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Federated learning with multi‐cohort real‐world data for predicting the progression from mild cognitive impairment to Alzheimer's disease
Jinqian Pan, Zhengkang Fan, Glenn E. Smith, Yi Maggie Guo, Jiang Bian, Jie Xu. Alzheimer s & Dementia, 2025-04-01
Topic: Amyloid, tau and blood-test diagnosis
Of 44,899 MCI patients, 6391 progressed to AD. FL models achieved a 6% improvement in AUC compared to local models. Key predictive features included body mass index, vitamin B12, blood pressure, and others. FL showed promise in predicting AD progression by integrating heterogeneous data across multiple institutions while preserving privacy. Despite limitations, it offers potential for future clinical applications. HIGHLIGHTS: We applied long short-term memory and federated learning (FL) to predict mild cognitive impairment to Alzheimer's disease progression using electronic health record data from multiple institutions. FL improved prediction performance, with a 6% increase in area under the receiver operating characteristic curve compared to local models. We identified key predictive features, such as body mass index, vitamin B12, and blood pressure. FL shows effectiveness in handling data heterogeneity across multiple sites while ensuring data privacy. Personalized and pooled FL models generally performed better than global and local models.
Key findings
- Of 44,899 MCI patients, 6391 progressed to AD.
- FL models achieved a 6% improvement in AUC compared to local models.
- Key predictive features included body mass index, vitamin B12, blood pressure, and others.
- FL showed promise in predicting AD progression by integrating heterogeneous data across multiple institutions while preserving privacy.
- Despite limitations, it offers potential for future clinical applications.
Why it matters
Using electronic health records from multiple institutions, federated learning improved prediction of progression from mild cognitive impairment to Alzheimer's disease by 6% in AUC compared to local models. This is a prediction tool, not a treatment, cure or prevention.
Study details
- Evidence strengthLow
How this study scored 42/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 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 | 3 / 7 | Cited 18 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 7.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 18 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Jie Xu: h-index 22 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Florida: institution h-index 1,303 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40219846) |
| Free to read | 2 / 2 | Free to read |
| Total | 42 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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
Pan, J., Fan, Z., Smith, G. E., Guo, Y. M., Bian, J., & Xu, J. (2025). Federated learning with multi‐cohort real‐world data for predicting the progression from mild cognitive impairment to Alzheimer's disease. Alzheimer s & Dementia. https://doi.org/10.1002/alz.70128
Vancouver
Pan J, Fan Z, Smith GE, Guo YM, Bian J, Xu J. Federated learning with multi‐cohort real‐world data for predicting the progression from mild cognitive impairment to Alzheimer's disease. Alzheimer s & Dementia. 2025. doi:10.1002/alz.70128. PMID: 40219846.
BibTeX
@article{pan2025-40219846,
author = {Pan, Jinqian and Fan, Zhengkang and Smith, Glenn E. and Guo, Yi Maggie and Bian, Jiang and Xu, Jie},
title = {{Federated learning with multi‐cohort real‐world data for predicting the progression from mild cognitive impairment to Alzheimer's disease}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz.70128},
pmid = {40219846},
url = {https://doi.org/10.1002/alz.70128}
}
Studies this one cites
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