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For many complex diseases, there is a wide variety of ways in which anindividual can manifest the disease. The challenge of personalized medicine isto develop tools that can accurately predict the trajectory of an individual'sdisease, which can in turn enable clinicians to optimize treatments. Werepresent an individual's disease trajectory as a continuous-valuedcontinuous-time function describing the severity of the disease over time. Wepropose a hierarchical latent variable model that individualizes predictions ofdisease trajectories. This model shares statistical strength acrossobservations at different resolutions--the population, subpopulation and theindividual level. We describe an algorithm for learning population andsubpopulation parameters offline, and an online procedure for dynamicallylearning individual-specific parameters. Finally, we validate our model on thetask of predicting the course of interstitial lung disease, a leading cause ofdeath among patients with the autoimmune disease scleroderma. We compare ourapproach against state-of-the-art and demonstrate significant improvements inpredictive accuracy.

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