使用非参数专家混合模型对生存数据进行聚类
Clustering Survival Data using a Mixture of Non-parametric Experts
AI总结：
SurvMixClust将聚类与生存函数预测统一于非参数专家混合框架，在五个公共数据集上生成平衡且生存曲线不同的聚类，优于聚类基线，预测精度媲美非聚类生存模型。
摘要：
生存分析旨在预测各种领域中未来事件的发生时间，从医疗结果到客户流失。然而，将聚类整合到生存分析中，特别是在精准医学领域，仍未得到充分探索。本研究引入了SurvMixClust，一种新颖的生存分析算法，它在统一框架内将聚类与生存函数预测相结合。SurvMixClust学习用于聚类的潜在表示，同时使用非参数专家混合模型预测个体生存函数。我们在五个公共数据集上的评估表明，SurvMixClust能够创建具有不同生存曲线的平衡聚类，优于聚类基线，并且在预测准确性方面与非聚类生存模型相当，这通过时间依赖性c指数和对数秩指标来衡量。
英文摘要：
Survival analysis aims to predict the timing of future events across various fields, from medical outcomes to customer churn. However, the integration of clustering into survival analysis, particularly for precision medicine, remains underexplored. This study introduces SurvMixClust, a novel algorithm for survival analysis that integrates clustering with survival function prediction within a unified framework. SurvMixClust learns latent representations for clustering while also predicting individual survival functions using a mixture of non-parametric experts. Our evaluations on five public datasets show that SurvMixClust creates balanced clusters with distinct survival curves, outperforms clustering baselines, and competes with non-clustering survival models in predictive accuracy, as measured by the time-dependent c-index and log-rank metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
