Bloodwork-free Early Screening for Alzheimer’s Disease via Comorbid Pattern Recognition in Electronic Health Records

Early identification of Alzheimer’s disease and related dementias (ADRD) remains limited by the need for specializedtests and late-stage diagnosis. The Zero-burden Risk Assessment (ZeBRA) is a AI-driven score that predictsincident ADRD up to a decade before diagnosis, using only routine electronic health record (EHR) data, withoutlaboratory tests, imaging, or questionnaires. Trained on 487,989 cases and 12,483,718 controls from nationwideU.S. insurance claims and validated on held-back samples , and two independent cohorts, ZeBRA achievedAUC = 0.93 and 0.83 for predicting out to 1-year and 10-year horizons respectively, maintaining positive likelihoodratios (>10) at 95% specificity and stable discrimination over time (AUC drop ≈ 1 to 1.3% per year). Performancewas consistent across age, sex, race, and ethnicity subgroups. In a limited prospective pilot, higher ZeBRA scorescorrelated with lower Montreal Cognitive Assessment (MoCA) scores, indicating a greater degree of cognitiveimpairment (R = −0.78). Compared with prior EHR-based models, ZeBRA provides superior accuracy, cross-site generalizability, and demonstrates noise-corrected interpretability via our novel Λ-OR attrubution metric. Its scalability, low cost, and independence from specialized testing position ZeBRA as a practical tool for population-level early detection and presymptomatic trial enrichment.