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FHIR-PYrate: a data science friendly Python package to query FHIR servers.
Hosch, René; Baldini, Giulia; Parmar, Vicky; Borys, Katarzyna; Koitka, Sven; Engelke, Merlin; Arzideh, Kamyar; Ulrich, Moritz; Nensa, Felix.
Afiliación
  • Hosch R; Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.
  • Baldini G; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany.
  • Parmar V; Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany. giulia.baldini@uk-essen.de.
  • Borys K; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany. giulia.baldini@uk-essen.de.
  • Koitka S; Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.
  • Engelke M; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany.
  • Arzideh K; Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.
  • Ulrich M; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany.
  • Nensa F; Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.
BMC Health Serv Res ; 23(1): 734, 2023 Jul 06.
Article en En | MEDLINE | ID: mdl-37415138
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Estándar HL7 / Ciencia de los Datos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: BMC Health Serv Res Asunto de la revista: PESQUISA EM SERVICOS DE SAUDE Año: 2023 Tipo del documento: Article País de afiliación: Alemania
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Estándar HL7 / Ciencia de los Datos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: BMC Health Serv Res Asunto de la revista: PESQUISA EM SERVICOS DE SAUDE Año: 2023 Tipo del documento: Article País de afiliación: Alemania