Deep Survival Learning for Kidney Transplantation: Knowledge Distillation and Data Integration
Kevin He, Associate Professor of Biostatistics and Associate Director of the Kidney Epidemiology and Cost Center (KECC), University of Michigan
Abstract: 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 addressing these challenges, but their effectiveness often relies on large datasets. However, when applied to moderate- or small-sized datasets, deep models frequently encounter limitations such as insufficient training data, overfitting, and difficulty in hyperparameter optimization. To mitigate these issues and enhance prognostic performance, this talk presents a flexible deep learning framework that integrates external risk scores with internal time-to-event data through a generalized Kullback–Leibler divergence regularization term. Applied to the national kidney transplant data, the proposed method demonstrates improved prediction of short-term mortality and graft failure following kidney transplantation by distilling and transferring prior knowledge from pre-policy-change teacher models to newly arrived post-policy-change cohorts.
Cost: free