Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study

Yue Xiao; Yanfei Chen; Ruijian Huang; Feng Jiang; Jifang Zhou; Tianchi Yang

doi:10.1186/s12874-024-02214-5

Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study

BMC Med Res Methodol. 2024 Apr 20;24(1):92. doi: 10.1186/s12874-024-02214-5.

Authors

Yue Xiao¹, Yanfei Chen¹, Ruijian Huang¹, Feng Jiang¹, Jifang Zhou^#², Tianchi Yang^#³

Affiliations

¹ School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, China.
² School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, China. 1020202613@cpu.edu.cn.
³ Institute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, No.237, Yongfeng Road, Ningbo, Zhejiang, China. cn-yangtc@outlook.com.

^# Contributed equally.

Abstract

Background: The objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment.

Methods: A 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.

Results: A 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.

Conclusion: XGBoost 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.

Keywords: Drug-induced liver injury; Logistic regression; Machine learning; Retrospective study; Tuberculosis.

MeSH terms

Algorithms*
Area Under Curve
Benchmarking
Chemical and Drug Induced Liver Injury* / diagnosis
Chemical and Drug Induced Liver Injury* / etiology
Female
Humans
Machine Learning
Male
Middle Aged

Abstract

MeSH terms

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