The Role of HL7 FHIR in the European Project GATEKEEPER.

The European Project GATEKEEPER aims to develop a platform and marketplace to ensure a healthier independent life for the aging population. In this platform the role of HL7 FHIR is to provide a shared logical data model to collect data in heterogeneous living, which can be used by AI Service and the Gatekeeper HL7 FHIR Implementation Guide was created for this purpose. Independent pilots used this IG and illustrate the impact of the approach, benefit, value, and scalability.

Design and Adoption of a FHIR IG to Support a Telemonitoring Environment in Gatekeeper Project.

Telemedicine can provide benefits in patient affected by chronic diseases or elderly citizens as part of standard routine care supported by digital health. The GATEKEEPER (GK) Project was financed to create a vendor independent platform to be adopted in medical practice and to demonstrate its effect, benefit value, and scalability in 8 connected medical use cases with some independent pilots. This paper, after a description of the GK platform architecture, is focused on the creation of a FHIR (Fast Healthcare Interoperability Resource) IG (Implementation Guide) and its adoption in specific use cases. The final aim is to combine conventional data, collected in the hospital, with unconventional data, coming from wearable devices, to exploit artificial intelligence (AI) models designed to evaluate the effectiveness of a new parsimonious risk prediction model for Type 2 diabetes (T2D).

Experience from the Development of HL7 FHIR IG for Gatekeeper Project.

The GATEKEEPER (GK) Project was financed by the European Commission to develop a platform and marketplace to share and match ideas, technologies, user needs and processes to ensure a healthier independent life for the aging population connecting all the actors involved in the care circle. In this paper, the GK platform architecture is presented focusing on the role of HL7 FHIR to provide a shared logical data model to be explored in heterogeneous daily living environments. GK pilots are used to illustrate the impact of the approach, benefit value, and scalability, suggesting ways to further accelerate progress.

A Preliminary Investigation of Reversing RML: From an RDF dataset to its Column-Based data source.

BACKGROUND: A large percentage of scientific data with tabular structure are published on the Web of Data as interlinked RDF datasets. When we come to the issue of long-term preservation of such RDF-based digital objects, it is important to provide full support for reusing them in the future. In particular, it should include means for both players who have no familiarity with RDF data model and, at the same time, who by working only with the native format of the data still provide sufficient information. To achieve this, we need mechanisms to bring the data back to their original format and structure. NEW INFORMATION: In this paper, we investigate how to perform the reverse process for column-based data sources. In particular, we devise an algorithm, RML2CSV, and exemplify its implementation in transforming an RDF dataset into its CSV tabular structure, through the use of the same RML mapping document that was used to generate the set of RDF triples. Through a set of content-based criteria, we attempt a comparative evaluation to measure the similarity between the rebuilt CSV and the original one. The results are promising and show that, under certain assumptions, RML2CSV reconstructs the same data with the same structure, offering more advanced digital preservation services.