Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
This provides a more efficient and accurate prognostic framework for personalized healthcare applications, though it appears incremental as an improvement over existing joint modeling methods.
The paper tackles individualized event prediction by jointly modeling longitudinal and time-to-event data using a multivariate Gaussian convolution process (MGCP) with a Cox model, and introduces a variational inference framework that reduces computational complexity and prevents overfitting. Experiments on synthetic and real-world data show it outperforms state-of-the-art approaches based on two-stage inference and parametric assumptions.
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.