Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study

Authors: Marc Raynaud, PhD, Olivier Aubert, MD, Gillian Divard, MD, Peter P Reese, ProfMD, Nassim Kamar, ProfMD, Daniel Yoo, MPH, Chen-Shan Chin, PhD, Élodie Bailly, MD, Matthias Buchler, ProfMD, Marc Ladrière, ProfMD, Moglie Le Quintrec, ProfMD, Michel Delahousse, ProfMD, Ivana Juric, MD, Nikolina Basic-Jukic, ProfMD, Marta Crespo, ProfMD, Helio Tedesco Silva, Jr, ProfMD, Kamilla Linhares, MD, Maria Cristina Ribeiro de Castro, ProfMD, Gervasio Soler Pujol, ProfMD, Jean-Philippe Empana, ProfMD, Camilo Ulloa, ProfMD, Enver Akalin, ProfMD, Georg Böhmig, ProfMD, Edmund Huang, MD, Mark D Stegall, ProfMD, Andrew J Bentall, ProfMD, Robert A Montgomery, ProfMD, Stanley C Jordan, ProfMD, Rainer Oberbauer, ProfMD, Dorry L Segev, ProfMD, John J Friedewald, ProfMD, Xavier Jouven, ProfMD, Christophe Legendre, ProfMD, Carmen Lefaucheur, ProfMD, Alexandre Loupy, ProfMD

Source: The Lancet: Digital Health, Vol 3, Iss 12, Pp e795-e805 (2021)

Subject Terms: Computer applications to medicine. Medical informatics, R858-859.7

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2589750021002090; https://doaj.org/toc/2589-7500

Access URL: https://doaj.org/article/cb9de491fcba4238b25f7cfc45add677