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Marco de Referencia Asistido por Máquina para el Desarrollo y la Estandarización de Ontologías: Estudio de Caso en
Fang Chen1, Taylor B Harrison1,2, Sunyang Fu1
1McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
Resumen
Las tecnologías de salud digital (DHT) ofrecen atención personalizada pero generan una gran cantidad de literatura. Un marco de ontología adaptativa, que utiliza modelos de lenguaje grandes, ofrece una solución semiautomática para organizar y mejorar el conocimiento de DHT.
Área de la Ciencia:
- Salud Digital; Informática Médica; Gestión del Conocimiento
Sus antecedentes:
- Las tecnologías de salud digital (DHT) están transformando la prestación de atención médica y la investigación.
- El rápido crecimiento de la literatura de DHT presenta desafíos significativos para la organización y recuperación del conocimiento.
- Los métodos existentes de desarrollo de ontologías en salud digital son a menudo manuales, lo que dificulta la eficiencia y la escalabilidad.
Objetivo del estudio:
- Proponer e ilustrar un marco novedoso para gestionar el creciente cuerpo de conocimiento de salud digital.
- Abordar las limitaciones de los métodos tradicionales y manuales de desarrollo de ontologías en el dominio de la salud digital.
- Introducir el concepto de una "ontología adaptativa" para la mejora sistemática y semiautomática de las ontologías de DHT.
Principales métodos:
- Desarrollo de un marco que integra la extracción del léxico de DHT, el enriquecimiento de ontologías y la validación con intervención humana.
- Utilización de modelos de lenguaje grandes (LLM) para potenciar el concepto de ontología adaptativa.
- Aplicación del marco para clasificar y mejorar sistemáticamente las ontologías de salud digital.
Principales resultados:
- Demostración de un enfoque semiautomático para clasificar y mejorar ontologías de DHT.
- Implementación exitosa de una ontología adaptativa impulsada por LLM.
- Un método práctico para gestionar las complejidades de la información cambiante de salud digital.
Conclusiones:
- El marco de ontología adaptativa propuesto ofrece una solución eficiente y escalable para organizar el conocimiento de salud digital.
- Las ontologías adaptativas impulsadas por LLM pueden mejorar sistemáticamente la gestión de la información de DHT.
- Este enfoque proporciona un camino viable para navegar el panorama dinámico de la investigación y aplicación de la salud digital.
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