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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Reasonable neuropsychological battery to identify mild cognitive impairment
Shengjie Zhao1, Gonglu Liu, Yuan Shen
1Department of Neurology, Shanghai Jiaotong University Affiliated First People's Hospital, Shanghai, China.
Abstract:
Mild cognitive impairment (MCI) was popular studied while the neuropsychological tests to evaluate and identify MCI patients were not standard used. So there is a need to develop objective, cost-effective and normative screening tests which can evaluate cognitive problems for older individuals at risk of Alzheimer's disease (AD). According to lots of valuable research results, we hypothesize that a neuropsychological battery containing MMSE, AVLT, and MoCA could successfully identify MCI from other cognitive impairment. And CDT in MoCA should not only be scored by a 3-point scoring method but for the second time in qualitative assessments and quantitative analysis independently. This battery measures almost the broadest aspects of cognition in relatively short time to avoid lacking of concentration due to long time. Of course, general physical examinations and a computed tomography (CT) or magnetic resonance imaging (MRI) brain scan should be performed before neuropsychological tests to exclude neurologic diseases which would affect cognition.
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