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Updated: Jul 7, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Nurses' role in identifying mild cognitive impairment in older adults
Amanda Floetke Elliott1, Ann L Horgas, Michael Marsiske
1University of Alabama Center for Mental Health and Aging, Tuscaloosa, AL, USA.
Abstract:
Mild cognitive impairment (MCI), a relatively new descriptive category, is believed to represent a stage between normal aging and early dementia. Nurse practitioners, who provide care for older adults across a variety of settings, are in a key position to detect early cognitive changes. The purpose of this study is to describe an approach to identifying MCI using a variety of measures and a consensus conference with neuropsychologists. The study was conducted in a sample of 130 elderly participants (aged 82.5 years; 81% female) residing in nursing homes, assisted living facilities, and senior housing. A team of clinicians (neuropsychologists and nurses) reviewed cognitive, mental health, and demographic data in consensus conference and classified study participants into 1 of 3 groups: cognitively intact (50.8%), amnestic MCI (19.2%), or probable dementia (30%). Discriminant function analysis (DFA) was used to independently classify individuals into cognitive status groups based on test scores alone and to compare quantitatively determined groups with consensus conference evaluations. The results indicate that the DFA correctly classified 95% of the participants. Further, results revealed a pattern in which persons with amnestic MCI have subtle memory impairments (similar to persons with dementia) but that more general cognitive functioning remains high (similar to intact persons). Nurse practitioners' heightened awareness of subtle distinctions in the dimensions of cognitive status associated with MCI can enhance their practice and assist them in making more informed referrals for dementia evaluations.
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