La investigación desarrollada en el proyecto MIRATAR por Matteo Leghissa en https://www.telemadrid.es/noticias/sociedad/Un-espejo-inteligente-para-predecir-la-fragilidad-en-ancianos-0-2730326963--20241202020929.html
The article "Exploring Temporal Features in Health Records for Frailty Detection" wa presented at the conference CASEIB 2024 (Congreso Anual de la Sociedad Española de Ingeniería Biomédica 2024), in Sevilla on the 13th of November. The full paper can be found in the "Libro de actas CASEIB 2024" at this link.
The publication is authored by Julia de Enciso, Matteo Leghissa, Óscar Áraque and Álvaro Carrera, and the study was supported by the AROMA / MIRATAR project, grant TED2021-132149BC42 funded by MICIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. The full article can be found in the in-proceedings book of CASEIB 2024.
Abstract:
The global population is rapidly aging, which poses significant challenges for healthcare systems worldwide, including increased costs and a rising demand for effective geriatric care. Addressing these challenges necessitates innovative approaches to improve the early detection and prediction of frailty among elderly individuals, aiming to alleviate healthcare burdens and enhance quality of life. This study focuses on the development of a machine learning system aimed at improving early detection and prediction of frailty among elderly populations. To achieve these results we used the FRELSA dataset, a frailty-specific dataset originated ELSA, an influential longitudinal study on aging with 9 waves of data collection and mo re than 5000 participants. The research begins by optimizing clinical data collection through feature extraction to enhance efficiency in frailty assessment. Various machine learning techniques, including Multilayer Perceptron (MLPs) and Convolutional Neural Networks (CNNs), are evaluated for their ability to predict frailty based on the identified features. Additionally, the study explores temporal dependencies within data to gain insights into the progression of frailty and to facilitate more personalized patient care approaches. A comparative analysis with existing baseline models highlights the superior performance of the proposed algorithms in the early detection and prediction of frailty. These findings contribute significantly to advancing the field and lay a foundation for future research aimed at implementing advanced clinical decision support systems in geriatric care settings.
La ETSIT ha organizado un evento de homenaje a Gregorio Fernández, profesor de la ETSIT, donde se inaugurará la exposición de máquinas físicas de Símplez y Algorítmez en el museo de la ETSIT.
El evento es el miércoles 11 de diciembre a las 13:30 en la Biblioteca de la ETSIT. Estáis todos invitados. Si queréis asistir, por favor registraros para estimar el aforo del evento: registro del evento.
Como resultado del proyecto AMOR, se ha publicado el plugin multinavegador del GSI para analizar valores morales. Es una versión aún inicial que se irá mejorando y extendiendo al español durante el proyecto.
Está accesible en: https://chromewebstore.google.com/detail/moral-analysis-gsi/djkmleadajmkilcihgneikefeomnodim?hl=en-GB
