ObjectiveTo introduce and validate the censoring unbiased deep learning (CUDL) algorithm suitable for survival data and to explore its utility in survival analysis. MethodsThe principles of the CUDL algorithm were detailed, followed by an introduction to its software implementation steps and parameter settings. Using the construction of a long-term survival prognosis model for patients with myocardial infarction (MI) as a case study, the algorithms were compared with the conventional Cox proportional hazards model and random survival forest model. ResultsIn the task of predicting the 5-year survival probability of patients, the mean Brier score (BS) of both the Buckley-James censoring unbiased deep learning (BJDL) model and the regularized BJDL were superior to that of the Cox proportional hazards model (mean BS: 0.1722 and 0.1715 vs. 0.1765, P<0.001). The regularized BJDL also outperformed the random survival forest model (RSF500) (mean BS: 0.1715 vs. 0.1750, P<0.001). ConclusionThe BJDL can effectively leverage censored information within survival data to achieve accurate predictions without the need to satisfy the proportional hazards assumption. It provides a novel methodological alternative for the analysis of medical survival data.