Joint Hierarchical Gaussian Process Model with Application to Forecast in Medical Monitoring

TL;DR

This paper introduces a hierarchical Gaussian process model for accurate longitudinal forecasting and joint modeling of continuous and survival data, demonstrated with cystic fibrosis medical monitoring data.

Contribution

It presents a novel Bayesian hierarchical Gaussian process approach that combines nonlinear population trends with individual memory for improved forecasting and joint modeling of medical data.

Findings

Robust latent estimation and correlation detection in simulations

High accuracy in forecasting lung function and respiratory events

Effective joint modeling of continuous and survival data in medical monitoring

Abstract

A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint modeling of continuous measurements and survival events. The baseline hazard, covariate and joint effects are conveniently modeled in this hierarchical structure. The estimation and inference are implemented in fully Bayesian framework using the objective and shrinkage priors. In simulation studies, this model shows robustness in latent estimation, correlation detection and high accuracy in forecasting. The model is illustrated with medical monitoring data from cystic fibrosis (CF) patients. Estimation and forecasts are obtained in the measurement of lung function and records of acute respiratory events. Keyword: Extrapolation, Joint Model, Longitudinal Model, Hierarchical Gaussian Process, Cystic Fibrosis, Medical Monitoring