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Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias
Sonia Akter, Zhandi Liu, Eduardo Jardim Simões, Praveen R. Rao. The Journal of Prevention of Alzheimer s Disease, 2025-04-16
Topic: Amyloid, tau and blood-test diagnosis
The GBT model achieved the best AUC-ROC scores of 0.809-0.833 across 1- to 5-year prediction windows. SHAP analysis identified depressive disorder, age groups 80-90 yrs and 70-80 yrs, heart disease, anxiety, and the novel risk factors of sleep apnea, and headache. This study underscores the potential of ML models for leveraging EHR data to enable early ADRD prediction, supporting timely interventions, and improving patient outcomes. By identifying both established and novel risk factors, these findings offer new opportunities for personalized screening and management strategies, advancing both clinical and informatics science.
Key findings
- The GBT model achieved the best AUC-ROC scores of 0.809-0.833 across 1- to 5-year prediction windows.
- SHAP analysis identified depressive disorder, age groups 80-90 yrs and 70-80 yrs, heart disease, anxiety, and the novel risk factors of sleep apnea, and headache.
- This study underscores the potential of ML models for leveraging EHR data to enable early ADRD prediction, supporting timely interventions, and improving patient outcomes.
- By identifying both established and novel risk factors, these findings offer new opportunities for personalized screening and management strategies, advancing both clinical and informatics science.
Why it matters
A machine learning model using electronic health record data predicted Alzheimer's disease and related dementias up to five years in advance with moderate accuracy. It identified both known risk factors like depression and heart disease, and new ones like sleep apnea and headache, which could help personalize screening.
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Study details
- Study designcase-control study
- Participants380,269
- Evidence strengthModerate
How this study scored 56/100
Published in a peer-reviewed journal, and it is a case-control study. Scored zero for effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (The Journal of Prevention of Alzheimer s Disease) |
| Journal's citation record | 0 / 4 | The Journal of Prevention of Alzheimer s Disease: journal h-index 63 (OpenAlex, 2026-10-29) |
| Study design | 6 / 12 | A case-control study |
| Strength of evidence | 3 / 5 | Rated “moderate” 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 | 4 / 4 | 380,269 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 3 / 7 | Cited 29 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 9.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 12.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 29 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Praveen R. Rao: h-index 16 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Missouri: institution h-index 780 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40246680) |
| Free to read | 2 / 2 | Free to read |
| Total | 56 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.tjpad.2025.100169
- PubMed: 40246680
- Original source →
- Free full text: The Journal of Prevention of Alzheimer s Disease →
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
Akter, S., Liu, Z., Simões, E. J., & Rao, P. R. (2025). Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias. The Journal of Prevention of Alzheimer s Disease. https://doi.org/10.1016/j.tjpad.2025.100169
Vancouver
Akter S, Liu Z, Simões EJ, Rao PR. Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias. The Journal of Prevention of Alzheimer s Disease. 2025. doi:10.1016/j.tjpad.2025.100169. PMID: 40246680.
BibTeX
@article{akter2025-40246680,
author = {Akter, Sonia and Liu, Zhandi and Simões, Eduardo Jardim and Rao, Praveen R.},
title = {{Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias}},
journal = {The Journal of Prevention of Alzheimer s Disease},
year = {2025},
doi = {10.1016/j.tjpad.2025.100169},
pmid = {40246680},
url = {https://doi.org/10.1016/j.tjpad.2025.100169}
}
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