AIDAVA’s second period demonstrated a new level of maturity and a different paradigm in health-data management and data interoperability, based on INDIVIDUAL health records, by validating its innovative combination of AI based curation and data quality enhancement tools. The project’s architecture integrates AI-based curation workflows with ontology-driven FAIRification tools and a knowledge-graph approach, demonstrating that heterogeneous health data can be automatically curated and reused while preserving context and meaning.
AIDAVA prototype demonstrated that Personal Health Knowledge Graphs (PHKG) can successfully be generated from heterogeneous - narrative and structured - data sources coming from hospitals, general practitioners, devices and PROMS to support data interoperability and high-quality toward a truly FAIR personal health record. Knowledge graphs enable embedding, supporting faster and accurate reasoning and retrieval (and RAG) ; knowledge graphs also support scalable data quality checks at individual patient level while all data quality frameworks focus on population level. Full value of a PHKG can only realised if the supporting ontology is easily aligned with other standards; as a consequence the project is developing and validating a Simplified Upper Level Ontology (SULO) providing a solid semantic backbone across standards
In terms of curation and quality enhancement, the project developed several novel machine-learning tools for data extraction, mapping, harmonisation and quality enhancement of structured data. Two are more noteworthy
1. The multilingual AIDAVA-mBERT model represents a major step forward in clinical natural-language processing for under-resourced European languages. Trained on newly annotated corpora in Estonian, Dutch and German, it enables accurate information extraction and entity linking to international terminologies such as SNOMED-CT and FHIR, that can be integrated into the PHKG.
2. AIDAVA implemented automated SHACL-based validation and patient-level quality labelling, enabling continuous measurement of completeness and consistency across datasets.
These combined innovations push the state of the art from isolated FAIRification efforts in specific domains toward FAIRification of the complete individual health, enabling the “curate once, use many times” paradigm. By embedding trust, transparency and multilingual capability into the process, AIDAVA establishes a replicable blueprint for interoperable, high-quality and ethically governed personal health data across Europe.
