Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
Abstract
We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of longitudinal signals and a Cox model to map time-to-event data with longitudinal 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.
Cite
@article{arxiv.1903.03867,
title = {Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes},
author = {Xubo Yue and Raed Kontar},
journal= {arXiv preprint arXiv:1903.03867},
year = {2023}
}