RIEMANNIAN METRIC LEARNING FOR PROGRESSION MODELING OF LONGITUDINAL DATASETS.

Explicit descriptions of the progression of biomarkers across time usually involve priors on the shapes of the trajectories. To circumvent this limitation, we propose a geometric framework to learn a manifold representation of longitudinal data. Namely, we introduce a family of Riemannian metrics that span a set of curves defined as parallel variations around a main geodesic, and apply that framework to disease progression modeling with a mixed-effects model, where the main geodesic represents the average progression of biomarkers and parallel curves describe the individual trajectories. Learning the metric from the data allows to fit the model to longitudinal datasets and provides few interpretable parameters that characterize both the group-average trajectory and individual progression profiles. Our method outperforms the 56 methods benchmarked in the TADPOLE challenge for cognitive scores prediction.

Automated summary

This study proposes a geometric framework for learning a manifold representation of longitudinal data to model disease progression of biomarkers. By learning the metric from the data, the method can fit longitudinal datasets well and provide a few interpretable parameters.