Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study.

Tao Zhong Zian Zhuang Xiao-Li Dong Ka Hing Wong Wing Tak Wong Jian Wang Daihai He Shengyuan Liu

Published in: JMIR medical informatics (2021)

Our model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.

Keyphrases

drug induced
liver injury
machine learning
end stage renal disease
ejection fraction
newly diagnosed
chronic kidney disease
adverse drug
healthcare
prognostic factors
peritoneal dialysis
palliative care
emergency department
health information
social media
patient reported
electronic health record