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Updated: May 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predictors of future cognitive decline in persons with mild cognitive impairment
Haiqun Xie1, Nancy Mayo, Lisa Koski
1Division of Experimental Medicine, Department of Medicine, McGill University, and The Montreal General Hospital, Montreal, Que., Canada.
Abstract:
This study sought first to identify individual items of the Mini-Mental State Examination (MMSE) and demographic variables at baseline that predicted the trajectories of cognitive change among patients with mild cognitive impairment (MCI), and second to quantify the risk of cognitive decline in such patients based on their pattern of failure of MMSE items. 187 MCI patients were evaluated serially with the MMSE for up to 3.5 years. Patients who followed a declining cognitive trajectory differed from the stable reference group in their baseline profile of MMSE test performance. Patient age and performance on delayed recall, constructional praxis, attention, and orientation to time and floor predicted future cognitive decline with good accuracy (79.9%) and specificity (86.4%), and moderate sensitivity (67.2%). These results are presented in the form of a simple clinical tool for quantifying risk of future cognitive decline in MCI.
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