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Updated: Jun 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Including persistency of impairment in mild cognitive impairment classification enhances prediction of 5-year decline
Susan Vandermorris1, David F Hultsch, Michael A Hunter
1Department of Psychology, University of Victoria, Victoria, British Columbia, Canada. susan.vanderhill@aya.yale.edu
Abstract:
Although older adults with Mild Cognitive Impairment (MCI) show elevated rates of conversion to dementia as a group, heterogeneity of outcomes is common at the individual level. Using data from a prospective 5-year longitudinal investigation of cognitive change in healthy older adults (N = 262, aged 64-92 years), this study addressed limitations in contemporary MCI identification procedures which rely on single occasion assessment ("Single-Assessment [SA] MCI") by evaluating an alternate operational definition of MCI requiring evidence of persistent cognitive impairment over multiple-testing sessions ("Multiple-Assessment [MA] MCI"). As hypothesized, prevalence of SA-MCI exceeded that of MA-MCI. Further, the MA-MCI groups showed lower baseline cognitive and functional performance and steeper cognitive decline compared with Control and SA-MCI group. Results are discussed with reference to retest effects and clinical implications.
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