论文标题
半结构化深度分段指数模型
Semi-Structured Deep Piecewise Exponential Models
论文作者
论文摘要
我们提出了一个用于生存分析的多功能框架，将统计数据中的先进概念与深度学习结合在一起。提出的框架基于分段指数模型，从而支持各种生存任务，例如竞争风险和多州建模，并进一步允许估算时间变化的效果和时变特征。为了在模型中包括多个数据源和高阶交互作用，我们将模型类嵌入到神经网络中，从而可以同时估算固有可解释的结构化回归输入以及深度神经网络组件，以及可能处理其他非结构化数据源。通过使用框架来预测基于表格和3D点云数据的阿尔茨海默氏病进展并将其应用于合成数据，提供了概念证明。
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.
