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Dataset used in \An Interpretable Color-Coded Prognostics Using Deep Learning for Longitudinal Tracking of Alzheimer\u2019s Disease and Related Disorders\
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Bipul Simkhada相关数据集
Dataset for Inexpensive, Non-invasive Biomarkers Predict Alzheimer Transition using Machine Learning Analysis of the Alzheimer’s Disease Neuroimaging (ADNI) Database
Data supplementing the information found in "Inexpensive, Non-invasive Biomarkers Predict Alzheimer Transition using Machine Learning Analysis of the Alzheimer’s Disease Neuroimaging (ADNI) Database"
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Predicting Progression of Alzheimer’s Disease Using Ordinal Regression
We propose a novel approach to predicting disease progression in Alzheimer’s disease (AD) – multivariate ordinal regression – which inherently models the ordered nature of brain atrophy spanning norma
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Additional file 1 of The impact of subthreshold levels of amyloid deposition on conversion to dementia in patients with amyloid-negative amnestic mild cognitive impairment
Additional file 1: Table S1. The result of stepwise backward elimination. Figure S1. Flow chart for this study of ADNI dataset. The solid outline squares represent subjects that remained. The dash lin
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Table_1_Neuroimaging Biomarkers Predicting the Efficacy of Multimodal Rehabilitative Intervention in the Alzheimer’s Dementia Continuum Pathology.docx
In this work we aimed to identify neural predictors of the efficacy of multimodal rehabilitative interventions in AD-continuum patients in the attempt to identify ideal candidates to improve the treat
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