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Updated: Feb 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Conversions of cognitive screenings : Mini-Mental State Examination vs. Montreal Cognitive Assessment vs. DemTect]
J F Scheffels1, H Kräling2, E Kalbe3
1Klinik und Poliklinik für Neurologie, Uniklinik Köln, Köln, Deutschland. jannik.scheffels@web.de.
Background:
For a comprehensive specification and quantification of neuropsychological deficits, extensive neuropsychological assessment is needed. Due to its time intensiveness, this cannot be accomplished in every clinical setting and is not always necessary. Therefore, screening instruments provide a first step. Because the selection differs between and sometimes even within clinics, a comparison of results for different screening procedures would be helpful. The current study aimed at achieving this in the German-speaking area, i.e. conversions between sum scores of the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Dementia Detection Test (DemTect) can be accomplished.
Method:
In the Department of Neurology at the University Hospital of Cologne, 8240 patients with different neurological diseases were examined between 2008 and 2017. Conversion scores using the results in the MMSE, MoCA and DemTect were computed by using the equipercentile equating method.
Results:
The calculated bidirectional conversion tables enable a quick and easy comparison between the three most commonly used screening instruments. They are also similar to those from previous studies in English-speaking countries.
Conclusion:
The results enable an enhanced longitudinal assessment of cognitive functions in different clinical settings, provide comparability, and offer more flexibility for determination of patient status. An extension of the current study might be the transfer of the method presented to other cognitive or affective domains, such as memory and depression.
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