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.

翻译：深入学习网络(深入学习)是人工智能的现代模型,在生存分析中已经加以利用。虽然以前的工作显示了若干改进,但培训一个极好的深层学习模型需要大量数据,而这些数据实际上可能无法维持。为了应对这一挑战,我们开发了一个基于Kullback-Lebel(KL)的深层学习程序,将外部生存预测模型与新收集的时间-事件数据相结合。根据时间/时间/时间的KL差异信息被用来衡量外部和内部数据之间的差异。这是考虑使用以前的信息来处理生存分析中短期数据问题以进行深层学习的首次工作。模拟和真实数据结果显示,拟议的模型与以前的工作相比,取得了更好的业绩和更高的强健性。