Background: Chronic hepatitis C (HCV) infection presents global health challenges with
significant morbidity and mortality implications. Successfully treating patients with cirrhosis …

Existing predictive models of risk of disease progression in chronic hepatitis C have limited
accuracy. The aim of this study was to improve upon existing models by applying novel …

Accuracy of risk assessments for clinical outcomes in patients with chronic liver disease has
been limited given the nonlinear nature of disease progression. Longitudinal prediction …

Background Machine learning (ML) algorithms provide effective ways to build prediction
models using longitudinal information given their capacity to incorporate numerous predictor …

Background. Although several direct-acting antivirals (DAAs) are now available, the therapy
regimen for chronic hepatitis C will continue to include pegylated interferon and ribavirin for …

Despite the availability of efficacious direct-acting antiviral (DAA) therapy, the number of
people infected with hepatitis C virus (HCV) continues to rise, and HCV remains a leading …

The objective of this work is to accurately predict the severity of the Hepatitis C virus using
various Machine Learning (ML) algorithms. This study is developed using thirteen different …

Abstract Background and Aim This study developed liver outcome scores in chronic hepatitis
C (CHC) that directly predict liver‐related death, hepatocellular carcinoma (HCC), and liver …

Machine learning algorithms for predicting direct‐acting ant... : Hepatology Machine learning
algorithms for predicting direct‐acting antiviral treatment failure in chronic hepatitis C: An …

Importance Machine-learning algorithms offer better predictive accuracy than traditional
prognostic models but are too complex and opaque for clinical use. Objective To compare …