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Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data.

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Title: Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data.
Authors: Wadhwani SI; Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio., Hsu EK; University of Washington School of Medicine, Seattle Children's Hospital, Seattle, Washington., Shaffer ML; University of Washington, Seattle, Washington., Anand R; EMMES Corporation, Rockville, Maryland., Ng VL; Transplant and Regenerative Medicine Center, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada., Bucuvalas JC; Icahn School of Medicine at Mount Sinai, Kravis Children's Hospital, New York, New York.
Source: Pediatric transplantation [Pediatr Transplant] 2019 Nov; Vol. 23 (7), pp. e13554. Date of Electronic Publication: 2019 Jul 22.
Publication Type: Journal Article
Journal Info: Publisher: Munksgaard Country of Publication: Denmark NLM ID: 9802574 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1399-3046 (Electronic) Linking ISSN: 13973142 NLM ISO Abbreviation: Pediatr Transplant Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Wadhwani+SI%22">Wadhwani SI</searchLink>; Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.<br /><searchLink fieldCode="AU" term="%22Hsu+EK%22">Hsu EK</searchLink>; University of Washington School of Medicine, Seattle Children's Hospital, Seattle, Washington.<br /><searchLink fieldCode="AU" term="%22Shaffer+ML%22">Shaffer ML</searchLink>; University of Washington, Seattle, Washington.<br /><searchLink fieldCode="AU" term="%22Anand+R%22">Anand R</searchLink>; EMMES Corporation, Rockville, Maryland.<br /><searchLink fieldCode="AU" term="%22Ng+VL%22">Ng VL</searchLink>; Transplant and Regenerative Medicine Center, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.<br /><searchLink fieldCode="AU" term="%22Bucuvalas+JC%22">Bucuvalas JC</searchLink>; Icahn School of Medicine at Mount Sinai, Kravis Children's Hospital, New York, New York.
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  Data: <searchLink fieldCode="JN" term="%229802574%22">Pediatric transplantation</searchLink> [Pediatr Transplant] 2019 Nov; Vol. 23 (7), pp. e13554. <i>Date of Electronic Publication: </i>2019 Jul 22.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Munksgaard%22">Munksgaard </searchLink><i>Country of Publication: </i>Denmark <i>NLM ID: </i>9802574 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1399-3046 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2213973142%22">13973142 </searchLink><i>NLM ISO Abbreviation: </i>Pediatr Transplant <i>Subsets: </i>MEDLINE
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        Value: 10.1111/petr.13554
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      – Code: eng
        Text: English
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      – TitleFull: Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data.
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              Text: 2019 Nov
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              Y: 2019
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