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Updated: Sep 6, 2025

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Development of informant-reported or self-reported MCI-J questionnaire
Takashi Asada1,2, Tatsuyuki Kakuma3, Miho Ota4
1Memory Clinic Ochanomizu, Tokyo, Japan.
International Journal of Geriatric Psychiatry
|July 1, 2022
Summary
A new questionnaire helps nonprofessionals detect early signs of mild cognitive impairment (MCI) and dementia. High sensitivity suggests its utility in promoting earlier clinical presentation for timely treatment.
Area of Science:
- Geriatric Psychiatry
- Neurology
- Cognitive Health
Background:
- Delayed diagnosis of mild cognitive impairment (MCI) and dementia is common.
- This delay increases patient and caregiver burden and postpones treatment.
Purpose of the Study:
- To develop a novel questionnaire for non-professionals to identify early indicators of MCI and dementia.
- To create a tool that aids in the early detection of cognitive decline.
Main Methods:
- A four-step development process involving a working group and 67 geriatric psychiatry specialists.
- A scoring algorithm was created and validated using misdiscrimination rate and cross-validation.
- Item selection and cut-off value establishment for MCI or dementia symptoms.
Main Results:
- The questionnaire demonstrated high sensitivity (90.6%) and specificity (56.6%) for detecting MCI or dementia symptoms.
- An 82% concordance rate was observed between questionnaire results and definitive diagnoses.
- A cut-off score of 2 was established for patient or informant respondents.
Conclusions:
- The developed questionnaire can facilitate earlier patient presentation for medical evaluation and treatment.
- Its high sensitivity highlights its potential as a screening tool for cognitive impairment.
- The tool is intended for screening and should be complemented by professional medical assessment for definitive diagnosis.
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