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40/100

✓ Peer-reviewed alzhub usefulness score 40 out of 100. Getting a diagnosis

Topic: Amyloid, tau and blood-test diagnosis

score 0.8323, although only the AUC was statistically significantly different) and other AI-based models. Several of the AI-based models, including convolutional neural networks, also outperformed the CCW algorithm. These findings highlight the potential of AI-based text-classification methods to optimize the automated identification of patients with ADRD using rich EHR data. However, the success of this approach depends on the quality of clinical notes, and more work is needed to refine and validate these methods across more diverse data sets. Highlights: The current e-phenotype for patients with Alzheimer's disease and related dementias (ADRD) in electronic health records has suboptimal diagnostic accuracy.The study used artificial intelligence (AI)-based text classification methods to improve the detection of patients with ADRD.AI-based models, including convolutional neural networks, outperformed the Chronic Conditions Warehouse algorithm.

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

APA 7th

Knox, S., Aghamoosa, S., Heider, P. M., Cutty, M. E., Wright, A. W. E., Scherbakov, D., et al. (2025). AI approaches for phenotyping Alzheimer's disease and related dementias using electronic health records. Alzheimer s & Dementia Translational Research & Clinical Interventions. https://doi.org/10.1002/trc2.70089

Vancouver

Knox S, Aghamoosa S, Heider PM, Cutty ME, Wright AWE, Scherbakov D, et al. AI approaches for phenotyping Alzheimer's disease and related dementias using electronic health records. Alzheimer s & Dementia Translational Research & Clinical Interventions. 2025. doi:10.1002/trc2.70089. PMID: 40291122.

BibTeX

@article{knox2025-40291122,
  author  = {Knox, Sara and Aghamoosa, Stephanie and Heider, Paul M. and Cutty, Maxwell E. and Wright, Andrew W. E. and Scherbakov, Dmitry and Hood, Gavin and Nolin, Sara A. and others},
  title   = {{AI approaches for phenotyping Alzheimer's disease and related dementias using electronic health records}},
  journal = {Alzheimer s \& Dementia Translational Research \& Clinical Interventions},
  year    = {2025},
  doi     = {10.1002/trc2.70089},
  pmid    = {40291122},
  url     = {https://doi.org/10.1002/trc2.70089}
}

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Key findings

score 0.8323, although only the AUC was statistically significantly different) and other AI-based models.
Several of the AI-based models, including convolutional neural networks, also outperformed the CCW algorithm.
These findings highlight the potential of AI-based text-classification methods to optimize the automated identification of patients with ADRD using rich EHR data.
However, the success of this approach depends on the quality of clinical notes, and more work is needed to refine and validate these methods across more diverse data sets.
Highlights: The current e-phenotype for patients with Alzheimer's disease and related dementias (ADRD) in electronic health records has suboptimal diagnostic accuracy.The study used artificial intelligence (AI)-based text classification methods to improve the detection of patients with ADRD.AI-based models, including convolutional neural networks, outperformed the Chronic Conditions Warehouse algorithm.

Why it matters

Artificial intelligence-based text classification methods outperformed a standard algorithm for identifying patients with Alzheimer's disease and related dementias from electronic health records. This is about improving automated identification and does not address treating, curing, or preventing the disease.

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Study details

Participants1,000
Evidence strengthModerate

How this study scored 40/100

Published in a peer-reviewed journal. Scored zero for study design, effect reported 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 Translational Research & Clinical Interventions)
Journal's citation record	1 / 4	Alzheimer s & Dementia Translational Research & Clinical Interventions: journal h-index 84 (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	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	3 / 4	1,000 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	1 / 7	Cited 2 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.7 citations a year over 3 year(s) since 2025
Impact for its field and year	1 / 6	Cited 0.9× 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	0 / 4	Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Jihad S. Obeid: h-index 32 (OpenAlex, whole career)
Institution's track record	1 / 3	Medical University of South Carolina: institution h-index 715 (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 40291122)
Free to read	2 / 2	Free to read
Total	40 / 100	—

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