Research Project
iHelp: Artificial Intelligence against Pancreatic Cancer
Identification and mitigation of Pancreatic Cancer risk with Artificial Intelligence and DecisionSupport techniques on historic and real-world data.
Where: international
Where: international
Challenge
Cancer is one of the main causes of death in Europe and worldwide. Pancreatic Cancer is the seventh leading cause of cancer-related deaths worldwide, with a higher incidence in developed countries and a survival rate of about 5%.
Pancreatic Cancer usually lacks clear symptoms at its early stages but once invasive cancer develops, it could rapidly spread beyond the Pancreas: early risk assessment and early detection can be the key to help save lives.
Pancreatic Cancer usually lacks clear symptoms at its early stages but once invasive cancer develops, it could rapidly spread beyond the Pancreas: early risk assessment and early detection can be the key to help save lives.
Approach
iHelp aims at applying Artificial Intelligence and Decision Support techniques based on patient data to:
- determine key risks associated with Pancreatic Cancer
- develop predictive models for identified risks
- develop adaptive models for targeted prevention and intervention measures.
iHelp delivers a customisable framework enabling the collection, integration and management of health-related data from various sources (medical records, lifestyle, behaviours, etc) in a standardised structure called Holistic Health Record, aiming at providing a comprehensive view of the status of the patients including a large number of health determinants.
Data in the HHRs are analysed using AI techniques to draw adaptive learning models that are used to provide decision support through early risk predictions, personalised prevention, and intervention measures (alerts, consultations, therapies, screening, etc) delivered via user-centric mobile applications.
The project has received co-funding from the European Union's Horizon 2020 programme - Contract No. 101017441
Financing Framework
Results
AI models for personalised recommendations
Effective recommendations through mobile apps & wearables
HHRs for data modelling and management
Human-centric personalised interfaces
Technologies
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