Project Description

KATY EURECAT

Development of an innovative AI-empowered Personalised Medicine system that bring medical “AI-empowered knowledge” to the tips of the fingers of clinicians and clinical researchers.

The KATY project aims to develop an AI-empowered personalised medicine system built around two main components: a distributed knowledge graph that connects sources of medical knowledge, and a pool of “explainable predictors” which allows the AI to model patient outcomes.

The AI-empowered knowledge is a human interpretable knowledge that clinicians and researchers can understand, trust and effectively use in their daily working routines. In this sense, the system developed relates the information obtained to researchers and clinicians to help them see new options for patient treatment related to AI-empowered knowledge. The new system will be tested in clear cell renal cell carcinoma (ccRCC) – the most frequent subtype of renal (kidney) cancer accounting approximately for 80% of cases.

The Data Science & Big Data Unit of Eurecat participates in KATY project developing and applying novel methods to analyse large amounts of data. They focus on obtaining actionable knowledge from people’s digital interactions and content on the web and using this knowledge to efficiently support innovative applications.

On the other hand, the IT&OT Security Unit of Eurecat focuses on two main activity lines of the project: security in highly distributed system, which includes topics like security in Cloud Computing, privacy and identity access management, and fraud and cybercrime, including cybercrime risks over the mobile channel.

KATY consortium is coordinated by the University of Rome Tor Vergata and counts with the participation 20 European partners.

 

 

 

 

 

KATY LOGO EURECAT

General details

Project 

KATY – Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity.

Project reference 

DT-TDS-04-2020-101017453

Programme and call for tender

Project funded by the European Union’s program Horizon 2020 under under the call DT-TDS-04-2020 – AI for Genomics and Personalised Medicine