The Potential of Machine Learning to Predict Early Allograft Dysfunction after Normothermic Machine Perfusion in Liver transplantation.

van Leeuwen, L Leonie; Irizar, Haritz; Kim-Schluger, Leona; Florman, Sander; Akhtar, M Zeeshan

van Leeuwen, L Leonie; Irizar, Haritz; Kim-Schluger, Leona; Florman, Sander; Akhtar, M Zeeshan.

Afiliación

van Leeuwen LL; Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA. Electronic address: Leonie.vanleeuwen@mountsinai.org.
Irizar H; Center for Biostatistics, Department of Population Health Science & Policy, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Kim-Schluger L; Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Florman S; Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Akhtar MZ; Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.

J Hepatol ; 2024 Jul 31.

Article en En | MEDLINE | ID: mdl-39094744

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Hepatol Asunto de la revista: GASTROENTEROLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos

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