Abstract

We present a nonparametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions. Technical details are available in the supplementary materials.

摘要

我们提出了一个基于时间序列和时间到事件数据的联合建模的个性化事件预测的非参数预测框架。我们的方法利用多元高斯卷积过程 (MGCP) 对时间序列信号的演变进行建模，并利用 Cox 模型将时间到事件数据与通过 MGCP 建模的时间序列数据进行映射。利用卷积过程施加的独特结构，我们提供了一个变分推理框架来同时估计联合 MGCP-Cox 模型中的参数。这显着降低了计算复杂度并防止模型过度拟合。对合成和现实世界数据的实验表明，所提出的框架优于基于两阶段推理和强参数假设的最先进方法。