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. 2026 Aug 6;28:e90502. doi: 10.2196/90502

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©Eva Soler Espejo, Yang Chen, María Pilar Ramos-Bratos, Sandra Ortega-Martorell, Iván Olier, José Miguel Rivera-Caravaca, Francisco Marín, Vanessa Roldán, Gregory Y.H. Lip. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.08.2026.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Generative topographic mapping (GTM) reference vector maps illustrating the distribution of modeling variables across the latent space in the Murcia Atrial Fibrillation Project III (MAFP-III) cohort. Variables included in GTM model training are grouped according to clinically relevant domains, including demographics, blood count parameters, diabetes risk markers, renal and liver function, cholesterol markers, arterial blood pressure, and echocardiographic features. Color intensity reflects the relative magnitude of each variable across latent-space regions. HbA1c: glycated hemoglobin; HDL: high-density lipoproteins; LVEF: left ventricular ejection fraction.