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FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling
Javid S, Nir TM, Zhu AH, Bhatt RR, Aksman LM, Jahanshad N, Alzheimer’s Disease Neuroimaging Initiative.. bioRxiv, 2026-07-22
Topic: Genetics and APOE
Event-based models (EBMs) are used to infer ordering of biomarker alteration patterns with respect to disease progression. However, EBM approaches rely on computationally expensive permutation-based inference, assumptions of feature independence, and likelihood optimization that can limit scalability and stability in high-dimensional settings. Here, we introduce Fast Event-Based Model (FastEBM), a scalable, uncertainty aware, Markov- chain-based framework that reformulates disease progression inference as a subject-ordering problem on a data-driven diffusion manifold. The progression uncertainty, used to derive positional variance diagrams, is quantified using first-passage-time variability derived directly from the inferred Markov process. Using synthetic experiments varying feature dimensionality, cohort size, noise level, and feature-correlation structure, we compared FastEBM with established methods, including Gaussian mixture model EBM (GMM-EBM), kernel density estimation EBM (KDE-EBM), and discriminative EBM (DEBM). FastEBM achieved the best accuracy and runtime. In low-subject/high-dimensional stress tests, FastEBM retained event-order recovery. FastEBM remained robust in simulations containing correlated and redundant features after decorrelation and feature-group handling. We applied FastEBM to real-world data to characterize biomarker progression in Alzheimer’s disease. First, we evaluated a low-dimensional multi- modal dataset from The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TAD- POLE) challenge. Second, to demonstrate high-dimensional disease progression mapping, we applied FastEBM to regional cortical tau-PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In both cases, FastEBM recovered progression patterns broadly consistent with the literature, also revealing lateralized progression trends. These results show that diffusion-based Markov geometry provides a scalable and robust alternative to conventional event-based modeling. FastEBM is available at: https://github.com/sjusc07/FastEBM .
Key findings
- These results show that diffusion-based Markov geometry provides a scalable and robust alternative to conventional event-based modeling.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 35 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 22 of the 62 points we could judge it on, which is 35 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 22 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 22 of the 62 points we could judge it on is 35 out of 100. |
| Total | 35 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Javid, S., Nir, T. M., Zhu, A. H., Bhatt, R. R., Aksman, L. M., Jahanshad, N., et al. (2026). FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling. bioRxiv. https://doi.org/10.64898/2026.07.18.739221
Vancouver
Javid S, Nir TM, Zhu AH, Bhatt RR, Aksman LM, Jahanshad N, et al. FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling. bioRxiv. 2026. doi:10.64898/2026.07.18.739221.
BibTeX
@article{javid2026-9d8b8fd7,
author = {Javid, S. and Nir, T. M. and Zhu, A. H. and Bhatt, R. R. and Aksman, L. M. and Jahanshad, N. and {Alzheimer’s Disease Neuroimaging Initiative}},
title = {{FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.07.18.739221},
url = {https://doi.org/10.64898/2026.07.18.739221}
}
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