论文标题
DEEPPAMM：在生存分析中，深层分段指数添加混合模型，用于复杂危害结构
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
论文作者
论文摘要
生存分析（SA）是一个积极的研究领域，与事件时间结果有关，并且在许多领域（尤其是生物医学应用）中很普遍。尽管它很重要，但由于小规模的数据集和复杂的结果分布，SA仍然具有挑战性，并被截断和审查过程所隐藏。分段指数添加混合模型（PAMM）是一种解决这些挑战中许多挑战的模型类，但是PAMM不适用于高维功能设置或非结构化或多模式数据的情况下。我们通过提出Deeppamm来统一现有方法，Deeppams是一个多功能的深度学习框架，从统计的角度有充分的基础，但具有足够的灵活性来对复杂的危害结构进行建模。我们说明，Deeppamm在预测性能方面与其他机器学习方法具有竞争力，同时通过基准实验和扩展的案例研究保持了可解释性。
Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications. Despite its importance, SA remains challenging due to small-scale data sets and complex outcome distributions, concealed by truncation and censoring processes. The piecewise exponential additive mixed model (PAMM) is a model class addressing many of these challenges, yet PAMMs are not applicable in high-dimensional feature settings or in the case of unstructured or multimodal data. We unify existing approaches by proposing DeepPAMM, a versatile deep learning framework that is well-founded from a statistical point of view, yet with enough flexibility for modeling complex hazard structures. We illustrate that DeepPAMM is competitive with other machine learning approaches with respect to predictive performance while maintaining interpretability through benchmark experiments and an extended case study.
