We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential models and thereby supports various survival tasks, such as competing risks and multi-state modeling, and further allows for estimation of time-varying effects and time-varying features. To also include multiple data sources and higher-order interaction effects into the model, we embed the model class in a neural network and thereby enable the simultaneous estimation of both inherently interpretable structured regression inputs as well as deep neural network components which can potentially process additional unstructured data sources. A proof of concept is provided by using the framework to predict Alzheimer's disease progression based on tabular and 3D point cloud data and applying it to synthetic data.
翻译:我们提出一个多功能的生存分析框架,将来自统计的先进概念与深层学习结合起来,提出框架的依据是小片指数模型,从而支持各种生存任务,例如相互竞争的风险和多状态模型,并进一步允许估计时间变化效应和时间变化特征。为了将多种数据源和更高层次的相互作用效应纳入模型,我们将模型类嵌入神经网络,从而能够同时估计内在可解释的结构回归输入以及深神经网络组成部分,从而有可能处理额外的非结构化数据源。通过使用这一框架,根据表格和三维点云数据预测阿尔茨海默氏病的演变,并将其应用于合成数据,可以提供概念证据。