A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
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.
The authors design and fit a hierarchical Bayesian model for predicting disease trajectories (i.e., a scalar measure of disease severity measured throughout the course of the disease) for individual patients. The overall model is an additive combination of a a number of terms including: (1) a population-level term, (2) a subpopulation term, (3) an individual term, (4) a GP term for structured errors. Each of these terms is a function of time, which is modeled parametrically in terms of the coefficients on pre-defined basis expansions (linear and/or B-splines). The subpopulation term involves a discrete mixture model, and the individual level term is a Bayesian linear regression. Distributions are chosen to be Gaussian, which makes most steps of inference and learning work out nicely. The discrete mixture leads to a GMM-like EM algorithm. The authors tune hyperparameters by hand, use likelihood-based learning (EM) for regression coefficients, and use posterior inference to predict the disease trajectory as it evolves. The qualitative results suggest that the method is effective at picking up the signal in lung-disease trajectories to make accurate predictions. The quantitative results demonstrate significant improvements over reasonable baseline. Quality:I find this to be a well done application paper. The model is not earth shattering, but not trivial either. It is clearly explained and appropriate to the problem at hand. The inference and learning methods are carefully derived. The experiments are designed well and demonstrate the value of the method. Note: for the qualitative results, please comment on the range of different results. Are the examples in Figure 2 typical, or were these chosen as particularly compelling examples? Clarity:See above. The paper is well written, and the methods are explained clearly. One small comment: the related work section was hard to understand without having first seeing some details of the proposed method---e.g., to understand why plain GPs would not do the job.Originality:To the best of my knowledge, the model structure proposed here is original. It reuses fairly standard components, so it is not highly original. Because most of the components of the model are Gaussian, the math generally works out nicely. But there is enough complexity in the model components (splines, discrete mixture model, Bayesian linear regression, GP) that the careful derivation of inference and learning updates represent a contribution. When that is all done, standard algorithms can solve the inference and learning problems. Significance:The primary contributions are in the application domain area (health modeling): the design of a new model, derivation of inference and learning algorithms, and demonstration that the method works on a real task. The model could potentially apply elsewhere, but there is not a convincing case made here of significance/relevance as a general-purpose model.