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51/100

✓ Peer-reviewed alzhub usefulness score 51 out of 100. Mild cognitive impairment

Topic: Risk, prevention and care in population studies

Alzheimer's disease (AD) remains a formidable challenge in modern healthcare, necessitating innovative approaches for its early detection and intervention. This study aimed to enhance the identification of individuals with mild cognitive impairment (MCI) at risk of developing AD. Leveraging advances in computational power and the extensive availability of healthcare data, we explored the potential of deep learning models for early prediction using medical claims data. We employed a bidirectional gated recurrent unit (BiGRU) deep learning model for predictive modeling of MCI progression across various prediction intervals, extending up to five years post-initial MCI diagnosis. The performance of the BiGRU model was rigorously compared with several machine-learning model baselines to evaluate its efficacy. Using a robust cross-validation methodology, the BiGRU emerged as the top-performing model, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.833 (95% CI: 0.822, 0.843), an Area Under the Precision-Recall Curve (AUC-PR) of 0.856 (95% CI: 0.845, 0.867), and an F1-Score of 0.71 (95% CI: 0.694, 0.724) for a five-year prediction interval. The results indicate that BiGRU, utilizing longitudinal claims data, reliably predicts MCI-to-AD progression over a lengthy interval following the initial MCI diagnosis, offering clinicians a valuable tool for targeted risk identification and stratification.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Abdelhameed, A. M., Feng, J., Hu, X., Li, F., Lundin, S. K., Schulz, P. E., et al. (2025). AI-powered model for accurate prediction of MCI-to-AD progression. Acta Pharmaceutica Sinica B. https://doi.org/10.1016/j.apsb.2025.01.027

Vancouver

Abdelhameed AM, Feng J, Hu X, Li F, Lundin SK, Schulz PE, et al. AI-powered model for accurate prediction of MCI-to-AD progression. Acta Pharmaceutica Sinica B. 2025. doi:10.1016/j.apsb.2025.01.027. PMID: 41049748.

BibTeX

@article{abdelhameed2025-41049748,
  author  = {Abdelhameed, Ahmed M. and Feng, Jingna and Hu, Xinyue and Li, Fang and Lundin, Sori Kim and Schulz, Paul Ernest and Tao, Cui},
  title   = {{AI-powered model for accurate prediction of MCI-to-AD progression}},
  journal = {Acta Pharmaceutica Sinica B},
  year    = {2025},
  doi     = {10.1016/j.apsb.2025.01.027},
  pmid    = {41049748},
  url     = {https://doi.org/10.1016/j.apsb.2025.01.027}
}

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.

Key findings

The performance of the BiGRU model was rigorously compared with several machine-learning model baselines to evaluate its efficacy.
The results indicate that BiGRU, utilizing longitudinal claims data, reliably predicts MCI-to-AD progression over a lengthy interval following the initial MCI diagnosis, offering clinicians a valuable tool for targeted risk identification and stratification.

Why it matters

A deep learning model using medical claims data predicted progression from mild cognitive impairment to Alzheimer's disease up to five years after diagnosis with an AUC-ROC of 0.833. This is a prediction tool for risk identification, not a treatment or cure.

Study details

Study designlongitudinal study
Evidence strengthModerate

How this study scored 51/100

Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomized / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Acta Pharmaceutica Sinica B)
Journal's citation record	2 / 4	Acta Pharmaceutica Sinica B: journal h-index 168 (OpenAlex, 2026-10-29)
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	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)
Randomized / blinded	0 / 2	No randomization or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	2 / 7	Cited 11 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	3.7 citations a year over 3 year(s) since 2025
Impact for its field and year	3 / 6	Cited 7.0× 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	1 / 4	Cited 11 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Cui Tao: h-index 40 (OpenAlex, whole career)
Institution's track record	2 / 3	Mayo Clinic: institution h-index 1,606 (OpenAlex)
Multicenter collaboration	1 / 3	OpenAlex lists 3 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41049748)
Free to read	2 / 2	Free to read
Total	51 / 100	—

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