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Cognitive Development During Adulthood01:30

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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in

Hongxin Gao1, Stefan Schneider2,3,4,5, Raymond Hernandez2,5

  • 1School of Health Sciences, University of Surrey, Guildford, United Kingdom.

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Summary

A new tool uses data science to detect cognitive impairment early by analyzing questionnaire responses. This approach improves early identification and supports timely dementia intervention in community settings.

Keywords:
artificial intelligencecognitive impairmentscommunity health servicesdementiaearly identificationelder caremachine learningpublic healthsurveys and questionnaires

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Area of Science:

  • Gerontology
  • Data Science
  • Psychometrics

Background:

  • Underdiagnosis of cognitive impairment delays dementia intervention.
  • Community health professionals face challenges in early detection, including a lack of tools and training.

Purpose of the Study:

  • To explore a novel application integrating psychometric methods and data science for early cognitive impairment identification.
  • To model subtle inconsistencies in questionnaire response data for cognitive impairment detection in community settings.

Main Methods:

  • Analysis of questionnaire response data from 12,942 participants (aged 50+) in the Health and Retirement Study.
  • Utilized low-quality response indices from well-being questionnaires (optimism, hopelessness, purpose in life, life satisfaction) and predictive modeling.
  • Evaluated seven predictive models for identifying current cognitive impairment and predicting future dementia or mortality.

Main Results:

  • A multilayer perceptron model showed the best performance in predicting cognitive impairment.
  • Combining low-quality response indices with age and gender improved prediction accuracy (AUC 0.71-0.74).
  • The developed tool outperformed age or health-based screening strategies, especially for younger age groups (50-69).

Conclusions:

  • A novel prediction tool was developed using psychometric methods and data science for passive cognitive impairment assessment.
  • The tool simplifies assessment, reduces burden, and offers a new perspective on utilizing low-quality questionnaire data.
  • The tool is freely accessible on a public portal website to promote early cognitive impairment detection.