomes and disease prognosis. In one of these studies, a dataset containing data from six countries [that suffer from a high burden of tuberculosis [TB] infections was analyzed using ML methods to extrapolate attributes that predicted treatment failure in patients with TB [11]. ML approaches have also been applied in emergency medicine to examine the predictors of opioid prescription inside the emergency department and post discharge [12].…”

Section: Disease Predictionmentioning

confidence: 99%

Artificial Intelligence in Healthcare and Biotechnology: A Review of the Saudi Experience

Dabdoub¹,

Colangelo²,

Aljumah³

2022

J Arti Inte & Cloud Comp

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“…Recently, most studies have used AI and machine learning (ML) models to diagnose TB and explore the data characteristics and features used for algorithm accuracy [ 7 ]. Limited studies have focused on predicting adverse outcomes such as mortality and treatment failure [ 8 , 9 , 10 ]. Our study aimed to use the AI/ML model to detect hepatitis, respiratory failure, and mortality early in patients with TB after receiving anti-TB medications.…”

Section: Introductionmentioning

confidence: 99%

Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis

et al. 2023

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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.

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A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries

Cited by 15 publications

References 22 publications

Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases

Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases

Artificial Intelligence in Healthcare and Biotechnology: A Review of the Saudi Experience

Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis

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