A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
This work addresses personalized medicine by enabling more accurate predictions of disease trajectories for clinicians, though it appears incremental as it builds on existing hierarchical modeling approaches.
The authors tackled the problem of predicting individual disease trajectories by proposing a hierarchical latent variable model that shares statistical strength across population, subpopulation, and individual levels, and validated it on interstitial lung disease data, showing significant improvements in predictive accuracy compared to state-of-the-art methods.
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.