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Updated: May 3, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A new scoring method of the mini-mental status examination to screen for dementia
Jin Yeong Choe1, Ji Won Han, Tae Hui Kim
1Department of Neuropsychiatry at Seoul National University Bundang Hospital, Seongnam, Korea.
Background:
Although the Mini-Mental Status Examination (MMSE) is the most widely used screening instrument for dementia, it has several limitations.
Methods:
We developed and validated a new scoring method of the MMSE, namely the short form of the MMSE (MMSE-S).
Results:
The MMSE-S was more robust to demographic influences than the MMSE. The influence of education, in particular, was smaller in the MMSE-S compared to the MMSE (p < 0.01). The diagnostic accuracy of the MMSE-S for very mild to mild dementia was also better than that of the original MMSE (p < 0.0001). Its specificity, in particular, was higher than that of the original MMSE. In Korea, we could improve the post-test probability for dementia from 46.88 to 64.76% by employing the MMSE-S instead of the MMSE. We also provided optimal cut-off scores for dementia stratified by age, education, and gender, which may further improve the diagnostic accuracy of the MMSE-S for dementia.
Conclusion:
Due to its good accuracy and brevity, the MMSE-S may contribute to enhancing the cost-effectiveness of and accessibility to dementia screening as well as early diagnosis and treatment of dementia, particularly in low- and middle-income countries.
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