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Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control.
Lopes, Marcelle G M; Recktenwald, Steffen M; Simionato, Greta; Eichler, Hermann; Wagner, Christian; Quint, Stephan; Kaestner, Lars.
Afiliação
  • Lopes MGM; Experimental Physics, Saarland University, Saarbrücken, Germany.
  • Recktenwald SM; Cysmic GmbH, Saarbrücken, Germany.
  • Simionato G; Experimental Physics, Saarland University, Saarbrücken, Germany.
  • Eichler H; Experimental Physics, Saarland University, Saarbrücken, Germany.
  • Wagner C; Institute for Clinical and Experimental Surgery, Saarland University, Saarbrücken, Germany.
  • Quint S; Institute of Clinical Hemostaseology and Transfusion Medicine, Saarland University, Saarbrücken, Germany.
  • Kaestner L; Experimental Physics, Saarland University, Saarbrücken, Germany.
Transfus Med Hemother ; 50(3): 163-173, 2023 Jun.
Article em En | MEDLINE | ID: mdl-37408647
Background: "Artificial intelligence" and "big data" increasingly take the step from just being interesting concepts to being relevant or even part of our lives. This general statement holds also true for transfusion medicine. Besides all advancements in transfusion medicine, there is not yet an established red blood cell quality measure, which is generally applied. Summary: We highlight the usefulness of big data in transfusion medicine. Furthermore, we emphasize in the example of quality control of red blood cell units the application of artificial intelligence. Key Messages: A variety of concepts making use of big data and artificial intelligence are readily available but still await to be implemented into any clinical routine. For the quality control of red blood cell units, clinical validation is still required.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article