A large US study using VHA EHRs suggests Alzheimer disease risk can be predicted years before diagnosis by analyzing routine clinical notes.
Using machine learning and keyword-based analysis, investigators found that early cognitive and noncognitive symptoms documented during standard care visits preceded formal Alzheimer diagnoses by more than a decade.
Investigators analyzed longitudinal EHR data from 61 537 veterans diagnosed with Alzheimer disease and 234 105 matched controls without dementia between 2000 and 2022.
The study focused on unstructured clinical notes from primary care, emergency, mental health, geriatrics, neurology, and other settings.
Using random forest models, keyword-based predictors achieved an area under the receiver operating characteristic curve (AUROC) of 0.577 at 10 years before diagnosis and 0.861 a day before diagnosis.
For clinicians, the findings highlight the potential value of routine clinical documentation as an early warning system for Alzheimer disease.
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