论文标题
生存集群分析
Survival Cluster Analysis
论文作者
论文摘要
常规生存分析方法估计以协变量为条件的风险评分或个性化的事件分布。在实践中，通常是由于（未知的）亚群具有不同风险概况或生存分布的（未知）亚群而产生的，通常存在良好的人口水平的表型异质性。结果，生存分析对于识别具有不同风险概况的亚种群的生存分析有未满足的需求，同时共同考虑了准确的个性化的活动时间预测。一种解决这种需求的方法可能是通过利用亚种群中的规律来改善个人结果的表征，从而考虑了人口水平的异质性。在本文中，我们提出了一种贝叶斯非参数方法，该方法代表聚类的潜在空间中的观察（受试者），并鼓励具有不同风险概况的准确的活动预测和群集（亚群）。相对于现有的最新生存分析模型，现实世界数据集的实验显示出预测性能和可解释性的一致性。
Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions. An approach that addresses this need is likely to improve characterization of individual outcomes by leveraging regularities in subpopulations, thus accounting for population-level heterogeneity. In this paper, we propose a Bayesian nonparametrics approach that represents observations (subjects) in a clustered latent space, and encourages accurate time-to-event predictions and clusters (subpopulations) with distinct risk profiles. Experiments on real-world datasets show consistent improvements in predictive performance and interpretability relative to existing state-of-the-art survival analysis models.
