This paper is in the following e-collection/theme issue:

Proposals (eHealth) (80) Clinical Information and Decision Making (1407) Data Science (270) Decision Support for Health Professionals (1185) Machine Learning (1465) Artificial Intelligence (1371) Tools, Programs and Algorithms (266) Development and Evaluation of Research Methods, Instruments and Tools (481)

Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48892, first published .
NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

Authors of this article:

Matthieu-P Schapranow1 Author Orcid Image ;   Mozhgan Bayat1 Author Orcid Image ;   Aadil Rasheed1 Author Orcid Image ;   Marcel Naik2 Author Orcid Image ;   Verena Graf3 Author Orcid Image ;   Danilo Schmidt3 Author Orcid Image ;   Klemens Budde2 Author Orcid Image ;   Héloïse Cardinal4 Author Orcid Image ;   Ruth Sapir-Pichhadze5 Author Orcid Image ;   Franz Fenninger6 Author Orcid Image ;   Karen Sherwood6 Author Orcid Image ;   Paul Keown6 Author Orcid Image ;   Oliver P Günther7 Author Orcid Image ;   Konstantin D Pandl8 Author Orcid Image ;   Florian Leiser8 Author Orcid Image ;   Scott Thiebes8 Author Orcid Image ;   Ali Sunyaev8 Author Orcid Image ;   Matthias Niemann9 Author Orcid Image ;   Andreas Schimanski9 Author Orcid Image ;   Thomas Klein10 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Aksoy G, Akçay H, Arı Ç, Adar M, Koyun M, Çomak E, Akman S. Predicting graft survival in paediatric kidney transplant recipients using machine learning. Pediatric Nephrology 2024 View