BIOMEDICAL DATA SCIENCE LABORATORY


About us


We are a multidisciplinary research group committed with the development of technology based on data science & artificial intelligence for real health problems. We are located at ITACA Institute, at Universitat Politècnica de València (Spain). Our expertise includes machine learning, predictive modelling, data quality and variability, multiparametric tissue signatures, decision support systems and medical imaging.

Team


Juan Miguel García Gómez

Juan Miguel García Gómez
Professor at UPV and Head of BDSLab

>Marta Durá Hernández

Marta Durá Hernández
Scientific manager

Carlos Sáez Silvestre

Carlos Sáez Silvestre
Senior researcher at BDSLab

Sabina Asensio Cuesta

Sabina Asensio Cuesta
Associate Professor at UPV

Javier Juan Albarracín

Javier Juan Albarracín
PhD

Pablo Ferri Borredá

Pablo Ferri Borredá
PhD Student

Vicent Blanes Selva

Vicent Blanes Selva
PhD Student

María del Mar Álvarez Torres

María del Mar Álvarez Torres
PhD Student

Ángel Sánchez García

Ángel Sánchez García
Researcher

Eduard Artur Chelebian Kocharyan

Eduard Artur Chelebian Kocharyan
Researcher

Elies Fuster Garcia

Elies Fuster Garcia
Collaborator

Miquel Oltra

Miquel Oltra
Collaborator

David Lorente

David Lorente
Collaborator

Harold Woods

Harold Woods
Collaborator

Jose Muñoz

Jose Muñoz
Collaborator

Raquel Faubel

Raquel Faubel
Collaborator

Alberto Conejero

Alberto Conejero
Associate Professor at UPV

Ricardo Garcia de León

Ricardo Garcia de León
Collaborator

Research


Mortality Forecast
Oncohabitats
112
magma
spumoni
jamia
blast2go
elvesier
methods
Biomedical Data Quality
Medical Imaging
Clinical Decision Support Systems

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.

Biomedical Data Quality

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.

Medical Imaging

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.

Clinical Decision Support Systems

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.

Projects

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Publications

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Scientific Software


oncohabitats

Oncohabitats

wakamola

Wakamola

crowdhealth

Crowdhealth

lalaby

Lalaby

covid

COVID-19 SDE Tool

ehr temporal variability

EHRTemporalVariability

112

112

alcoa

ALCOA+

inadvance palliative project

Palliative

agrofoodai

AgrofoodAI

Contact


Send us a message

Send us a message through the website or the following links:

Prof. Juan M Garcia-Gomez (E-mail)
Prof. Juan M Garcia-Gomez (Linkedin)
Prof. Juan M Garcia-Gomez (Twitter)