iHelp aims to early detect and mitigate the risks associated with Pancreatic Cancer applying advanced Artificial Intelligence (AI)-based techniques to support the actors of the system. Those techniques are performed on historic data of cancer patients gathered from existing cohorts and biobanks. The models developed through the AI-based learning techniques will be useful to identify the risk earlier, and to elaborate a mitigation plan. To this end, iHelp will use and extend the paradigm of Holistic Health Records (HHR) (K., A., B., + 17), in order to aggregate and re-use data needed by the Artificial Intelligence for developing their models. Several different sources will feed the common shared informative base, and for achieving this goal,...
The collection of medical data has increased dramatically in recent years, which made it possible to...
Substantial interest and investment in clinical artificial intelligence (AI) research has not result...
To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research ...
iHelp aims to early detect and mitigate the risks associated with Pancreatic Cancer applying advance...
This deliverable summarizes the work that has been done in the core of T4.2 (“Model Library: Impleme...
This document summarizes the actions performed under T4.1 “Personalized Health Modelling and Predict...
The objective of this deliverable is to define personalised recommendations in terms of prevention a...
This report summarizes the actions performed under Task 5.1 - “Techniques for early risk identificat...
This document represents the assessment of the iHelp approaches by showing the proposed data collect...
The iHelp integrated solution aims at providing personalised health monitoring and decision support ...
This report provides an overview of the data modelling approaches adopted in iHELP project. The main...
There are five pilot studies within the iHelp designed to help test and validate the personalised he...
The iHelp integrated solution aims at providing personalised health monitoring and decision support ...
HELPSALUD (Research in Machine Learning techniques applied to real problems in the Health sector) is...
The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and me...
The collection of medical data has increased dramatically in recent years, which made it possible to...
Substantial interest and investment in clinical artificial intelligence (AI) research has not result...
To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research ...
iHelp aims to early detect and mitigate the risks associated with Pancreatic Cancer applying advance...
This deliverable summarizes the work that has been done in the core of T4.2 (“Model Library: Impleme...
This document summarizes the actions performed under T4.1 “Personalized Health Modelling and Predict...
The objective of this deliverable is to define personalised recommendations in terms of prevention a...
This report summarizes the actions performed under Task 5.1 - “Techniques for early risk identificat...
This document represents the assessment of the iHelp approaches by showing the proposed data collect...
The iHelp integrated solution aims at providing personalised health monitoring and decision support ...
This report provides an overview of the data modelling approaches adopted in iHELP project. The main...
There are five pilot studies within the iHelp designed to help test and validate the personalised he...
The iHelp integrated solution aims at providing personalised health monitoring and decision support ...
HELPSALUD (Research in Machine Learning techniques applied to real problems in the Health sector) is...
The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and me...
The collection of medical data has increased dramatically in recent years, which made it possible to...
Substantial interest and investment in clinical artificial intelligence (AI) research has not result...
To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research ...