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Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer’s disease using routine clinical and MRI data
Abstract Early identification of patients with Alzheimer’s disease (AD) who will experience near-term cognitive decline can support trial enrichment and risk-stratified follow-up. Using the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we developed two prognostic models for 12-month Mini-Mental State Examination (MMSE) decrease (≥ 3 points): (i) a clinical logistic-regression model and (ii) a random-forest model combining clinical variables with MRI-derived volumetric measures.
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