State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Centre, Guangzhou 510060, China.
Deepaint Intelligence Technology Co., Ltd., Guangzhou, 510080, China.
Artif Intell Med. 2019 Jul;98:1-9. doi: 10.1016/j.artmed.2019.06.001. Epub 2019 Jun 6.
Survival analyses of populations and the establishment of prognoses for individual patients are important activities in the practice of medicine. Standard survival models, such as the Cox proportional hazards model, require extensive feature engineering or prior knowledge to model at an individual level. Some survival analysis models can avoid these problems by using machine learning extended the CPH model, and higher performance has been reported. In this paper, we propose an innovative loss function that is defined as the sum of an extended mean squared error loss and a pairwise ranking loss based on ranking information on survival data. We apply this loss function to optimize a deep feed-forward neural network (RankDeepSurv), which can be used to model survival data. We demonstrate that the performance of our model, RankDeepSurv, is superior to that of other state-of-the-art survival models based on an analysis of 4 public medical clinical datasets. When modelling the prognosis of nasopharyngeal carcinoma (NPC), RankDeepSurv achieved better prognostic accuracy than the CPH established by clinical experts. The difference between high and low risk groups in the RankDeepSurv model is greater than the difference in the CPH. The results show that our method has considerable potential to model survival data in medical settings.
人群的生存分析和个体患者预后的建立是医学实践中的重要活动。标准生存模型，如 Cox 比例风险模型，需要广泛的特征工程或先验知识才能进行个体水平的建模。一些生存分析模型可以通过使用机器学习扩展 CPH 模型来避免这些问题，并且已经报告了更高的性能。在本文中，我们提出了一种创新的损失函数，该函数定义为扩展均方误差损失和基于生存数据排序信息的成对排序损失的总和。我们将该损失函数应用于优化深度前馈神经网络（RankDeepSurv），该神经网络可用于对生存数据进行建模。我们证明，基于对 4 个公共医学临床数据集的分析，我们的模型 RankDeepSurv 的性能优于其他最先进的生存模型。在对鼻咽癌（NPC）的预后建模时，RankDeepSurv 比临床专家建立的 CPH 具有更好的预后准确性。RankDeepSurv 模型中高低风险组之间的差异大于 CPH 的差异。结果表明，我们的方法在医学环境中对生存数据进行建模具有相当大的潜力。
