Interpretable Machine Learning in Predicting Drug-Induced Liver Injury among Tuberculosis Patients: Model Development and Validation Study
Yue Xiao,
Yanfei Chen,
Ruijian Huang
et al.
Abstract:Background: This study aimed to develop and validate an interpretable prediction model for Drug-Induced Liver Injury during tuberculosis treatment.
Methods: Using a dataset of TB patients from Ningbo City, the models were developed using eXtreme Gradient Boosting, random forest, and logistic regression algorithms. Features were selected using the Least Absolute Shrinkage and Selection Operator method. The model's performance was assessed through various metrics, including receiver operating characteristic and … Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.