Predicting Disease Progression with a Model for Multivariate Longitudinal Clinical Data

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Joseph Futoma, Mark Sendak, Blake Cameron, Katherine Heller ;
Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:42-54, 2016.

Abstract

Accurate prediction of the future trajectory of a disease is an important challenge in 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 that may help improve prediction of future disease state. To this end, we propose a novel probabilistic generative model for multivariate longitudinal data that captures dependencies between multivariate trajectories of clinical variables. 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 develop a scalable variational inference algorithm that we use to fit our model to a large dataset of longitudinal electronic patient health records. Our model’s dynamic predictions have significantly lower errors compared to a recent state of the art method for modeling disease trajectories, and they are being incorporated into a population health rounding tool to be used by clinicians at our local accountable care organization.

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