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

Sigle, Manuel; Faller, Wenke; Heurich, Diana; Zdanyte, Monika; Wunderlich, Robert; Gawaz, Meinrad; Mueller, Karin Anne Lydia; Goldschmied, Andreas

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