An Interoperable pan-European federated repository of clinical data and medical images that allows sharing data in compliance with legal, ethical, privacy and security requirements, for AI-related training and experimentation; the repository will rely on federated data storage and will operated on a Federated Learning basis, abiding to the highest data privacy and security standards. It will also offer High Performance Computing-as-a-service, where necessary, thereby allowing for cost-effective performance of computationally intensive processing without the need for maintaining expensive equipment.
Interested in sharing medical images and/or clinical data to contribute to INCISIVE’s repository? Register your interest and we’ll be in touch with more details.
These are the INCISIVE Goals:
The INCISIVE project decided that the data upload process will be carried out using an Excel file that indicates the terms to be reported and that each Data Provider will upload to their Federated Node or in the Central Node anonymously indicating a patientID. This mechanism makes it possible to send data from different patients from the same data provider with the same file. Four file templates have been generated, one for each cancer type with different clinical data, for this reason it was decided to create four different HL7 FHIR messages, one for each cancer type.
INCISIVE clinical data was semantically encoded in a way that gets everyone within a system to speak the same language and understand the meaning of the data. Templates contain input fields about clinical elements and laboratory elements; it was distinguished between these two types of data and used SNOMED CT or LOINC according to the medical concepts.
With DICOM and HL7 FHIR, INCISIVE achieves a Common Data Model (CDM) that allows receiving, storing, and processing information from multiple sources, multiple data providers, as a one standard way.
For more detail, see INCISIVE Standardization Suggestions.