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

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 …

Objective Assessing risk of adverse outcomes among patients with chronic liver disease has
been challenging due to non-linear disease progression. We previously developed accurate …

BACKGROUND & AIMS: Assessment of the risk of hepatocellular carcinoma (HCC)
development is essential for formulating personalized surveillance or antiviral treatment plan …

Importance Deep learning, a family of machine learning models that use artificial neural
networks, has achieved great success at predicting outcomes in nonmedical domains …

Background & Aims Accurate hepatocellular carcinoma (HCC) risk prediction facilitates
appropriate surveillance strategy and reduces cancer mortality. We aimed to derive and …

Background Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases
such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was …

Background & Aims Refining hepatocellular carcinoma (HCC) surveillance programs
requires improved individual risk prediction. Thus, we aimed to develop algorithms based on …

Background This study uses machine learning techniques to identify sociodemographic and
clinical predictors of progression through the hepatitis C (HCV) cascade of care for patients …

Background Most existing predictive models of hepatocellular carcinoma (HCC) risk after
sustained virologic response (SVR) are built on data collected at baseline and therefore …