Juan Miguel García Gómez
Full Professor at UPV and Head of BDSLab
Marta Durá Hernández
Scientific manager and PhD Student
Carlos Sáez Silvestre
Associate Professor at UPV
Elies Fuster Garcia
Associate Professor at UPV
Sabina Asensio Cuesta
Associate Professor at UPV
Alberto Conejero
Full Professor at UPV
Pablo Ferri Borredá
PhD and Senior Researcher
Vicent Blanes Selva
PhD and Senior Researcher
Ángel Sánchez García
PhD Student
Rebeca Burgos Panadero
PhD and Senior Researcher
Francisco Javier Gil-Terrón Rodríguez
PhD Student
Carles López Mateu
Researcher
Kevin García Santos
Researcher
María Gómez Mahiques
Researcher
Víctor Montosa Micó
Researcher
Daniel Sánchez García
Researcher
María José Cardona Cubells
Administrative Officer
David Fernández Narro
Researcher
Toni Blasco Calafat
PhD Student
Choni Doñate Martínez
PhD and Senior Researcher
Eduard Artur Chelebian Kocharyan
Miquel Oltra
David Lorente
Jose Muñoz
Raquel Faubel
Ricardo Garcia de León
Nekane Romero
Lexin Zhou
Juan Martínez Miranda
Jorge David Mínguez Fons
María del Mar Álvarez Torres
Javier Juan Albarracín
We investigate and apply novel methods to measure, describe and control data quality and variability for trustworthy use of biomedical data. Our 9-dimensional DQ framework and specialized methods for assessing temporal and multi-source variability complement database, machine learning and visual analytics approaches. Being robust to data quality and dataset shifts, we aim for reliable and explainable artificial intelligence for real world data.
Artificial Intelligence is revolutionizing medical imaging, becoming an indisputable tool for modern medicine. We investigate innovative Deep Learning solutions to extract valuable knowledge from images to help addressing complex clinical problems. Our habitats-based imaging technology has demonstrated strong correlations with relevant clinical outcomes in patients with glioblastoma, unlocking new possibilities in early therapy planning support.
Our research line in decision support is focused on the development of predictive models and Clinical Decision Support Systems (CDSS). From rule-based systems implementing international diabetes guidelines to deep learning for emergency medical call incidents classification and machine learning models to assess palliative care needs. Our goal is to provide decision support tools to physicians and health experts concerning several health issues.
We investigate and apply novel methods to measure, describe and control data quality and variability for trustworthy use of biomedical data. Our 9-dimensional DQ framework and specialized methods for assessing temporal and multi-source variability complement database, machine learning and visual analytics approaches. Being robust to data quality and dataset shifts, we aim for reliable and explainable artificial intelligence for real world data.
Artificial Intelligence is revolutionizing medical imaging, becoming an indisputable tool for modern medicine. We investigate innovative Deep Learning solutions to extract valuable knowledge from images to help addressing complex clinical problems. Our habitats-based imaging technology has demonstrated strong correlations with relevant clinical outcomes in patients with glioblastoma, unlocking new possibilities in early therapy planning support.
Our research line in decision support is focused on the development of predictive models and Clinical Decision Support Systems (CDSS). From rule-based systems implementing international diabetes guidelines to deep learning for emergency medical call incidents classification and machine learning models to assess palliative care needs. Our goal is to provide decision support tools to physicians and health experts concerning several health issues.
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ONCOhabitats
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112
ALCOA+
Palliative Care Assessment Tool
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