Machine learning algorithms for predicting direct-acting antiviral treatment failure in chronic hepatitis C: An HCV-TARGET analysis.

ABSTRACT:

Background and aims

We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with HCV infection.

Approach and results

We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pretreatment predictors (n = 179) included sociodemographic, clinical characteristics, and virologic data. We applied multivariable logistic regression as well as elastic net, random forest, gradient boosting machine (GBM), and feedforward neural network machine learning algorithms to predict DAA treatment failure. Training (n = 4894) and validation (n = 1631) patient samples had similar sociodemographic and clinical characteristics (mean age, 57

SUBMITTER: Park H 

PROVIDER: S-EPMC9287493 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature