Identifying clinical phenotypes in extremely low birth weight infants—an unsupervised machine learning approach.
| Publicado en: | European Journal of Pediatrics Vol. 181; no. 3; pp. 1085 - 1098 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155625536&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155625536 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03406199 CR1 jtl: European Journal of Pediatrics issn: 03406199 maglogo: N pubinfo: dt: Mar2022 vid: 181 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155625536 153367915 155625536 155625536 10.1007/s00431-021-04298-3 155625536 ppf: 1085 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying clinical phenotypes in extremely low birth weight infants—an unsupervised machine learning approach. aug: au: Matsushita, Felipe Yu Krebs, Vera Lúcia Jornada de Carvalho, Werther Brunow affil: Department of Pediatrics, Neonatology Division, Faculty of Medicine of the University of São Paulo, Instituto da Criança, Av. Dr. Enéas de Carvalho Aguiar, 647, 05403-000, São Paulo, Brazil sug: subj: Phenotype Infant, Very Low Birth Weight Machine Learning Human Gestational Age Pregnancy Trimester, Second Pregnancy Female Pregnancy Trimester, Third Respiration, Artificial Ventilator Patients Clinical Research Female pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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