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Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts
Dan Mungas, Brandon E. Gavett, Liliana Paloma Rojas‐Saunero, Yixuan Zhou, Eleanor Hayes‐Larson, Crystal L. Shaw, Sarah Tomaszewski Farias, Keith F. Widaman et al.. Alzheimer s & Dementia, 2025-08-01
Topic: Risk, prevention and care in population studies
Measures of cognition were especially important predictive features of clinical diagnosis. Prediction accuracy was higher in a sample that had longer follow-up, better balance across diagnostic outcomes, and both self- and informant-report independent function measures. Algorithmic diagnosis might be a meaningful substitute for clinical diagnosis in studies in which clinical evaluation and diagnosis are not feasible for all participants and may provide a standardized alternative when clinical diagnosis is available. HIGHLIGHTS: A machine learning algorithm was used to diagnose cognitive impairment and dementia. Measures of cognition were strongest predictive features for clinical diagnosis. Algorithm accuracy was improved by informant-report independent function measures. Algorithmic diagnosis might be an alternative if clinical diagnosis is not feasible. Standardization is an important advantage of algorithmic diagnosis.
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APA 7th
Mungas, D., Gavett, B. E., Rojas‐Saunero, L. P., Zhou, Y., Hayes‐Larson, E., Shaw, C. L., et al. (2025). Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts. Alzheimer s & Dementia. https://doi.org/10.1002/alz.70508
Vancouver
Mungas D, Gavett BE, Rojas‐Saunero LP, Zhou Y, Hayes‐Larson E, Shaw CL, et al. Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts. Alzheimer s & Dementia. 2025. doi:10.1002/alz.70508. PMID: 40747592.
BibTeX
@article{mungas2025-40747592,
author = {Mungas, Dan and Gavett, Brandon E. and Rojas‐Saunero, Liliana Paloma and Zhou, Yixuan and Hayes‐Larson, Eleanor and Shaw, Crystal L. and Farias, Sarah Tomaszewski and Widaman, Keith F. and others},
title = {{Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz.70508},
pmid = {40747592},
url = {https://doi.org/10.1002/alz.70508}
}
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Key findings
- Measures of cognition were especially important predictive features of clinical diagnosis.
- Prediction accuracy was higher in a sample that had longer follow-up, better balance across diagnostic outcomes, and both self- and informant-report independent function measures.
- Algorithmic diagnosis might be a meaningful substitute for clinical diagnosis in studies in which clinical evaluation and diagnosis are not feasible for all participants and may provide a standardized alternative when clinical diagnosis is available.
- HIGHLIGHTS: A machine learning algorithm was used to diagnose cognitive impairment and dementia.
- Measures of cognition were strongest predictive features for clinical diagnosis.
Why it matters
A machine learning algorithm diagnosed cognitive impairment and dementia in harmonized older adult cohorts, with cognition measures as the strongest predictors and better accuracy when informant-report function measures were included. Algorithmic diagnosis is proposed as a possible substitute or standardized alternative to clinical diagnosis, not as a treatment, cure, or prevention.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 48/100
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 (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (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 | 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 | 2 / 6 | Cited 1.3× 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 | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Elizabeth Rose Mayeda: h-index 43 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, Los Angeles: institution h-index 1,923 (OpenAlex) |
| Multicenter collaboration | 3 / 3 | OpenAlex lists 10 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40747592) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
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