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dc.contributor.authorMiles, Jamie
dc.contributor.authorTurner, Janette
dc.contributor.authorJacques, Richard
dc.contributor.authorWilliams, Julia
dc.contributor.authorMason, Suzanne
dc.date.accessioned2020-12-11T13:37:58Z
dc.date.available2020-12-11T13:37:58Z
dc.date.issued2020-10-02
dc.identifier.citationMiles, J., et al, 2020. Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review. Diagnostic and Prognostic Research, Oct. 2, 4-16.en_US
dc.identifier.issn2397-7523
dc.identifier.doi10.1186/s41512-020-00084-1
dc.identifier.urihttp://hdl.handle.net/20.500.12417/946
dc.language.isoenen_US
dc.subjectEmergency Medical Servicesen_US
dc.subjectAmbulance Servicesen_US
dc.subjectEmergency Departmenten_US
dc.subjectMachine Learningen_US
dc.subjectTriageen_US
dc.titleUsing machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic reviewen_US
dc.typeJournal Article/Review
dc.source.journaltitleDiagnostic and Prognostic Researchen_US
dcterms.dateAccepted2020-12-04
rioxxterms.versionNAen_US
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden_US
rioxxterms.licenseref.startdate2020-12-04
refterms.panelUnspecifieden_US
refterms.dateFirstOnline2020-10-02


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