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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
Defining and validating a short form Montreal Cognitive Assessment (s-MoCA) for use in neurodegenerative disease
David R Roalf1, Tyler M Moore1, David A Wolk2,3
1Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Introduction:
Screening for cognitive deficits is essential in neurodegenerative disease. Screening tests, such as the Montreal Cognitive Assessment (MoCA), are easily administered, correlate with neuropsychological performance and demonstrate diagnostic utility. Yet, administration time is too long for many clinical settings.
Methods:
Item response theory and computerised adaptive testing simulation were employed to establish an abbreviated MoCA in 1850 well-characterised community-dwelling individuals with and without neurodegenerative disease.
Results:
8 MoCA items with high item discrimination and appropriate difficulty were identified for use in a short form (s-MoCA). The s-MoCA was highly correlated with the original MoCA, showed robust diagnostic classification and cross-validation procedures substantiated these items.
Discussion:
Early detection of cognitive impairment is an important clinical and public health concern, but administration of screening measures is limited by time constraints in demanding clinical settings. Here, we provide as-MoCA that is valid across neurological disorders and can be administered in approximately 5 min.
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