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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

[A review of the construct of mild cognitive impairment: general aspects]

J L Sanchez-Rodriguez1, C Torrellas-Morales

  • 1Universidad de Salamanca, Facultad de Psicologia, 37005 Salamanca, Espana. jlsanch@usal.es

Revista De Neurologia
|February 23, 2011
PubMed
Abstract

Insights

Mild cognitive impairment (MCI) is a critical transitional stage to dementia. Accurate diagnosis is essential to differentiate pathological aging from normal aging processes.

Area of Science:

  • Neurology
  • Gerontology
  • Cognitive Science

Context:

  • Mild cognitive impairment (MCI) represents a complex diagnostic challenge.
  • Distinguishing MCI from normal aging is crucial for early intervention.
  • MCI is debated as either an early dementia stage or an independent condition.

Purpose:

  • To provide a comprehensive overview of mild cognitive impairment (MCI).
  • To explore the historical context, subtypes, and diagnostic criteria for MCI.
  • To review epidemiological data and progression markers of MCI.

Summary:

  • This review details the definition, subtypes, and diagnostic criteria of MCI, including Petersen's widely used method.
  • It examines epidemiological data (incidence/prevalence) and key markers (biological, psychological, behavioral, neuropsychological) associated with MCI progression.
  • The evaluation process and diagnostic steps for MCI are thoroughly described.

Impact:

  • Aims to improve diagnostic accuracy for mild cognitive impairment (MCI).
  • Facilitates better discrimination between normal aging and pathological cognitive decline.
  • Provides a foundational resource for researchers and clinicians in dementia and aging.

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