A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure

TL;DR

This paper introduces a hierarchical latent variable model that personalizes disease trajectory predictions by leveraging multi-resolution data, improving accuracy in forecasting interstitial lung disease progression.

Contribution

The novel hierarchical model captures population, subpopulation, and individual variations, enabling dynamic and personalized disease trajectory predictions.

Findings

Significant improvement over state-of-the-art in predictive accuracy.

Effective online learning of individual-specific disease trajectories.

Validated on interstitial lung disease with promising results.

Abstract

For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an individual's disease trajectory as a continuous-valued continuous-time function describing the severity of the disease over time. We propose a hierarchical latent variable model that individualizes predictions of disease trajectories. This model shares statistical strength across observations at different resolutions--the population, subpopulation and the individual level. We describe an algorithm for learning population and subpopulation parameters offline, and an online procedure for dynamically learning individual-specific parameters. Finally, we validate our model on the task of predicting the course of interstitial lung disease, a leading cause of death among patients with the autoimmune disease scleroderma. We compare our approach against state-of-the-art and demonstrate significant improvements in predictive accuracy.