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Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.

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Title: Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.
Authors: Dong J; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA. junzi.dong@philips.com., Feng T; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA., Thapa-Chhetry B; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA., Cho BG; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA., Shum T; Department of Information Technology, Phoenix Children's Hospital, Phoenix, AZ, USA., Inwald DP; Paediatric Intensive Care Unit, Addenbrooke's Hospital, Cambridge, UK., Newth CJL; Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA, USA.; Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Vaidya VU; Department of Information Technology, Phoenix Children's Hospital, Phoenix, AZ, USA.
Source: Critical care (London, England) [Crit Care] 2021 Aug 10; Vol. 25 (1), pp. 288. Date of Electronic Publication: 2021 Aug 10.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 9801902 Publication Model: Electronic Cited Medium: Internet ISSN: 1466-609X (Electronic) Linking ISSN: 13648535 NLM ISO Abbreviation: Crit Care Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.
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  Data: <searchLink fieldCode="AU" term="%22Dong+J%22">Dong J</searchLink>; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA. junzi.dong@philips.com.<br /><searchLink fieldCode="AU" term="%22Feng+T%22">Feng T</searchLink>; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA.<br /><searchLink fieldCode="AU" term="%22Thapa-Chhetry+B%22">Thapa-Chhetry B</searchLink>; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA.<br /><searchLink fieldCode="AU" term="%22Cho+BG%22">Cho BG</searchLink>; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA.<br /><searchLink fieldCode="AU" term="%22Shum+T%22">Shum T</searchLink>; Department of Information Technology, Phoenix Children's Hospital, Phoenix, AZ, USA.<br /><searchLink fieldCode="AU" term="%22Inwald+DP%22">Inwald DP</searchLink>; Paediatric Intensive Care Unit, Addenbrooke's Hospital, Cambridge, UK.<br /><searchLink fieldCode="AU" term="%22Newth+CJL%22">Newth CJL</searchLink>; Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA, USA.; Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.<br /><searchLink fieldCode="AU" term="%22Vaidya+VU%22">Vaidya VU</searchLink>; Department of Information Technology, Phoenix Children's Hospital, Phoenix, AZ, USA.
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  Data: <searchLink fieldCode="JN" term="%229801902%22">Critical care (London, England)</searchLink> [Crit Care] 2021 Aug 10; Vol. 25 (1), pp. 288. <i>Date of Electronic Publication: </i>2021 Aug 10.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22BioMed+Central+Ltd%22">BioMed Central Ltd </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>9801902 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1466-609X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2213648535%22">13648535 </searchLink><i>NLM ISO Abbreviation: </i>Crit Care <i>Subsets: </i>MEDLINE
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        Value: 10.1186/s13054-021-03724-0
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        Text: English
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      – TitleFull: Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.
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              Text: 2021 Aug 10
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              Y: 2021
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