Overview

Background

Everyday, thousands of  biomedical research and clinical data are produced worldwide. Nevertheless, the heterogeneity of biomedical data  prevents current machine learning architectures from extracting medical knowledge and value from them and to develop models that can generalize well to clinical practice. 

Objectives

MedMax sets a crucial  step towards clinical data exploitation by allowing unsupervised knowledge extraction. MedMax's objectives include the development of methods to harmonize medical data, to link medical text data to medical knowledge references and to identify significant relationships among inter-disciplinary data, targeting precision medicine. 

Excellence

MedMax has a ground breaking impact because is that it allows a transversal use of clinical and scientific biomedical data, allowing to identify automatically significant relationships among data collected for decades in different domains.

MedMax is beyond the state of the art but has a solid scientific approach, based on the most recent advancements in biomedical and clinical applications of applied machine learning and multimodal data fusion.

Funding

Supporting TAlent in ReSearch@University of Padua - STARS@UNIPD 2021; Fondo per la promozione e lo sviluppo delle politiche del Programma Nazionale per la Ricerca (PNR) di cui al decreto ministeriale n. 737 del 25 giugno 2021

Contacts

manfredo.atzori at unipd.it

andrea.zanola at phd.unipd.it

federico.delpup at studenti.unipd.it

louisfabrice.tshimanga at unipd.it