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Updated: Jun 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Identification of high-risk dementia cohorts in a community sample of Japanese elderly
Shunichiro Shinagawa1, Shiori Nakamura, Makoto Iwamoto
1Department of Psychiatry, The Jikei University School of Medicine, Minato-ku, Tokyo 105-8461, Japan.
Aim:
The aim of this study was to develop a simple diagnostic procedure for subjects at high risk of developing dementia using the Clinical Dementia Rating (CDR), which is applicable to community-based activities.
Methods:
This study divided 252 community-dwelling elderly with a CDR score of 0.5 into two groups based on the presence or absence of cognitive decline within the previous one year of the baseline, as assessed by a semi-structured interview. One hundred subjects were in the 'previously progressive group' (PP group) and 152 subjects were in the 'previously stable group' (PS group). After 6 years of observation, a total of 111 subjects were assessed in the follow-up investigation.
Results:
Among the 39 subjects from the PP group (82.9 +/- 6.8 years old, 11 male, 28 female), 34 developed dementia (87%). Among the 72 subjects from the PS group (84.4 +/- 6.0 years old, 22 male, 50 female), 44 developed dementia (61%). The relative risk of developing dementia for the PP group versus the PS group was 1.43. The rate of conversion to dementia was 12.9% per 100 person-years in the PP group, and 9.8% in the PS group. In the PP group, the Mini-Mental State Examination score was significantly lower and the CDR score was significantly higher than in the PS group.
Conclusion:
Although there have been many attempts to identify subjects with high risk of dementia, this preliminary study suggests that information about temporal changes in cognitive function is useful when performing community-based surveys.
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