KL-divergence Based Deep Learning for Discrete Time Model
This addresses the data scarcity issue in survival analysis for deep learning, though it is incremental as it builds on prior work by incorporating external information.
The authors tackled the problem of training deep learning models for survival analysis with limited data by developing a KL-divergence-based method that integrates external survival prediction models with new time-to-event data, achieving better performance and higher robustness in simulations and real data.
Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previous works, training an excellent deep learning model requires a huge amount of data, which may not hold in practice. To address this challenge, we develop a Kullback-Leibler-based (KL) deep learning procedure to integrate external survival prediction models with newly collected time-to-event data. Time-dependent KL discrimination information is utilized to measure the discrepancy between the external and internal data. This is the first work considering using prior information to deal with short data problem in Survival Analysis for deep learning. Simulation and real data results show that the proposed model achieves better performance and higher robustness compared with previous works.