Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining.
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| Title: | Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining. |
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| Authors: | Gurazada, Sai Gayatri1 (AUTHOR) sgur0006@student.monash.edu, Gao, Shijia1 (AUTHOR) caddie.gao@monash.edu, Burstein, Frada1 (AUTHOR), Buntine, Paul2 (AUTHOR) paul.buntine@easternhealth.org.au |
| Source: | Sensors (14248220). Jul2022, Vol. 22 Issue 13, p4968-N.PAG. 15p. |
| Database: | Academic Search Ultimate |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: asn DbLabel: Academic Search Ultimate An: 157994498 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=157994498 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s22134968 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 4968 Titles: – TitleFull: Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gurazada, Sai Gayatri – PersonEntity: Name: NameFull: Gao, Shijia – PersonEntity: Name: NameFull: Burstein, Frada – PersonEntity: Name: NameFull: Buntine, Paul IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 22 – Type: issue Value: 13 Titles: – TitleFull: Sensors (14248220) Type: main |
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