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Updated: Jul 8, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predicting rapid clinical progression in amnestic mild cognitive impairment
Samrah Ahmed1, Joanna Mitchell, Robert Arnold
1Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK.
Background/Aims:
We investigated whether an initial neuropsychological assessment could predict rapid progression over 12 months, from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD).
Methods:
A longitudinal study compared the neuropsychological profiles of 27 normal controls and 18 aMCI patients at baseline and 12 months.
Results:
At 12 months, 24 control subjects followed up remained cognitively normal. 7 aMCI patients (6 multiple-domain aMCI and 1 single-domain aMCI) progressed to AD, and 11 were non-progressors. Prognosis was best captured by a combination of associative learning, the paired associate learning task (PAL), and global cognition, the Addenbrooke's Cognitive Examination (ACE).
Conclusion:
The PAL and ACE can sensitively detect meaningful differences in scores at baseline and may be used as prognostic indicators. Multiple-domain aMCI patients progressed rapidly to AD and may be more usefully labelled as early stage AD.
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