State of the art
EDITH-CSA Knowledge Base
EDITH-CSA developed an interactive knowledge base leveraging OpenAI GPT models. The system allows to ask questions about the Virtual Human Twin in natural language, and receive pertinent answers on the basis of the relevant literature. The tool is available for testing at the following page
Current standards and guidelines
To know more about the FAIR sharing standards collection for EDITH click here.
To construct Virtual Human Twins from health data complex multi-scale and multi-organ modelling processes must be brought together. This calls for data that are highly interoperable to integrate them into the modelling processes, but also for interoperable models and standardized output, as well as quality assurance of the models.
To this end, standards for data formatting and data input into the models (ISO 20691) and for data publication (ISO/TR 3985) are defined, as well as for data preparation and the modelling process itself (ISO/TS 9491-1). To standardize the description of the data and models, they should be semantically annotated by using domain-specific ontology terms according to minimum reporting guidelines. Provenance information for each data handling and processing step should be captured in a standardized way (ISO 23494 series).
To assess the quality of the models and their output, the model credibility and modelling results should be validated (ISO/TS 9491-1 and ASME V&V40). Where applicable, model exchange should do, based on model standards like SBML or CellML, which are defined by COMBINE (Computational Modelling in Biology Network) and other scientific standardization initiatives.
To make the data and models interoperable it is suggested to use the Biomedical Research Integrated Group (BRIDG) domain model (ISO 14199) and the HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) framework. For phenotypic data exchange the phenopackets standard (ISO 4454), defined by GA4GH (Global Alliance for Genomics and Health), can be used to link detailed phenotypic descriptions with information about disease, patient, diagnosis, treatments, and genetic information.