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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

Predictive utility of type and duration of symptoms at initial presentation in patients with mild cognitive

Deidre J Devier1, Nicole Villemarette-Pittman, Patrick Brown

  • 1Department of Neurology, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA. ddevie @ lsuhsc.edu

Dementia and Geriatric Cognitive Disorders
|September 18, 2010
PubMed
Abstract

Insights

Early memory decline and disorientation in mild cognitive impairment (MCI) predict faster conversion to Alzheimer's disease (AD). Older age and lower MMSE scores further increase this risk.

Area of Science:

  • Neurology
  • Neuroscience
  • Gerontology

Background:

  • Mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD).
  • Understanding predictors of MCI conversion to AD is crucial for early intervention.

Purpose of the Study:

  • To determine the duration and symptoms associated with MCI.
  • To evaluate how these factors predict conversion to Alzheimer's disease (AD).

Main Methods:

  • Systematic assessment and follow-up of 148 participants diagnosed with MCI.
  • Utilized age-stratified Cox models to identify predictors of conversion.

Main Results:

  • Memory decline was the initial symptom in 118 MCI cases.
  • Individuals converting to AD exhibited more symptoms (e.g., language, depression) and disorientation.
  • Shorter time since memory decline onset and lower baseline Mini-Mental State Examination (MMSE) scores predicted conversion.

Conclusions:

  • Recent memory decline, older age, lower MMSE scores, performance changes, and disorientation are indicators of short-term AD conversion risk.
  • These findings aid in identifying MCI patients at higher risk for progression to Alzheimer's disease.

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