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Updated: May 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Validation of a latent construct for dementia case-finding in Mexican-Americans
Donald R Royall1, Raymond F Palmer,
1Department of Psychiatry, The University of Texas Health Science Center, San Antonio, TX, USA. royall@uthscsa.edu
Abstract:
We have constructed a latent dementia proxy, "δ", and validated it in several datasets, including well characterized subjects participating in the Texas Alzheimer's Research and Care Consortium (TARCC) study. It may be possible to construct δ homologs from almost any ad hoc combination of cognitive and functional status measures. δ homologs may also be relatively immune to measurement error, including cultural, linguistic, or educational biases. These properties make factor scores derived from latent variables a potentially attractive solution for dementia case-finding in rural or minority populations. Here we have explored an alternative and briefer assessment by which to construct a δ homolog and validate the resulting latent variable (dMA) in Mexican-American (MA) TARCC subjects. dMA, composed of simple "bedside" dementia screening instruments, achieves Areas Under the Receiver Operative Curve that rival those of δ itself. Ethnicity has a small effect on dMA's performance. These results suggest that it may be possible to validly export dMA into other MA populations, or to export δ homolog factor scores from one population to another.
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