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

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Dementia

Dementia

Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....

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Related Experiment Video

Updated: Jan 19, 2026

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

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

Published on: January 11, 2020

A latent variable model approach for assembling and scoring screening tests for dementia.

Jianzhao Shen¹, Paul Crane, Sujuan Gao

¹Division of Biostatistics, Indiana University School of Medicine, 1050 Wishard Boulevard, RG4101, Indianapolis, IN 46202, USA. jiashen@iupui.edu

Journal of Alzheimer'S Disease : JAD

|December 4, 2003

Summary

This summary is machine-generated.

This study introduces a novel latent variable model for dementia screening tests, improving diagnostic accuracy over traditional scoring methods. This approach enhances the predictive power of cognitive assessments for dementia detection.

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Last Updated: Jan 19, 2026

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

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

Published on: January 11, 2020

Using Retinal Imaging to Study Dementia

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Published on: November 6, 2017

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

Published on: October 13, 2016

Area of Science:

Neurology
Psychometrics
Biostatistics

Background:

Dementia diagnosis frequently uses cognitive screening tests.
Current scoring sums correct responses, potentially lacking precision.
This method may limit the predictive power for dementia.

Purpose of the Study:

To propose a latent variable model for scoring and item selection in dementia screening.
To enhance the precision and predictive efficiency of cognitive test scores.
To improve dementia diagnosis through advanced statistical modeling.

Main Methods:

Developed a latent variable model where item responses are random variables.
Modeled disease outcomes as a function of these latent variables.
Utilized maximum likelihood estimation and nonparametric bootstrap for variance estimation.

Main Results:

The proposed latent variable model offers a more precise scoring method.
This approach potentially increases the predictive power for dementia diagnosis.
Demonstrated application using a community-based dementia screening test.

Conclusions:

Latent variable modeling provides a superior alternative to traditional scoring for dementia screening.
This method enhances the utility of cognitive tests in dementia research and clinical practice.
The approach offers improved accuracy and efficiency in identifying individuals at risk for dementia.