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Updated: May 12, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Which part of the Quick mild cognitive impairment screen (Qmci) discriminates between normal cognition, mild
Rónán O'Caoimh1, Yang Gao, Paul Francis Gallagher
1Centre for Gerontology and Rehabilitation, St Finbarrs Hospital, Douglas Road, Cork City, Ireland. rocaoimh@hotmail.com
Introduction:
the Qmci is a sensitive and specific test to differentiate between normal cognition (NC), mild cognitive impairment (MCI) and dementia. We compared the sensitivity and specificity of the subtests of the Qmci to determine which best discriminated NC, MCI and dementia.
Objective:
the objective was to determine the contribution each subtest of the Qmci makes, to its sensitivity and specificity in differentiating MCI from NC and dementia, to refine and shorten the instrument.
Methods:
existing data from our previous study of 965 subjects, testing the Qmci, was analysed to compare the sensitivity and specificity of the Qmci subtests.
Results:
all the subtests of the Qmci differentiated MCI from NC. Logical memory (LM) performed the best (area under the receiver operating curve of 0.80), registration the worst, (0.56). LM and verbal fluency had the largest median differences (expressed as percentage of total score) between MCI and NC, 20 and 25%, respectively. Other subtests did not have clinically useful differences. LM was best at differentiating MCI from NC, irrespective of age or educational status.
Conclusion:
the Qmci incorporates several important cognitive domains making it useful across the spectrum of cognitive impairment. LM is the best performing subtest for differentiating MCI from NC.
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