  The event-based model (EBM) for data-driven disease progression modeling
estimates the sequence in which biomarkers for a disease become abnormal. This
helps in understanding the dynamics of disease progression and facilitates
early diagnosis by staging patients on a disease progression timeline. Existing
EBM methods are all generative in nature. In this work we propose a novel
discriminative approach to EBM, which is shown to be more accurate as well as
computationally more efficient than existing state-of-the art EBM methods. The
method first estimates for each subject an approximate ordering of events, by
ranking the posterior probabilities of individual biomarkers being abnormal.
Subsequently, the central ordering over all subjects is estimated by fitting a
generalized Mallows model to these approximate subject-specific orderings based
on a novel probabilistic Kendall's Tau distance. To evaluate the accuracy, we
performed extensive experiments on synthetic data simulating the progression of
Alzheimer's disease. Subsequently, the method was applied to the Alzheimer's
Disease Neuroimaging Initiative (ADNI) data to estimate the central event
ordering in the dataset. The experiments benchmark the accuracy of the new
model under various conditions and compare it with existing state-of-the-art
EBM methods. The results indicate that discriminative EBM could be a simple and
elegant approach to disease progression modeling.

Computer Vision and Pattern Recognition;Quantitative Methods