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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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[Patterns of detection of mild cognitive impairment in nursing]

Ana J Sebastián Hernández, Luís Carlos Arranz Santamaría

    Revista Espanola De Geriatria Y Gerontologia
    |April 10, 2018
    PubMed

    Mild cognitive impairment (MCI) is characterized by an acquired cognitive loss that places individuals, mainly older adults, in an intermediate stage between normal cognitive functioning and dementia. This impairment has a high risk of progression to dementia and is suitable for screening, which allows more effective early intervention. Nursing professionals, especially community-based primary care nurses, play an important role in the detection and follow-up of MCI and in interventions for this condition. The first step should be to take a thorough history from both the patient and his or her carers, which should assess the changes occurring in the patient's daily, family and social life through functional patterns. In subsequent assessment of cognitive function, brief screening tests can be used such as the Mini Mental State Examination (MMSE) or other similar tests. Special attention should be paid to the presence of affective or depressive symptoms, sensory deficits, polypharmacy, decompensated cardiovascular risk factors, and rapid functional deterioration, given their particular influence on MCI. Finally, various nurse-led, non-pharmacological interventions that are effective in MCI can be recommended, based on cardiovascular risk factor control, physical exercise, and cognitive and psychosocial interventions.

    Keywords:
    Alzheimer diseaseBrief cognitive testsCuidados enfermerosDemenciaDementiaEnfermedad de AlzheimerFactores de riesgo vascularIntervenciones no farmacológicasMid cognitive impairmentNon-pharmacological interventionsNursing careTest breve para la evaluación del estadoVascular risk factorscognitivo

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