… the patients to recover from the effects of hepatitis c disease. … hepatitis c patients who are
being treated with LOLA therapy. … machine learning techniques: Support Vector Machine (SVM) …

… a quicker and more efficient technique for disease diagnosis, leading to timely patient treatment.
Our method analyzed 4962 patients with chronic hepatitis C from fifteen different centers …

… a prediction framework based on ML approaches to predict … are more suited to small datasets
than deep learning approaches, … We investigate the use of deep learning methods on huge …

… prediction accuracy of a deep learning model based on RNNs for predicting progression to
HCC in a cohort of patients … to compare the performance of a deep learning RNN model with …

… Machine learning classifiers are used in this work to predict … and patients who have been
diagnosed with hepatitis C. Other … the use of genetic analysis and machine-learning techniques …

… prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning
techniques… In this study, several machine learning techniques (Classification and regression tree, …

… Patients: Our retrospective study included 427 Egyptian patients with chronic hepatitis C
who were naïve candidates for an antiviral therapy. A local ethical committee approval was …

… programs requires improved individual risk prediction. Thus, we aimed to … algorithms based
on machine learning approaches to predict the risk of HCC more accurately in patients with …

… pre-procedurally, provided that planned treatment characteristics are specified. The … use
MR imaging and clinical patient data to create an AI framework for the prediction of therapeutic …

… and machine learning, which intends to predict the … machine learning algorithms to help
physicians to explore unknown patterns, to predict the progression of HCV-related liver disease …