Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance.

翻译：常规生存分析方法通常对人口的多样性特征没有效果,而这种信息可用于协助预测模型。在本研究中,我们建议采用混合生存分析方法,称为深集生存机,结合有区别和基因化的机制。与混合模型一样,我们假设生存数据的时机信息由某些参数分布(即专家分布)的混合组合来描述。我们根据个别案例的特点来了解专家分布的权重,有区别地说,每个案例的生存信息都可以以有学识的常态专家分布的加权组合为特征。这种方法还便于根据相关专家分布对所有案例进行可解释的分组/分组。关于真实和合成数据集的广泛实验表明,该方法能够取得有希望的集群结果和有竞争力的时间到活动预测性。</s>