29 research outputs found
HochverfĂŒgbare Middlewareplattform fĂŒr ein mobiles Patientenbetreuungssystem
Auswahl, Test und Implementierung einer objektorientierten, hochverfĂŒgbaren Middlewareplattform auf Basis von EJB fĂŒr ein mobiles Patientenbetreuungssystem (Mobtel) unter besonderer BerĂŒcksichtigung konsistenter und persistenter Informationsspeicherung (Java
COVID-19-Forschungsdaten leichter zugĂ€nglich machen â Aufbau einer bundesweiten Informationsinfrastruktur
Public-Health-Forschung, epidemiologische und klinische Studien sind erforderlich, um die COVID-19-Pandemie besser zu verstehen und geeignete MaĂnahmen zu ergreifen. Daher wurden auch in Deutschland zahlreiche Forschungsprojekte initiiert. Zum heutigen Zeitpunkt ist es ob der FĂŒlle an Informationen jedoch kaum noch möglich, einen Ăberblick ĂŒber die vielfĂ€ltigen ForschungsaktivitĂ€ten und deren Ergebnisse zu erhalten. Im Rahmen der Initiative âNationale Forschungsdateninfrastruktur fĂŒr personenbezogene Gesundheitsdatenâ (NFDI4Health) schafft die âTask Force COVID-19â einen leichteren Zugang zu SARS-CoV-2- und COVID-19-bezogenen klinischen, epidemiologischen und Public-Health-Forschungsdaten. Dabei werden die sogenannten FAIR-Prinzipien (Findable, Accessible, Interoperable, Reusable) berĂŒcksichtigt, die eine schnellere Kommunikation von Ergebnissen befördern sollen. Zu den wesentlichen Arbeitsinhalten der Taskforce gehören die Erstellung eines Studienportals mit Metadaten, Erhebungsinstrumenten, Studiendokumenten, Studienergebnissen und Veröffentlichungen sowie einer Suchmaschine fĂŒr Preprint-Publikationen. Weitere Inhalte sind ein Konzept zur VerknĂŒpfung von Forschungs- und Routinedaten, Services zum verbesserten Umgang mit Bilddaten und die Anwendung standardisierter Analyseroutinen fĂŒr harmonisierte QualitĂ€tsbewertungen. Die im Aufbau befindliche Infrastruktur erleichtert die Auffindbarkeit von und den Umgang mit deutscher COVID-19-Forschung. Die im Rahmen der NFDI4Health Task Force COVID-19 begonnenen Entwicklungen sind fĂŒr weitere Forschungsthemen nachnutzbar, da die adressierten Herausforderungen generisch fĂŒr die Auffindbarkeit von und den Umgang mit Forschungsdaten sind.Public health research and epidemiological and clinical studies are necessary to understand the COVID-19 pandemic and to take appropriate action. Therefore, since early 2020, numerous research projects have also been initiated in Germany. However, due to the large amount of information, it is currently difficult to get an overview of the diverse research activities and their results. Based on the âFederated research data infrastructure for personal health dataâ (NFDI4Health) initiative, the âCOVID-19 task forceâ is able to create easier access to SARS-CoV-2- and COVID-19-related clinical, epidemiological, and public health research data. Therefore, the so-called FAIR data principles (findable, accessible, interoperable, reusable) are taken into account and should allow an expedited communication of results. The most essential work of the task force includes the generation of a study portal with metadata, selected instruments, other study documents, and study results as well as a search engine for preprint publications. Additional contents include a concept for the linkage between research and routine data, a service for an enhanced practice of image data, and the application of a standardized analysis routine for harmonized quality assessment. This infrastructure, currently being established, will facilitate the findability and handling of German COVID-19 research. The developments initiated in the context of the NFDI4Health COVID-19 task force are reusable for further research topics, as the challenges addressed are generic for the findability of and the handling with research data.Peer Reviewe
FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research
Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.This research was financially supported by the European Unionâs Horizon 2020 research and innovation programme under the grant agreement No 824666 (project FAIR4Health). Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) âA way of making Europeâ.Peer reviewe
HochverfĂŒgbare Middlewareplattform fĂŒr ein mobiles Patientenbetreuungssystem
Auswahl, Test und Implementierung einer objektorientierten, hochverfĂŒgbaren Middlewareplattform auf Basis von EJB fĂŒr ein mobiles Patientenbetreuungssystem (Mobtel) unter besonderer BerĂŒcksichtigung konsistenter und persistenter Informationsspeicherung (Java
HochverfĂŒgbare Middlewareplattform fĂŒr ein mobiles Patientenbetreuungssystem
Auswahl, Test und Implementierung einer objektorientierten, hochverfĂŒgbaren Middlewareplattform auf Basis von EJB fĂŒr ein mobiles Patientenbetreuungssystem (Mobtel) unter besonderer BerĂŒcksichtigung konsistenter und persistenter Informationsspeicherung (Java
A Topical Collection on ICT for Health Science Research â EFMI Special Topic Conference
FAIRness for FHIR: Towards Making Health Datasets FAIR Using HL7 FHIR.
Medical data science aims to facilitate knowledge discovery assisting in data, algorithms, and results analysis. The FAIR principles aim to guide scientific data management and stewardship, and are relevant to all digital health ecosystem stakeholders. The FAIR4Health project aims to facilitate and encourage the health research community to reuse datasets derived from publicly funded research initiatives using the FAIR principles. The 'FAIRness for FHIR' project aims to provide guidance on how HL7 FHIR could be utilized as a common data model to support the health datasets FAIRification process. This first expected result is an HL7 FHIR Implementation Guide (IG) called FHIR4FAIR, covering how FHIR can be used to cover FAIRification in different scenarios. This IG aims to provide practical underpinnings for the FAIR4Health FAIRification workflow as a domain-specific extension of the GoFAIR process, while simplifying curation, advancing interoperability, and providing insights into a roadmap for health datasets FAIR certification