Identification of profiles associated with conversions between the Alzheimer's disease stages, using a machine learning approach.
Journal:
Alzheimer's research & therapy
PMID:
39061107
Abstract
BACKGROUND: The identification of factors involved in the conversion across the different Alzheimer's disease (AD) stages is crucial to prevent or slow the disease progression. We aimed to assess the factors and their combination associated with the conversion across the AD stages, from mild cognitive impairment to dementia, at a mild, moderate or severe stage and to identify profiles associated with earliest/latest conversion across the AD stages.