The primary goal of a time-to-event estimation model is to accurately infer the occurrence time of a target event. Most existing studies focus on developing new models to effectively utilize the information in the censored observations. In this paper, we propose a model to tackle the time-to-event estimation problem from a completely different perspective. Our model relaxes a fundamental constraint that the target variable, time, is a univariate number which satisfies a partial order. Instead, the proposed model interprets each event occurrence time as a time concept with a vector representation. We hypothesize that the model will be more accurate and interpretable by capturing (1) the relationships between features and time concept vectors and (2) the relationships among time concept vectors. We also propose a scalable framework to simultaneously learn the model parameters and time concept vectors. Rigorous experiments and analysis have been conducted in medical event prediction task on seven gene expression datasets. The results demonstrate the efficiency and effectiveness of the proposed model. Furthermore, similarity information among time concept vectors helped in identifying time regimes, thus leading to a potential knowledge discovery related to the human cancer considered in our experiments.

事件发生时间估计模型的主要目标是准确推断目标事件的发生时间。现有的大多数研究都集中在开发新模型上，以有效利用审查后的观察结果中的信息。在本文中，我们提出了一个模型，用于从完全不同的角度解决事件到时间的估计问题。我们的模型放宽了一个基本约束，即目标变量时间是一个满足偏序的单变量数。而是，提出的模型将每个事件发生时间解释为具有矢量表示的时间概念。我们假设通过捕获（1）特征和时间概念向量之间的关系以及（2）时间概念向量之间的关系，该模型将更加准确和可解释。我们还提出了一个可扩展的框架，以同时学习模型参数和时间概念向量。在医学事件预测任务中，对七个基因表达数据集进行了严格的实验和分析。结果证明了该模型的有效性和有效性。此外，时间概念向量之间的相似性信息有助于确定时间方案，从而导致我们实验中考虑的与人类癌症有关的潜在知识发现。