Description du projet
L’automatisation fondée sur l’IA aide les citoyens à conserver leurs propres données de santé
D’ici 2030, les citoyens européens devraient être en pleine possession de leurs données de santé personnelles. À l’heure actuelle, ces données sont conservées par différents services de chirurgie, hôpitaux ou cliniques, en plus d’être réparties entre différents dispositifs médicaux ou applications de santé personnelles. Une grande quantité d’informations se présente également sous forme papier. Les algorithmes avancés prenant en charge la médecine préventive et personnalisée ne sont donc pas en mesure d’exploiter la majorité de ces données. Dans ce contexte, le projet AIDAVA, financé par l’UE, optimisera l’automatisation de la conservation et de la publication des données hétérogènes, non structurées et structurées, grâce à un assistant virtuel animé par l’IA. Le concept des principes directeurs FAIR, qui exigent que les données soient facilement trouvables, accessibles, interopérables et réutilisables, figure au cœur du projet.
Objectif
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.
Champ scientifique
- 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
Mots‑clés
Programme(s)
Régime de financement
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinateur
6200 MD Maastricht
Pays-Bas