The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond.

Sass, Julian; Bartschke, Alexander; Lehne, Moritz; Essenwanger, Andrea; Rinaldi, Eugenia; Rudolph, Stefanie; Heitmann, Kai U; Vehreschild, Jörg J; von Kalle, Christof; Thun, Sylvia

Sass, Julian; Bartschke, Alexander; Lehne, Moritz; Essenwanger, Andrea; Rinaldi, Eugenia; Rudolph, Stefanie; Heitmann, Kai U; Vehreschild, Jörg J; von Kalle, Christof; Thun, Sylvia.

Afiliación

Sass J; Berlin Institute of Health (BIH), Berlin, Germany.
Bartschke A; Charité - Universitätsmedizin Berlin, Berlin, Germany.
Lehne M; Berlin Institute of Health (BIH), Berlin, Germany.
Essenwanger A; Berlin Institute of Health (BIH), Berlin, Germany.
Rinaldi E; Charité - Universitätsmedizin Berlin, Berlin, Germany.
Rudolph S; Charité - Universitätsmedizin Berlin, Berlin, Germany.
Heitmann KU; HIH - Health Innovation Hub of the Federal Ministry of Health, Berlin, Germany.
Vehreschild JJ; Medical Department 2, Hematology/Oncology, University Hospital of Frankfurt, Frankfurt, Germany.
von Kalle C; Department I for Internal Medicine, University Hospital Cologne, Cologne, Germany.
Thun S; German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany.

BMC Med Inform Decis Mak ; 20(1): 341, 2020 12 21.

Article en En | MEDLINE | ID: mdl-33349259

Asunto(s)

Investigación Biomédica; COVID-19; Conjuntos de Datos como Asunto; Medicina; Consenso; Humanos; Pandemias

Palabras clave

COVID-19; FHIR; Interoperability; Standard dataset

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Investigación Biomédica / Conjuntos de Datos como Asunto / COVID-19 / Medicina Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Alemania

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google