Machine learning to predict dementia for American Indian and Alaska native peoples: a retrospective cohort study

Topic: Risk, prevention and care in population studies

The study cohort included 17,398 AI/AN adults aged ≥ 65 years who were dementia-free at baseline, of whom 59.8% were female. Over the two-year follow-up, 611 individuals (3.5%) were diagnosed with incident dementia. Extended models for logistic regression, LASSO, and XGBoost performed comparably: AUCs (95% CI) of 0.83 (0.79, 0.86), 0.83 (0.79, 0.86), and 0.82 (0.79, 0.86). These top-performing models shared 12 of the 15 highest-ranked predictors, with novel predictors including service utilization. Machine learning algorithms utilizing EHR data can effectively predict two-year dementia risk among AI/AN older adults. These models could aid IHS and Tribal health clinicians in identifying high-risk individuals, facilitating timely interventions and improved care coordination. Funding: NIH.

Key findings

• The study cohort included 17,398 AI/AN adults aged ≥ 65 years who were dementia-free at baseline, of whom 59.8% were female.
• Over the two-year follow-up, 611 individuals (3.5%) were diagnosed with incident dementia.
• Extended models for logistic regression, LASSO, and XGBoost performed comparably: AUCs (95% CI) of 0.83 (0.79, 0.86), 0.83 (0.79, 0.86), and 0.82 (0.79, 0.86).
• These top-performing models shared 12 of the 15 highest-ranked predictors, with novel predictors including service utilization.
• Machine learning algorithms utilizing EHR data can effectively predict two-year dementia risk among AI/AN older adults.

Why it matters

Machine learning models using electronic health record data predicted which American Indian and Alaska Native older adults would develop dementia within two years with similar accuracy across three model types. These tools could help clinicians identify high-risk individuals for timely care, but the study did not test whether acting on these predictions prevents dementia.

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Study details

• Participants17,398
• Effect size59.8%; 3.5%
• Evidence strengthModerate

Read the original

• DOI: 10.1016/j.lana.2025.101013
• PubMed: 40034839
• Original source →
• Free full text: The Lancet Regional Health - Americas →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{ports2025-40034839,
  author  = {Ports, Kayleen Deanna and Dai, Jiahui and Conniff, Kyle R and Corrada, María M. and Manson, Spero M. and O’Connell, Joan M. and Jiang, Luohua},
  title   = {{Machine learning to predict dementia for American Indian and Alaska native peoples: a retrospective cohort study}},
  journal = {The Lancet Regional Health - Americas},
  year    = {2025},
  doi     = {10.1016/j.lana.2025.101013},
  pmid    = {40034839},
  url     = {https://doi.org/10.1016/j.lana.2025.101013}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• External validation of four dementia prediction models for use in the general community-dwelling population: a comparative analysis from the Rotterdam Study European journal of epidemiology · 2018-05-08 · usefulness score 67/100 · Long-term cohort studies
• Dementia prevention, intervention, and care: 2020 report of the Lancet Commission The Lancet · 2020-07-30 · usefulness score 67/100 · Prevention and risk
• Association of body mass index with risk of cognitive impairment and dementia: A systematic review and meta-analysis of prospective studies Neuroscience and biobehavioral reviews · 2020-05-30 · usefulness score 65/100 · Prevention and risk
• Prevalence of dementia subtypes in United States Medicare fee-for-service beneficiaries, 2011-2013 Alzheimer's & dementia : the journal of the Alzheimer's Association · 2016-05-10 · usefulness score 59/100 · Getting a diagnosis
• Machine learning models to predict onset of dementia: A label learning approach Alzheimer's & dementia (New York, N. Y.) · 2019-12-10 · usefulness score 53/100 · Digital health tools
• Statistical methods for dementia risk prediction and recommendations for future work: A systematic review Alzheimer's & dementia (New York, N. Y.) · 2019-10-08 · usefulness score 44/100 · Digital health tools
• Estimates of diagnosed dementia prevalence and incidence among diverse beneficiaries in Traditional Medicare and Medicare Advantage Alzheimer s & Dementia · 2023-12-01 · usefulness score 36/100 · Health systems and care delivery

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