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Updated: Jan 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mild cognitive impairment and neuropsychological examination
1Department of Neurology, Akershus University Hospital and Department of Psychology, University of Oslo, Oslo, Norway.
Objective:
Mild cognitive impairment (MCI) is a condition that involves impairment of cognitive function beyond what is expected with normal ageing. The condition is prevalent in old age and may be a risk factor for the development of dementia. However, MCI can have medical and psychological causes that do not cause further cognitive decline or dementia. Thus, it is important to identify MCI at an early stage, aiming to prevent further impairment, to inform necessary life adaptation to cognitive problems or to treat the condition when the cause of cognitive impairment can be treated.
Method:
The present paper is not based on a comprehensive review of the field but considers the various types of MCI according to the internationally prevailing diagnostic systems and algorithms, proposed key progression factors, focusing on the role of neuropsychological assessment in the diagnosis of MCI.
Results:
The paper discusses according to prevailing diagnostic systems and algorithms, which cognitive domains that are relevant to investigate, which tests that may be relevant, what kind of norms have satisfactory quality, which cut-off scores do best balance sensitivity and specificity in a neurodiagnostic context, and what kind of conclusions and recommendations that can be drawn from neuropsychological findings.
Conclusion:
Comprehensive neuropsychological assessment based on more than one test in each of the five cognitive domains (memory, attention, language, visuospatial function, and executive function) recommended by NIA-AA, DSM-5 and ICD-11, employing national and culturally adapted norms has shown superior validity regarding neuropathology and prognosis and is recommended as best practice.
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