Research graph
References from Spatial Transferability of Explainable Machine Learning for Predicting Pre-Treatment Tuberculosis Loss to Follow-up Across West Java Districts. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2023 · External reference
Pre-treatment loss to follow-up in tuberculosis patients in low- and lower-middle-income countries and high-burden countries: a systematic review and meta-analysis
10.2471/blt.13.124800 · 2014 · External reference
Closing gaps in the tuberculosis care cascade: an action-oriented research agenda
10.1016/j.jctube.2020.100144 · 2020 · External reference
Early identification of individuals at risk for loss to follow-up of tuberculosis treatment: A generalised hierarchical analysis
10.1016/j.heliyon.2021.e06788 · 2021 · External reference
Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment
10.1186/s12889-024-18815-0 · 2024 · External reference
Applying machine learning to analyze tuberculosis service standards in Indonesia
10.37268/mjphm/26/1/art.3698 · 2026 · External reference
Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey
10.1038/s41598-024-74942-z · 2024 · External reference
Leveraging machine learning for predictive analysis of tuberculosis treatment outcomes: A comprehensive study using Karnataka TB data
10.1016/j.nexres.2024.100011 · 2024 · External reference
Enhanced cardiovascular disease risk prediction using explainable machine learning and data balancing
10.1007/s10791-026-09973-3 · 2026 · External reference
An empirical study of machine learning robustness and scalability for imbalanced tabular clinical data in emergency and critical care
10.1038/s41598-026-56413-9 · 2026 · External reference
Evaluating XAI techniques under class imbalance using CPRD data
10.3389/frai.2025.1682919 · 2025 · External reference
Unresolved reference
2017 · External reference
Unresolved reference
2022 · External reference
Random Forests
10.1023/a:1010933404324 · 2001 · External reference
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203451 · 2001 · External reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · 2016 · External reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity
10.1038/s41598-024-57446-8 · 2024 · External reference
Interpretable Machine Learning in Predicting Drug-Induced Liver Injury among Tuberculosis Patients: Model Development and Validation Study
10.21203/rs.3.rs-3423244/v1 · 2023 · External reference
Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia
10.1371/journal.pone.0315912 · 2025 · External reference
Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning
10.1186/s12879-025-10609-y · 2025 · External reference
An explainable machine learning framework for cardiovascular risk prediction using structured health data
10.3389/frai.2026.1812622 · 2026 · External reference
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
10.3390/diagnostics12102392 · 2022 · External reference
Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement
10.21203/rs.3.rs-28409/v2 · 2020 · External reference
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
10.1371/journal.pone.0118432 · 2015 · External reference
Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations
10.1186/s12911-025-03139-9 · 2025 · External reference
A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district
10.1371/journal.pgph.0001466 · 2023 · External reference
Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis
10.1371/journal.pone.0309151 · 2024 · External reference
Using machine learning methods to predict early treatment outcomes for multidrug-resistant or rifampicin-resistant tuberculosis to enhance patient cure rates: development and validation of multiple models
10.2196/69998 · 2025 · External reference
Machine learning in the loop for tuberculosis diagnosis support
10.3389/fpubh.2022.876949 · 2022 · External reference
completion among drug-sensitive tuberculosis patients: evidence from Indonesia's tuberculosis surveillance system
2024 · External reference
Incidence and predictors of lost to follow-up among drug-resistant tuberculosis patients at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia: a retrospective follow-up study
10.1186/s12879-019-4447-8 · 2019 · External reference
Risk factors associated with loss to follow-up from tuberculosis treatment in Tajikistan: a case-control study
10.1186/s12879-017-2655-7 · 2017 · External reference
Prediction of treatment failure and compliance in patients with tuberculosis
10.1186/s12879-020-05350-7 · 2020 · External reference
Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review
10.1186/s12874-022-01801-8 · 2022 · External reference
Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage
10.1186/s12874-024-02280-9 · 2024 · External reference
There is no such thing as a validated prediction model
10.1186/s12916-023-02779-w · 2023 · External reference
Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation
10.1186/s12874-020-00991-3 · 2020 · External reference
Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda
10.1186/s12879-024-10282-7 · 2024 · External reference
Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers
10.1186/s12879-025-12029-4 · 2025 · External reference
The validation of prediction models deserves more recognition
