The Biomedical Data Science Lab (BDSLab) is a multidisciplinary research group committed to developing a technology based on data science & artificial intelligence for real health problems. BDSLab expertise includes machine learning, predictive modelling, data quality and variability, multiparametric tissue signatures, decision support systems, and medical imaging.
Aiming to a trustworthy data use, BDSLAb conducts research in methods and tools to help profile and control data quality. The goal is to achieve reliable data science and artificial intelligence, being robust to data quality and variability in real-world data.
BDSLab investigates innovative Deep Learning solutions to extract valuable knowledge from MRI images to address complex clinical problems, for example, glioblastoma, which is the most aggressive brain tumour.
Another of BDSLab research lines focuses on the development of predictive models and Clinical Decision Support Systems Ther goal is to provide reliable tools to physicians and health experts concerning several health issues; our latest work is a system to assess palliative care needs on hospital admitted patients.
BDSLab also focuses its research on mHealth, mobile applications for health care. Such as the Wakamola chatbot to collect data from users about their lifestyle and calculate their obesity risk.
Defense made by Mr. Rubén Molero Alabau
Defense made by Mr. Rodolfo Zapata Zapata
UPV, CSIC and Sener sign an agreement to develop a pilot plant to generate green hydrogen using microwaves
Defense made by Mr. Santiago Jiménez Serrano
Do not miss this lecture by the renowned epidemiology expert Daniel López-Acuña
Two days to propose solutions to health and wellness challenges in the best of environments
Taught by ITACA-SABIEN group and organised by the European regional office of the World Health Organisation
PersonalizeAF project raises awareness of Atrial Fibrillation
Innovative and sustainable solutions for garment finishing in the textile industry and its automation.
New paper from Luis Nuño at the renowned Journal of Mathematics and Music