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Artificial intelligence for dementia-Applied models and digital health
Lyall DM, Kormilitzin A, Lancaster C, Sousa J, Petermann-Rocha F, Buckley C, Harshfield EL, Iveson MH et al.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2023-07-26
Topic: Risk, prevention and care in population studies
Introduction The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. Methods This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. Results This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. Discussion Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Lyall, D. M., Kormilitzin, A., Lancaster, C., Sousa, J., Petermann-Rocha, F., Buckley, C., et al. (2023). Artificial intelligence for dementia-Applied models and digital health. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.13391
Vancouver
Lyall DM, Kormilitzin A, Lancaster C, Sousa J, Petermann-Rocha F, Buckley C, et al. Artificial intelligence for dementia-Applied models and digital health. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023. doi:10.1002/alz.13391. PMID: 37496259.
BibTeX
@article{lyall2023-37496259,
author = {Lyall, D. M. and Kormilitzin, A. and Lancaster, C. and Sousa, J. and Petermann-Rocha, F. and Buckley, C. and Harshfield, E. L. and Iveson, M. H. and others},
title = {{Artificial intelligence for dementia-Applied models and digital health}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2023},
doi = {10.1002/alz.13391},
pmid = {37496259},
url = {https://doi.org/10.1002/alz.13391}
}
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Key findings
- Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.
- Results This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.
Why it matters
This narrative review reports that machine learning approaches show improved predictive power compared to standard clinical risk scores in predicting dementia. It discusses prediction and digital health tools, not treatments or cures for Alzheimer's disease.
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Study details
- Study designnarrative review
- Evidence strengthModerate
How this study scored 56/100
Published in a leading specialty 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 | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia : the journal of the Alzheimer's Association) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | Design recorded as “narrative review”, which our rubric does not rank (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 | 3 / 5 | 2023 — within 6 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 | 4 / 7 | Cited 68 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 13.6 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 12.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 14 other studies in this library |
| Still being cited | 3 / 4 | Cited 62 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Janice M. Ranson: h-index 26 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Broad Institute: institution h-index 2,541 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 16 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37496259) |
| Free to read | 2 / 2 | Free to read |
| Total | 56 / 100 | — |
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