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：神经网络（深度学习）是人工智能中的一种现代模型，已被应用于生存分析领域。尽管已有研究展现出若干改进成果，但训练优质的深度学习模型通常需要海量数据，这在实践中往往难以满足。针对这一挑战，我们提出了一种基于库尔贝克-莱布勒散度（KL散度）的深度学习流程，用于将外部生存预测模型与新收集的事件发生时间数据相融合。采用时变KL判别信息来度量外部数据与内部数据之间的差异。这是首个在生存分析深度学习中利用先验信息处理短数据问题的研究工作。仿真实验与真实数据结果均表明，与先前研究相比，所提模型在性能与鲁棒性方面均表现更优。