Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome
characterized by cognitive impairment severe enough to interfere with activities of daily life …
characterized by cognitive impairment severe enough to interfere with activities of daily life …
Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review
We performed a systematic review of studies focusing on the automatic prediction of the
progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a …
progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a …
[HTML][HTML] Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users
Abstract Background In the United States, 13% of adults are estimated to have alcohol use
disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this …
disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this …
Transmodal learning of functional networks for Alzheimer's disease prediction
Functional connectivity describes neural activity from resting-state functional magnetic
resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomark-er …
resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomark-er …
[PDF][PDF] A list of publications describing new supervised learning pipelines to predict clinical variables from neuroimaging data in Alzheimer's disease
AF Mendelson - 2016 - pfigshare-u-files.s3.amazonaws.com
This document contains a list of 272 journal publications and 198 conference publications
describing new supervised learning pipelines to predict the level of AD pathology in a …
describing new supervised learning pipelines to predict the level of AD pathology in a …