Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 38; no. 6; pp. 1357 - 1366 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2024
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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=181200963&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181200963 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Dec2024 vid: 38 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181200963 179081160 10.1007/s10877-024-01206-6 181200963 ppf: 1357 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices. aug: au: Jeong, Heejoon Kim, Donghee Kim, Dong Won Baek, Seungho Lee, Hyung-Chul Kim, Yusung Ahn, Hyun Joo affil: Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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