10.1186/s12916-025-03994-3 · 2025 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Enhanced cardiovascular disease risk prediction using explainable machine learning and data balancing
10.1007/s10791-026-09973-3 · ExternalCitation · doi-reference
Early identification of individuals at risk for loss to follow-up of tuberculosis treatment: A generalised hierarchical analysis
10.1016/j.heliyon.2021.e06788 · ExternalCitation · doi-reference
Closing gaps in the tuberculosis care cascade: an action-oriented research agenda
10.1016/j.jctube.2020.100144 · ExternalCitation · doi-reference
Leveraging machine learning for predictive analysis of tuberculosis treatment outcomes: A comprehensive study using Karnataka TB data
10.1016/j.nexres.2024.100011 · ExternalCitation · doi-reference
Random Forests
10.1023/a:1010933404324 · ExternalCitation · doi-reference
Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity
10.1038/s41598-024-57446-8 · ExternalCitation · doi-reference
Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey
10.1038/s41598-024-74942-z · ExternalCitation · doi-reference
An empirical study of machine learning robustness and scalability for imbalanced tabular clinical data in emergency and critical care
10.1038/s41598-026-56413-9 · ExternalCitation · doi-reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · ExternalCitation · doi-reference
Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation
10.1186/s12874-020-00991-3 · ExternalCitation · doi-reference
Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review
10.1186/s12874-022-01801-8 · ExternalCitation · doi-reference
Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage
10.1186/s12874-024-02280-9 · ExternalCitation · doi-reference
Risk factors associated with loss to follow-up from tuberculosis treatment in Tajikistan: a case-control study
10.1186/s12879-017-2655-7 · ExternalCitation · doi-reference
Incidence and predictors of lost to follow-up among drug-resistant tuberculosis patients at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia: a retrospective follow-up study
10.1186/s12879-019-4447-8 · ExternalCitation · doi-reference
Prediction of treatment failure and compliance in patients with tuberculosis
10.1186/s12879-020-05350-7 · ExternalCitation · doi-reference
Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda
10.1186/s12879-024-10282-7 · ExternalCitation · doi-reference
Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning
10.1186/s12879-025-10609-y · ExternalCitation · doi-reference
Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers
10.1186/s12879-025-12029-4 · ExternalCitation · doi-reference
Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment
10.1186/s12889-024-18815-0 · ExternalCitation · doi-reference
Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations
10.1186/s12911-025-03139-9 · ExternalCitation · doi-reference
There is no such thing as a validated prediction model
10.1186/s12916-023-02779-w · ExternalCitation · doi-reference
The validation of prediction models deserves more recognition
10.1186/s12916-025-03994-3 · ExternalCitation · doi-reference
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203451 · ExternalCitation · doi-reference
A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district
10.1371/journal.pgph.0001466 · ExternalCitation · doi-reference
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
10.1371/journal.pone.0118432 · ExternalCitation · doi-reference
Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis
10.1371/journal.pone.0309151 · ExternalCitation · doi-reference
Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia
10.1371/journal.pone.0315912 · ExternalCitation · doi-reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · ExternalCitation · doi-reference
Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement
10.21203/rs.3.rs-28409/v2 · ExternalCitation · doi-reference
Interpretable Machine Learning in Predicting Drug-Induced Liver Injury among Tuberculosis Patients: Model Development and Validation Study
10.21203/rs.3.rs-3423244/v1 · ExternalCitation · doi-reference
Using machine learning methods to predict early treatment outcomes for multidrug-resistant or rifampicin-resistant tuberculosis to enhance patient cure rates: development and validation of multiple models
10.2196/69998 · ExternalCitation · doi-reference
Pre-treatment loss to follow-up in tuberculosis patients in low- and lower-middle-income countries and high-burden countries: a systematic review and meta-analysis
10.2471/blt.13.124800 · ExternalCitation · doi-reference
Machine learning in the loop for tuberculosis diagnosis support
10.3389/fpubh.2022.876949 · ExternalCitation · doi-reference
Evaluating XAI techniques under class imbalance using CPRD data
10.3389/frai.2025.1682919 · ExternalCitation · doi-reference
An explainable machine learning framework for cardiovascular risk prediction using structured health data
10.3389/frai.2026.1812622 · ExternalCitation · doi-reference
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
10.3390/diagnostics12102392 · ExternalCitation · doi-reference
Applying machine learning to analyze tuberculosis service standards in Indonesia
10.37268/mjphm/26/1/art.3698 · ExternalCitation · doi-reference
