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Evaluating Patient Representation Strategies for Early Prediction of Alzheimer’s Disease and Related Dementias from Longitudinal EHRs
Farzana S, Arian A, Moïse Desvarieux3, Tatjana Rundek4 Rundek, Ahsan H.. Research Square, 2026-10-01
Topic: Brain imaging, connectivity and assessment
The neurodegenerative processes underlying Alzheimer’s disease and related dementias (ADRD) begin years before clinical diagnosis, motivating scalable approaches for earlier identification. Electronic health records (EHRs) provide longitudinal clinical information for early ADRD prediction but are sparse, temporal, and hierarchical, and alternative EHR representations encode these characteristics differently. We systematically evaluated how EHR representation and temporal distance from diagnosis influence ADRD prediction and whether representation-specific performance generalizes across health systems. Using All of Us EHRs, we compared interpretable count-based representations with pretrained clinical foundation-model representations at clinically meaningful lead times across multiple ADRD case definitions. Count-based models achieved the strongest overall discrimination and calibration. Performance generally declined with increasing prediction horizons, while differences between representations narrowed; at 36 months, pretrained representations achieved comparable AUROC (0.719 versus 0.738) with higher sensitivity and F1 at a fixed operating threshold. Temporal feature attribution showed greater model reliance on healthcare-utilization indicators at longer horizons and increasing importance of cognitive phenotypes, including amnesia and mild cognitive impairment, nearer diagnosis. Zero-shot evaluation on independent UChicago Medicine EHRs showed substantial performance degradation across representations, demonstrating challenges in cross-institutional transportability. These findings characterize representation-dependent tradeoffs in early ADRD prediction across temporal horizons and healthcare systems.
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APA 7th
Farzana, S., Arian, A., Desvarieux3, M., Rundek, T. R., & Ahsan, H. (2026). Evaluating Patient Representation Strategies for Early Prediction of Alzheimer’s Disease and Related Dementias from Longitudinal EHRs. Research Square. https://doi.org/10.21203/rs.3.rs-10988118/v1
Vancouver
Farzana S, Arian A, Desvarieux3 M, Rundek TR, Ahsan H. Evaluating Patient Representation Strategies for Early Prediction of Alzheimer’s Disease and Related Dementias from Longitudinal EHRs. Research Square. 2026. doi:10.21203/rs.3.rs-10988118/v1.
BibTeX
@article{farzana2026-758053b6,
author = {Farzana, S. and Arian, A. and Desvarieux3, Moïse and Rundek, Tatjana Rundek4 and Ahsan, H.},
title = {{Evaluating Patient Representation Strategies for Early Prediction of Alzheimer’s Disease and Related Dementias from Longitudinal EHRs}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-10988118/v1},
url = {https://doi.org/10.21203/rs.3.rs-10988118/v1}
}
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Key findings
- Count-based models achieved the strongest overall discrimination and calibration.
- Performance generally declined with increasing prediction horizons, while differences between representations narrowed; at 36 months, pretrained representations achieved comparable AUROC (0.719 versus 0.738) with higher sensitivity and F1 at a fixed operating threshold.
- These findings characterize representation-dependent tradeoffs in early ADRD prediction across temporal horizons and healthcare systems.
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 (Research Square) |
| 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. |
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