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Updated: Mar 22, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease
Genevera I Allen1, Nicola Amoroso2, Catalina Anghel3
1Department of Statistics and Electrical and Computer Engineering, Rice University, Houston, TX, USA.
Abstract:
Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.
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