Advancing dementia identification using machine learning and real‐world sequential health data

Topic: Risk, prevention and care in population studies

The best-performing model achieved an area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.87), with sensitivity of 73.3% (95% CI: 72.3-74.2) and specificity of 87.5% (95% CI: 86.7-88.2). Key predictors included time-stamped International Classification of Diseases 10th Revision diagnostic codes, health-care use, referral to aged residential care, and hospital delirium assessments. A unique feature was the inclusion of "timestamp data" that allowed us to assess the longitudinal changes which may have improved performance of the model. These findings demonstrate the potential for using machine learning with routine health data to enhance early dementia detection. Highlights: Machine learning models using routine health data in a real-world setting in New Zealand had good accuracy for identification of dementia (area under the curve 0.86, sensitivity 73.3%, and specificity 87.5%).Longitudinal sequential "timestamp" data is a unique feature that improved the performance of machine learning models for dementia case finding.Key predictors included International Classification of Diseases 10th Revision codes, health-care use, referrals to aged residential care, and positive delirium scores.

Key findings

• The best-performing model achieved an area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.87), with sensitivity of 73.3% (95% CI: 72.3-74.2) and specificity of 87.5% (95% CI: 86.7-88.2).
• Key predictors included time-stamped International Classification of Diseases 10th Revision diagnostic codes, health-care use, referral to aged residential care, and hospital delirium assessments.
• A unique feature was the inclusion of "timestamp data" that allowed us to assess the longitudinal changes which may have improved performance of the model.
• These findings demonstrate the potential for using machine learning with routine health data to enhance early dementia detection.
• Highlights: Machine learning models using routine health data in a real-world setting in New Zealand had good accuracy for identification of dementia (area under the curve 0.86, sensitivity 73.3%, and specificity 87.5%).Longitudinal sequential "timestamp" data is a unique feature that improved the performance of machine learning models for dementia case finding.Key predictors included International Classification of Diseases 10th Revision codes, health-care use, referrals to aged residential care, and positive delirium scores.

Why it matters

A machine learning model using routine health records identified dementia with an area under the curve of 0.86, sensitivity of 73.3% and specificity of 87.5%. This is about improving early detection, not treating, curing or preventing the disease.

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Study details

• Study designcase-control study
• Participants8,195
• Effect size73.3%; 87.5%
• Evidence strengthModerate

Read the original

• DOI: 10.1002/dad2.70332
• PubMed: 41958925
• Original source →
• Free full text: Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring →

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

@article{gonzalezprieto2026-41958925,
  author  = {González-Prieto, Cristian and Yelanchezian, Y Mukish M and Dobbie, Gillian and Wilson, Daniel P. and Rodriguez, Claudia Rivera and Oulaghan, Bede and Yates, Susan and Cullum, Sarah J.},
  title   = {{Advancing dementia identification using machine learning and real‐world sequential health data}},
  journal = {Alzheimer s \& Dementia Diagnosis Assessment \& Disease Monitoring},
  year    = {2026},
  doi     = {10.1002/dad2.70332},
  pmid    = {41958925},
  url     = {https://doi.org/10.1002/dad2.70332}
}

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