Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Mar 30, 2021
Date Accepted: May 16, 2021
Predicting Antituberculosis Drug-Induced Liver Injury Using Interpretable Machine Learning Method: Model Development and Validation Study
ABSTRACT
Background:
Tuberculosis is a global pandemic, being one of the top ten causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of tuberculosis patients.
Objective:
We aim to predict the status of liver injury in patients with TB at clinical treatment stage.
Methods:
We designed an interpretable prediction model based on XGBoost algorithm and identified the most robust and meaningful predictors to predict the risk of TB-DILI.
Results:
We found that the patients’ most recent ALT test value, average change rate of patients’ last two ALT test values, cumulative dose of PZA and cumulative dose of EMB to be the best predictors for assessing the DILI risk. In the validation dataset, the model has a precision of 90%, a recall of 74%, a classification accuracy of 76% and a balanced error rate of 77% in predicting the TB-DILI cases. In addition, the model provided high risk warnings for patients in advance of the drug-induced liver injury onset for a median of 29 days (IQR, (15, 45.75)).
Conclusions:
The model shows the high accuracy and interpretability in predicting the TB-DILI cases, which can provide useful information to Clinicians to adjust the medication regimen and avoid more serious liver injury in patients.
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