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：传统生存分析方法通常难以有效表征人群中的异质性，而此类信息可用于辅助预测建模。本研究提出一种混合生存分析方法——深度聚类生存机，该方法融合了判别机制与生成机制。与混合模型类似，我们假设生存数据的时序信息由若干参数化分布（即专家分布）的混合模型生成性地描述。通过判别性学习每个实例对应专家分布的权重，使得每个实例的生存信息可由学习所得的恒定专家分布的加权组合来表征。该方法还可根据实例关联的专家分布，实现所有实例的可解释性子群划分/聚类。在真实数据集与合成数据集上的大量实验表明，该方法能够获得具有前景的聚类结果与具有竞争力的时间至事件预测性能。