Machine learning predicts emergency physician specialties from treatment strategies for patients suspected of myocardial infarction

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dc.contributor.author Sigle, Manuel
dc.contributor.author Faller, Wenke
dc.contributor.author Heurich, Diana
dc.contributor.author Zdanyte, Monika
dc.contributor.author Wunderlich, Robert
dc.contributor.author Goldschmied, Andreas
dc.contributor.author Gawaz, Meinrad Paul
dc.contributor.author Müller, Karin Anne Lydia
dc.date.accessioned 2024-11-26T10:29:28Z
dc.date.available 2024-11-26T10:29:28Z
dc.date.issued 2024
dc.identifier.issn 0167-5273
dc.identifier.uri http://hdl.handle.net/10900/159118
dc.language.iso en de_DE
dc.publisher Clare : Elsevier Ireland Ltd de_DE
dc.relation.uri http://dx.doi.org/10.1016/j.ijcard.2024.132332 de_DE
dc.subject.ddc 610 de_DE
dc.title Machine learning predicts emergency physician specialties from treatment strategies for patients suspected of myocardial infarction de_DE
dc.type Article de_DE
utue.quellen.id 20241001000000_00773
utue.personen.roh Sigle, Manuel
utue.personen.roh Faller, Wenke
utue.personen.roh Heurich, Diana
utue.personen.roh Zdanyte, Monika
utue.personen.roh Wunderlich, Robert
utue.personen.roh Gawaz, Meinrad
utue.personen.roh Mueller, Karin Anne Lydia
utue.personen.roh Goldschmied, Andreas
dcterms.isPartOf.ZSTitelID International Journal of Cardiology de_DE
dcterms.isPartOf.ZS-Issue Article 132332 de_DE
dcterms.isPartOf.ZS-Volume 413 de_DE
utue.fakultaet 04 Medizinische Fakultät de_DE


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