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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development of a Brief Cognitive Screen for Mild Cognitive Impairment and Neurocognitive Disorder
John L Woodard1, Elinor S W Dorsett1, James G Cooper2
1a Department of Psychology, Rosalind Franklin University of Medicine and Science (formerly Finch University of Health Sciences/The Chicago Medical School).
Abstract:
The purpose of this study was to develop and evaluate a brief cognitive screen for possible Mild Cognitive Impairment (MCI) and other neurocognitive impairment in a sample of general medical patients. Two hundred community-dwelling general medical patients aged 65 or older participated in this study. Age and education corrected scores from the Rey Auditory Verbal Learning Test and Mattis Dementia Rating Scale were used to classify participants into three groups: MCI, neurocognitive disorder, cognitively intact. Groups were then compared on an independent set of measures: the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery, Mini-Mental State Examination, and Clock Drawing Test. The MCI group constituted 9% of the sample, while 10.5% of the sample was classified into the neurocognitive disorder group. The CERAD Verbal Category Fluency and CERAD Word List Memory Test Delayed Recall measures were individually and collectively the most effective in differentiating cognitively intact elders from persons in both the MCI and neurocognitive disorder groups. We conclude that these two brief measures appear to be effective for detecting possible MCI or neurocognitive disorder, thereby facilitating identification of candidates who would benefit from more detailed neuropsychological assessment.
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