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Updated: Feb 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development and Testing of a Neuropsychiatric Symptoms Model in Mild Cognitive Impairment
Abstract:
Neuropsychiatric symptoms are prevalent in individuals with mild cognitive impairment (MCI) and have a significant detrimental effect on health and quality of life. Identifying factors that contribute to their occurrence may enable prompt treatment and intervention. The current study entails the development and testing of a biopsychosocially based model to assist nurses in the identification of individuals with MCI who are most likely to experience symptoms of depression, apathy, and/or anxiety. Factors within the biological and sociodemographic domains of the Neuropsychiatric Symptoms in MCI (NPSMCI) model were tested using multivariate logistic regression analyses. Findings suggest that age, presence of an e4 allele of the apolipoprotein E gene, living situation, and degree of comorbid illness were associated with the occurrence of symptoms of depression and apathy. Further testing and refinement are necessary, but the findings provide guidance to nurses and alert them to assess individuals most likely to experience these symptoms. [Journal of Gerontological Nursing, 44(1), 21-30.].
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