Home › Research › Digital health tools
Forecasting Alzheimer’s disease progression using non-imaging clinical features and XGBoost
Gautier A, Zheng L, Schreurs BG.. medRxiv, 2026-10-07
Topic: Brain imaging, connectivity and assessment
Machine learning (ML) methods have shown promise in modeling clinical trajectories associated with neurodegenerative disorders such as Alzheimer’s disease (AD). These findings suggest that longitudinal machine learning models can identify future cognitive decline before diagnosis, indicating clinical utility for early dementia risk stratification. We designed our study to advance scalable, low-cost diagnostic alternatives that can identify individuals at significant risk of cognitive decline, ensuring faster access to emerging therapies.
Key findings
- Machine learning (ML) methods have shown promise in modeling clinical trajectories associated with neurodegenerative disorders such as Alzheimer’s disease (AD).
- These findings suggest that longitudinal machine learning models can identify future cognitive decline before diagnosis, indicating clinical utility for early dementia risk stratification.
- We designed our study to advance scalable, low-cost diagnostic alternatives that can identify individuals at significant risk of cognitive decline, ensuring faster access to emerging therapies.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designlongitudinal study
- Participants15,727
- Effect size29.58%; 28.29%; 30.87%
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 50 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for 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 31 of the 62 points we could judge it on, which is 50 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| 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 | 4 / 4 | 15,727 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| 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 | 31 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 31 of the 62 points we could judge it on is 50 out of 100. |
| Total | 50 / 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 →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
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. Check the citation against the original before you use it.
APA 7th
Gautier, A., Zheng, L., & Schreurs, B. G. (2026). Forecasting Alzheimer’s disease progression using non-imaging clinical features and XGBoost. medRxiv. https://doi.org/10.64898/2026.10.05.26364229
Vancouver
Gautier A, Zheng L, Schreurs BG. Forecasting Alzheimer’s disease progression using non-imaging clinical features and XGBoost. medRxiv. 2026. doi:10.64898/2026.10.05.26364229.
BibTeX
@article{gautier2026-b033970d,
author = {Gautier, A. and Zheng, L. and Schreurs, B. G.},
title = {{Forecasting Alzheimer’s disease progression using non-imaging clinical features and XGBoost}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.10.05.26364229},
url = {https://doi.org/10.64898/2026.10.05.26364229}
}