  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.
