Joint Models for Event Prediction From Time Series and Survival Data
Joint Models for Event Prediction From Time Series and Survival Data
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DOI：
10.1080/00401706.2020.1832582
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发表时间：
2019-03
期刊：
影响因子：
2.5
通讯作者：
Xubo Yue;R. Kontar
中科院分区：
文献类型：
--
作者：
Xubo Yue;R. Kontar
摘要提出了一种基于时间序列和时间到事件数据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）来模拟时间序列信号的演变，并利用Cox模型将时间到事件的数据映射到通过MGCP建模的时间序列数据。利用卷积过程的独特结构，我们提供了一个变分推理框架来同时估计MGCP-Cox联合模型中的参数。这大大降低了计算复杂度，防止了模型过拟合。在合成和真实世界数据上的实验表明，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。技术细节见补充资料。
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.