# Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling

arXiv: https://arxiv.org/abs/1808.03604 · HTML: https://mlsanity.com/paper/1808.03604/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review](https://mlsanity.com/paper/2201.02539/) (2022)
- [Data-Driven Disease Progression Modelling](https://mlsanity.com/paper/2211.05786/) (2022)
- [Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data](https://mlsanity.com/paper/2602.22018/) (2026)
- [Computer-aided diagnosis and prediction in brain disorders](https://mlsanity.com/paper/2206.14683/) (2022)
- [FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning](https://mlsanity.com/paper/2405.20504/) (2024)

## Abstract (arXiv)

Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can help in understanding the dynamics of disease progression and facilitate early diagnosis and prognosis. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate than existing state-of-the-art EBM methods. The method first estimates for each subject an approximate ordering of events. Subsequently, the central ordering over all subjects is estimated by fitting a generalized Mallows model to these approximate subject-specific orderings. We also introduce the concept of relative distance between events which helps in creating a disease progression timeline. Subsequently, we propose a method to stage subjects by placing them on the estimated disease progression timeline. We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods. We also performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease. The event orderings obtained on ADNI data seem plausible and are in agreement with the current understanding of progression of AD. The proposed patient staging algorithm performed consistently better than that of state-of-the-art EBM methods. Event orderings obtained in simulation experiments were more accurate than those of other EBM methods and the estimated disease progression timeline was observed to correlate with the timeline of actual disease progression. The results of these experiments are encouraging and suggest that discriminative EBM is a promising approach to disease progression modeling.