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Machine learning models to predict onset of dementia: A label learning approach
Nori VS, Hane CA, Crown WH, Au R, Burke WJ, Sanghavi DM, Bleicher P.. Alzheimer's & dementia (New York, N. Y.), 2019-12-10
Topic: Risk, prevention and care in population studies
Introduction The study objective was to build a machine learning model to predict incident mild cognitive impairment, Alzheimer's Disease, and related dementias from structured data using administrative and electronic health record sources. Methods A cohort of patients (n = 121,907) and controls (n = 5,307,045) was created for modeling using data within 2 years of patient's incident diagnosis date. Additional cohorts 3-8 years removed from index data are used for prediction. Training cohorts were matched on age, gender, index year, and utilization, and fit with a gradient boosting machine, lightGBM. Results Incident 2-year model quality on a held-out test set had a sensitivity of 47% and area-under-the-curve of 87%. In the 3-year model, the learned labels achieved 24% (71%), which dropped to 15% (72%) in year 8. Discussion The ability of the model to discriminate incident cases of dementia implies that it can be a worthwhile tool to screen patients for trial recruitment and patient management.
Key findings
- Results Incident 2-year model quality on a held-out test set had a sensitivity of 47% and area-under-the-curve of 87%.
Why it matters
A machine learning model using administrative and electronic health record data predicted incident mild cognitive impairment, Alzheimer's disease and related dementias, with 47% sensitivity and an area-under-the-curve of 87% at two years. This is a screening and prediction tool, not a treatment, cure or prevention.
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Study details
- Participants121,907
- Evidence strengthModerate
How this study scored 53/100
Published in a leading specialty journal. Scored zero for study design, effect reported 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 (New York, N. Y.)) |
| 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 | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 4 / 4 | 121,907 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 | 4 / 7 | Cited 87 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 9.7 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 4.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 39 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Paul A. Bleicher: h-index 18 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Boston University: institution h-index 1,456 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31879701) |
| Free to read | 2 / 2 | Free to read |
| Total | 53 / 100 | — |
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Read the original
- DOI: 10.1016/j.trci.2019.10.006
- PubMed: 31879701
- Original source →
- Free full text: Alzheimer s & Dementia Translational Research & Clinical Interventions →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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.
APA 7th
Nori, V. S., Hane, C. A., Crown, W. H., Au, R., Burke, W. J., Sanghavi, D. M., et al. (2019). Machine learning models to predict onset of dementia: A label learning approach. Alzheimer's & dementia (New York, N. Y.). https://doi.org/10.1016/j.trci.2019.10.006
Vancouver
Nori VS, Hane CA, Crown WH, Au R, Burke WJ, Sanghavi DM, et al. Machine learning models to predict onset of dementia: A label learning approach. Alzheimer's & dementia (New York, N. Y.). 2019. doi:10.1016/j.trci.2019.10.006. PMID: 31879701.
BibTeX
@article{nori2019-31879701,
author = {Nori, V. S. and Hane, C. A. and Crown, W. H. and Au, R. and Burke, W. J. and Sanghavi, D. M. and Bleicher, P.},
title = {{Machine learning models to predict onset of dementia: A label learning approach}},
journal = {Alzheimer's \& dementia (New York, N. Y.)},
year = {2019},
doi = {10.1016/j.trci.2019.10.006},
pmid = {31879701},
url = {https://doi.org/10.1016/j.trci.2019.10.006}
}
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