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Updated: Mar 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
"Missed" Mild Cognitive Impairment: High False-Negative Error Rate Based on Conventional Diagnostic Criteria
Emily C Edmonds1, Lisa Delano-Wood1,2, Amy J Jak1,2
1Department of Psychiatry, University of California San Diego, School of Medicine, La Jolla, CA, USA.
Abstract:
Mild cognitive impairment (MCI) is typically diagnosed using subjective complaints, screening measures, clinical judgment, and a single memory score. Our prior work has shown that this method is highly susceptible to false-positive diagnostic errors. We examined whether the criteria also lead to "false-negative" errors by diagnostically reclassifying 520 participants using novel actuarial neuropsychological criteria. Results revealed a false-negative error rate of 7.1%. Participants' neuropsychological performance, cerebrospinal fluid biomarkers, and rate of decline provided evidence that an MCI diagnosis is warranted. The impact of "missed" cases of MCI has direct relevance to clinical practice, research studies, and clinical trials of prodromal Alzheimer's disease.
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