Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/31964
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dc.contributor.authorKishore, Kartik-
dc.contributor.authorBraitberg, George-
dc.contributor.authorHolmes, Natasha E-
dc.contributor.authorBellomo, Rinaldo-
dc.date2023-
dc.date.accessioned2023-01-24T03:01:17Z-
dc.date.available2023-01-24T03:01:17Z-
dc.date.issued2023-08-
dc.identifier.citationEmergency medicine Australasia: EMA 2023-08; 35(4)en_US
dc.identifier.issn1742-6723-
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/31964-
dc.description.abstractThe early prediction of hospital admission is important to ED patient management. Using available electronic data, we aimed to develop a predictive model for hospital admission.en_US
dc.language.isoeng-
dc.subjectNEATen_US
dc.subjectSHAPen_US
dc.subjectAdmission predictionen_US
dc.subjectMachine learningen_US
dc.titleEarly prediction of hospital admission of emergency department patients.en_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleEmergency Medicine Australasia : EMAen_US
dc.identifier.affiliationData Analytics Research and Evaluation (DARE) Centreen_US
dc.identifier.affiliationEmergencyen_US
dc.identifier.doi10.1111/1742-6723.14169en_US
dc.type.contentTexten_US
dc.identifier.orcid0000-0002-4013-3364en_US
dc.identifier.orcid0000-0001-8501-4054en_US
dc.identifier.orcid0000-0002-1650-8939en_US
dc.identifier.pubmedid36634916-
local.name.researcherBellomo, Rinaldo
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
crisitem.author.deptData Analytics Research and Evaluation (DARE) Centre-
crisitem.author.deptInfectious Diseases-
crisitem.author.deptData Analytics Research and Evaluation (DARE) Centre-
crisitem.author.deptIntensive Care-
crisitem.author.deptData Analytics Research and Evaluation (DARE) Centre-
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