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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Problems in Classifying Mild Cognitive Impairment (MCI): One or Multiple Syndromes?
María Del Carmen Díaz-Mardomingo1, Sara García-Herranz2, Raquel Rodríguez-Fernández3
1Department of Basic Psychology I, National University of Distance Education, Juan del Rosal 10, 28040 Madrid, Spain. mcdiaz@psi.uned.es.
Abstract:
As the conceptual, methodological, and technological advances applied to dementias have evolved the construct of mild cognitive impairment (MCI), one problem encountered has been its classification into subtypes. Here, we aim to revise the concept of MCI and its subtypes, addressing the problems of classification not only from the psychometric point of view or by using alternative methods, such as latent class analysis, but also considering the absence of normative data. In addition to the well-known influence of certain factors on cognitive function, such as educational level and cultural traits, recent studies highlight the relevance of other factors that may significantly affect the genesis and evolution of MCI: subjective memory complaints, loneliness, social isolation, etc. The present work will contemplate the most relevant attempts to clarify the issue of MCI categorization and classification, combining our own data with that from recent studies which suggest the role of relevant psychosocial factors in MCI.
Insights
This study revises mild cognitive impairment (MCI) subtypes, incorporating psychometric, statistical, and psychosocial factors. It addresses classification challenges to improve understanding and diagnosis of MCI progression.
Area of Science:
- Neurology
- Psychiatry
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) classification into subtypes presents challenges.
- Advances in dementia research highlight the need for revised MCI conceptualization.
- Existing classification methods lack comprehensive consideration of influencing factors.
Purpose of the Study:
- To revise the concept and subtypes of mild cognitive impairment (MCI).
- To address MCI classification problems using psychometric and alternative methods.
- To integrate psychosocial factors into the understanding of MCI genesis and evolution.
Main Methods:
- Review of psychometric approaches to MCI classification.
- Application of latent class analysis for subtype identification.
- Integration of data on psychosocial factors impacting MCI.
Main Results:
- Identified limitations in current MCI classification systems.
- Highlighted the significant impact of psychosocial factors (e.g., loneliness, social isolation) on MCI.
- Demonstrated the need for a multidimensional approach to MCI categorization.
Conclusions:
- Revising MCI classification requires considering both cognitive and psychosocial dimensions.
- Accurate MCI subtyping is crucial for understanding disease progression and developing targeted interventions.
- Future research should focus on integrating diverse factors for a holistic view of MCI.
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