FHIR for FAIR - FHIR Implementation Guide
1.0.0 - STU 1 International flag

This page is part of the FHIR for FAIR - FHIR Implementation Guide (v1.0.0: STU 1) based on FHIR v4.3.0. This is the current published version. For a full list of available versions, see the Directory of published versions

Example Library: FAIR4Health portal [Library]

Active as of 2021-10-10

ID: https://example.org/registry/fair4health

FAIR4Health Studies overview

Research studyDescriptionTotal number of sitesTotal number of patientsAI Algorithm
1Identification of multimorbidity patterns and polypharmacy correlation on the risk of mortality in elderly, and demonstrate the reproducibility of research511.486FP Growth
2Develop and pilot an early prediction service for 30-days readmission risk in COPD (Chronic Obstructive Pulmonary Disease) patients34.944Support Vector Machine (SVM), Logistic Regression, Decision Trees, Random Forest, Gradient Boosted Trees

 

Platform access point: https://portal.fair4health.eu/

Common Data model: https://github.com/fair4health/common-data-model

Owner: FAIR4Health Consortium.

FAIR metadata repository: https://github.com/fair4health/metadata

 

 

Related Artifacts

documentationWeb sitePublication SiteFAIR4Health Platform access pointhttps://portal.fair4health.eu/
depends-onModelCommon Data modelCommon models, expressed as HL7 FHIR profiles, used by the FAIR4Health project in the FAIRification processhttps://github.com/fair4health/common-data-model
depends-onMetadataMetadata repositoryFAIR4Health FAIR metadata repositoryhttps://github.com/fair4health/metadata

 

Contents

Identification of multimorbidity patterns and polypharmacy correlation on the risk of mortality in elderly, and demonstrate the reproducibility of research: https://example.org/registry/f4h-study-1 (application/fhir+json)

Develop and pilot an early prediction service for 30-days readmission risk in COPD (Chronic Obstructive Pulmonary Disease) patients: https://example.org/registry/f4h-study-2 (application/fhir+json)

(C) Fair4Health H2020 Project.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 824666.