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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

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A Multifactor Approach to Mild Cognitive Impairment

Taha Qarni1, Arash Salardini1

  • 1Department of Neurology, Yale School of Medicine, Yale University, New Haven, Connecticut.

Seminars in Neurology
|March 30, 2019
PubMed

Insights

Mild cognitive impairment (MCI) is a stage between normal cognition and dementia. Early diagnosis and management of MCI are crucial for potentially slowing progression to dementia.

Area of Science:

  • Neurology
  • Gerontology
  • Cognitive Science

Background:

  • Mild cognitive impairment (MCI) signifies an intermediate phase between normal cognitive function and dementia.
  • Individuals with MCI face an elevated risk of progressing to dementia, with progression rates influenced by factors like age, gender, and education.

Purpose of the Study:

  • To review current understanding of MCI, including its various conceptions.
  • To discuss diagnostic and prognostic methods for MCI.
  • To outline existing management strategies for MCI.

Main Methods:

  • Neuropsychological criteria and functional status assessments are used for MCI diagnosis.
  • Advancements in dementia-related biomarker assessment aid in MCI staging and prognostication.

Main Results:

  • MCI diagnosis can be based on cognitive decrements or decline in functional status.
  • Biomarker analysis improves the accuracy of MCI staging and predicting dementia conversion.

Conclusions:

  • MCI is a critical stage for therapeutic intervention aimed at reducing dementia conversion rates.
  • Understanding MCI's diagnosis, prognosis, and management is vital for future treatment development.

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