Project description
AI-based automation helps citizens curate their personal health data
By 2030, European citizens should be in full possession of their personal health data. Currently, this data is scattered across different clinics, surgeries or hospitals and across medical devices or personal health apps. There is also a lot of information in paper form. Most of the data cannot be used by advanced algorithms supporting preventive and personalised medicine. In this context, the EU-funded AIDAVA project will maximise automation of data curation and publish unstructured and structured, heterogeneous data using a virtual assistant powered by AI. Central to the project is the concept of the FAIR Guiding Principles, which require data to be findable, accessible, interoperable and reusable.
Objective
Integrated, high-quality personal health data (PHD) represents a potential wealth of knowledge for healthcare systems, but there is no reliable conduit for this data to become interoperable, AI-ready and reuse-ready at scale across institutions, at national and EU level. AIDAVA will fill this gap by prototyping and testing an AI-powered, virtual assistant maximizing automation of data curation & publishing of unstructured and structured, heterogeneous data. The assistant includes a backend with a library of AI-based data curation tools and a frontend based on human-AI interaction modules that will help users when automation is not possible, while adapting to users? preferences. The interdisciplinary team of the consortium will develop and test two versions of this virtual assistant with hospitals and emerging personal data intermediaries, around breast cancer patient registries and longitudinal health records for cardio-vascular patients, in three languages. The team will work around four technology pillars: 1) automation of quality enhancement and FAIRification of collected health data, in compliance with EU data privacy; 2) knowledge graphs with ontology-based standards as universal representation, to increase interoperability and portability; 3) deep learning for information extraction from narrative content; and 4) AI-generated explanations during the process to increase users? confidence. By increasing automation of data quality enhancement, AIDAVA will decrease the workload of clinical data stewards; by providing high-quality data, AIDAVA will improve the effectiveness of clinical care and support clinical research. In the long-term, AIDAVA has the potential to democratise participation in data curation & publishing by citizens/patients leading to overall savings in health care costs (through disease prevention, early diagnosis, personalized medicine) and supporting delivery of the European Health Data Space.
Fields of science
- social sciencessociologyindustrial relationsautomation
- natural sciencescomputer and information sciencesknowledge engineering
- medical and health sciencesclinical medicineoncologybreast cancer
- medical and health scienceshealth sciencespersonalized medicine
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
6200 MD Maastricht
Netherlands