h1

h2

h3

h4

h5
h6
000943063 001__ 943063
000943063 005__ 20250515120738.0
000943063 0247_ $$2HSB$$aeva519761
000943063 0247_ $$2ISSN$$a2291-9694
000943063 0247_ $$2SCOPUS$$aSCOPUS:2-s2.0-85124177243
000943063 0247_ $$2WOS$$aWOS:000767356300006
000943063 0247_ $$2datacite_doi$$a10.18154/RWTH-CONV-250610
000943063 0247_ $$2doi$$a10.2196/29978
000943063 0247_ $$2pmid$$apmid:35103612
000943063 037__ $$aRWTH-CONV-250610
000943063 041__ $$aEnglish
000943063 1001_ $$0P:(DE-588)1275758932$$aSchilling, Maximilian$$b0$$eCorresponding author$$urwth
000943063 245__ $$aReduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study$$honline
000943063 260__ $$aToronto$$b[Verlag nicht ermittelbar]$$c2022
000943063 300__ $$a1-14
000943063 3367_ $$00$$2EndNote$$aJournal Article
000943063 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal
000943063 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book
000943063 3367_ $$2BibTeX$$aARTICLE
000943063 3367_ $$2DRIVER$$aarticle
000943063 3367_ $$2DataCite$$aOutput Types/Journal article
000943063 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000943063 591__ $$aGermany
000943063 7001_ $$0P:(DE-82)960953$$aRickmann, Lennart$$b1$$urwth
000943063 7001_ $$0P:(DE-82)033493$$aHutschenreuter, Gabriele$$b2$$urwth
000943063 7001_ $$aSpreckelsen, Cord$$b3
000943063 770__ $$aDecision Support for Health Professionals
000943063 773__ $$0PERI:(DE-600)2798261-0$$a10.2196/29978$$n2$$pe29978$$tJMIR medical informatics$$v10$$x2291-9694
000943063 8564_ $$uhttps://publications.rwth-aachen.de/record/943063/files/943063.pdf$$yOpenAccess
000943063 909CO $$ooai:publications.rwth-aachen.de:943063$$popenaire$$popen_access$$pdriver$$pdnbdelivery$$pVDB
000943063 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-588)1275758932$$aRWTH Aachen$$b0$$kRWTH
000943063 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)960953$$aRWTH Aachen$$b1$$kRWTH
000943063 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)033493$$aRWTH Aachen$$b2$$kRWTH
000943063 9141_ $$y2022
000943063 9151_ $$0StatID:(DE-HGF)0031$$2StatID$$aPeer reviewed article$$x0
000943063 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000943063 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2022-03-02T17:06:49Z
000943063 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJMIR MED INF : 2021$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-03-02T17:06:49Z
000943063 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-03-02T17:06:49Z
000943063 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000943063 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-03-30
000943063 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2023-03-30
000943063 9201_ $$0I:(DE-82)526500-2_20140620$$k526500-2$$lInstitut und Lehrstuhl für Medizinische Informatik$$x0
000943063 9201_ $$0I:(DE-82)9670009_20140620$$k9670009$$lTransfusionsmedizin$$x1
000943063 961__ $$z2023-03-21
000943063 970__ $$aeva519761
000943063 9801_ $$aFullTexts
000943063 980__ $$aI:(DE-82)526500-2_20140620
000943063 980__ $$aI:(DE-82)9670009_20140620
000943063 980__ $$aUNRESTRICTED
000943063 980__ $$aVDB
000943063 980__ $$acontb
000943063 980__ $$ajournal