Overview
This page collects the work developed by GSI within the project AROMA - MIRATAR.
Proyecto TED2021-132149B-C41 financiado por el Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 y por la Unión Europea - NextGenerationEU/PRTR.
Project funded by the Spanish Ministry for Science and Innovation and by the European Union - NextGenerationEU/PRTR.
Publications
- Matteo Leghissa, Álvaro Carrera Barroso & Carlos A. Iglesias. (2023). Machine Learning Approaches for Frailty Detection, Prediction and Classification in Elderly People: A Systematic Review. International Journal of Medical Informatics, 178, 105172.
- Matteo Leghissa, Álvaro Carrera Barroso & Carlos A. Iglesias. (2024). FRELSA: A dataset for frailty in elderly people originated from ELSA and evaluated through machine learning models. International journal of medical informatics, 192.
- Roberto Móstoles Rodríguez, Oscar Araque & Carlos A. Iglesias. (2024). Using Enhanced Representations to Predict Medical Procedures from Clinician Notes. Applied Sciences, 14 (15).
- de Enciso García, J., Matteo Leghissa, Oscar Araque & Álvaro Carrera Barroso (2024). Exploring Temporal Features in Health Records for Frailty Detection. In de Sevilla, U. (editor), CASEIB 2024. Libro de Actas del XLII Congreso Anual de la Sociedad Española de Ingeniería Biomédica.
Press
The AROMA project has been disseminated in the magazine "N3WS Tercera Edad":
- February, 2024, pages 20-21 - Desarrollan un sistema de predicción de fragilidad en ancianos, available here: