Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression

TL;DR

This paper introduces a scalable probabilistic model for multivariate longitudinal health data, improving disease progression predictions for chronic kidney disease using electronic health records.

Contribution

It presents a novel Gaussian process-based generative model that captures dependencies among multiple biomarkers and employs scalable variational inference for large datasets.

Findings

Improved dynamic prediction accuracy over existing methods

Effective modeling of multivariate dependencies in longitudinal data

Application to real-world electronic health records for CKD

Abstract

Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management. However, many complex chronic diseases exhibit large degrees of heterogeneity, and furthermore there is not always a single readily available biomarker to quantify disease severity. Even when such a clinical variable exists, there are often additional related biomarkers routinely measured for patients that may better inform the predictions of their future disease state. To this end, we propose a novel probabilistic generative model for multivariate longitudinal data that captures dependencies between multivariate trajectories. We use a Gaussian process based regression model for each individual trajectory, and build off ideas from latent class models to induce dependence between their mean functions. We fit our method using a scalable variational inference algorithm to a large dataset of longitudinal electronic patient health records, and find that it improves dynamic predictions compared to a recent state of the art method. Our local accountable care organization then uses the model predictions during chart reviews of high risk patients with chronic kidney disease.