In silico

Unsupervised discovery of public health subgroups associated with cognitive health vulnerability in adults: an analysis of BRFSS 2024

Figure 1. Architecture of the autoencoder used to learn low-dimensional latent representations from high-dimensional public health features.
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Figure 1. Architecture of the autoencoder used to learn low-dimensional latent representations from high-dimensional public health features.Architecture of the autoencoder used to learn low-dimensional latent representations from high-dimensional public health features.Wang et al. · CC BY

Frontiers in Public Health

Abstract

Background Cognitive health-related vulnerability is closely associated with multidimensional public health factors, including chronic disease burden, functional limitation, healthcare access, socioeconomic vulnerability, health behaviors, and preventive service utilization.

The paper

Ollscoil na Gaillimhe – University of Galway · Central South University

Frontiers in Public Health · 14 Sep 2026 · CC BY

doi.org/10.3389/fpubh.2026.1878119PubMed 42807198

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