ArticleBMC medical research methodology2024
Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Development and internal validation of an interpretable machine learning model for predicting vancomycin-induced nephrotoxicity in hospitalized children.International journal of clinical pharmacy · 2026Article
- Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy.International ophthalmology · 2026Article
- Use of deep learning to predict chronic wasting disease status based on animal movement.Movement ecology · 2026Article
- Fluconazole-induced liver injury in patients with pulmonary cryptococcosis: a comprehensive study integrating clinical cohort analysis, network toxicology, molecular docking, and transcriptomics.Antimicrobial agents and chemotherapy · 2026Article
- Development and validation of a machine learning stratified prediction model for early warning of anti-tuberculosis drug-induced liver injury risk based on real-world data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Analysis of High-Risk Factors for Tuberculosis Retreatment Based on Machine Learning and Latent Class Analysis.Infection and drug resistance · 2026Article
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.BMC infectious diseases · 2025Article
- Incidence and associated risk factors of anti-tuberculosis drug induced liver injury among TB patients.BMC infectious diseases · 2025Article
- Latent class analysis of inflammation and drug-induced liver injury phenotypes in older tuberculosis patients: associations with anxiety, depression, and insomnia.Frontiers in psychiatry · 2025Article
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6 authors at 2 institutions in 1 country.
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Abstract
backgroundThe objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment.
methodsA dataset of TB patients from Ningbo City was used to develop models employing the eXtreme Gradient Boosting (XGBoost), random forest (RF), and the least absolute shrinkage and selection operator (LASSO) logistic algorithms. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) alongside the decision curve. The Shapley Additive exPlanations (SHAP) method was used to interpret the variable contributions of the superior model.
resultsA total of 7,071 TB patients were identified from the regional healthcare dataset. The study cohort consisted of individuals with a median age of 47 years, 68.0% of whom were male, and 16.3% developed DILI. We utilized part of the high dimensional propensity score (HDPS) method to identify relevant variables and obtained a total of 424 variables. From these, 37 variables were selected for inclusion in a logistic model using LASSO. The dataset was then split into training and validation sets according to a 7:3 ratio. In the validation dataset, the XGBoost model displayed improved overall performance, with an AUROC of 0.89, an AUPR of 0.75, an F1 score of 0.57, and a Brier score of 0.07. Both SHAP analysis and XGBoost model highlighted the contribution of baseline liver-related ailments such as DILI, drug-induced hepatitis (DIH), and fatty liver disease (FLD). Age, alanine transaminase (ALT), and total bilirubin (Tbil) were also linked to DILI status.
conclusionXGBoost demonstrates improved predictive performance compared to RF and LASSO logistic in this study. Moreover, the introduction of the SHAP method enhances the clinical understanding and potential application of the model. For further research, external validation and more detailed feature integration are necessary.
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