In this study, we aim to examine the efficacy of deep learning methods in predicting the 1-year risk of developing cirrhosis in patients with chronic hepatitis C (CHC), as defined by transient elastography (TE), in comparison with conventional models, as well as to assess whether semi-supervised learning can improve performance relative to supervised learning when the labels are limited. We used the electronic health records of the 169,317 valid patients in the Veterans Health Administration system from 2000 to 2016. Predictor variables contained baseline characteristics, such as age, gender, race, hepatitis C virus genotype, and 26 liver-related longitudinal variables such as sustained virologic response and laboratory data. The response variable, developing cirrhosis, is defined as liver stiffness >12.5 kPa on TE within a 1-year window. Using baseline and longitudinal variables, we fitted four prediction models, including logistic regression (LR), random forest (RF), supervised recurrent neural network (RNN), and semi-supervised RNN (semi-RNN) and evaluated their performances. Both RNN (area under the receiver operating characteristic curve [AuROC] 0.744) and semi-RNN (AuROC 0.785) accurately predicted the risk of cirrhosis within 1 year and significantly outperformed RF (AuROC 0.731) and LR (AuROC 0.724). By enabling early identification of high-risk patients, these models hold promise for targeted interventions in clinical CHC treatment.