Predicting postoperative surgical site infection with administrative data: a random forests algorithm
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1471-2288
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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online resource.
- Erschienen in
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Predicting postoperative surgical site infection with administrative data: a random forests algorithm ; volume:21 ; number:1 ; day:28 ; month:8 ; year:2021 ; pages:1-11 ; date:12.2021
BMC medical research methodology ; 21, Heft 1 (28.8.2021), 1-11, 12.2021
- Urheber
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Petrosyan, Yelena
Thavorn, Kednapa
Smith, Glenys
Maclure, Malcolm
Preston, Roanne
Walravan, Carl van
Forster, Alan J.
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
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10.1186/s12874-021-01369-9
- URN
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urn:nbn:de:101:1-2021111307140552047458
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:36 MESZ
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Beteiligte
- Petrosyan, Yelena
- Thavorn, Kednapa
- Smith, Glenys
- Maclure, Malcolm
- Preston, Roanne
- Walravan, Carl van
- Forster, Alan J.
- SpringerLink (Online service)
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