Early Detection of Late Onset Sepsis in Extremely Preterm Infants Using Machine Learning: Towards an Early Warning System.
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| Title: | Early Detection of Late Onset Sepsis in Extremely Preterm Infants Using Machine Learning: Towards an Early Warning System. |
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| Authors: | Garstman, Arno G.1, agarstman@icloud.com, Rodriguez Rivero, Cristian2, agarstman@icloud.com, Onland, Wes3,4, agarstman@icloud.com |
| Source: | Applied Sciences (2076-3417); Aug2023, Vol. 13 Issue 16, p9049, 24p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 170711180 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app13169049 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 9049 Titles: – TitleFull: Early Detection of Late Onset Sepsis in Extremely Preterm Infants Using Machine Learning: Towards an Early Warning System. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Garstman, Arno G. – PersonEntity: Name: NameFull: Rodriguez Rivero, Cristian – PersonEntity: Name: NameFull: Onland, Wes IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 13 – Type: issue Value: 16 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
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