A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling

TL;DR

This paper introduces a novel discriminative event-based model for Alzheimer's disease progression that improves accuracy and efficiency over existing generative EBMs by estimating individual event orderings and deriving a central progression sequence.

Contribution

The paper proposes a discriminative approach to EBM using probabilistic Kendall's Tau distance, enhancing accuracy and computational efficiency over prior generative methods.

Findings

Discriminative EBM outperforms existing methods in synthetic data experiments.

The model accurately estimates disease progression order in ADNI data.

The approach is computationally more efficient than traditional EBMs.

Abstract

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.