Flexible Deep Survival Learning for Kidney Transplantation: Knowledge Distillation and Data Integration

Prognostic prediction using survival analysis faces challenges due to complex relationships between risk factors and time-to-event outcomes. Deep learning methods have shown promise in overcoming these challenges, but their effectiveness often relies on large datasets. In contrast, when implemented on moderate or small data sets, these methods often suffer from severe problems, such as insufficient training data, overfitting, and difficulty in tuning hyperparameters. To address these issues and improve prognosis predictions, this talk introduces a flexible deep learning framework for integrating external risk models with internal time-to-event data using a generalized Kullback-Leibler divergence penalty. Applied to the Scientific Registry of Transplant Recipients (SRTR), the method improves prediction of short-term mortality and graft failure after kidney transplant. These gains enable transplant-specific applications such as donor risk reclassification and early post-transplant triage, supporting more reliable, data-driven decision making across the kidney transplant pathway.