Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation

Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, performance metrics alone do not determine practical ut...

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Detalles Bibliográficos
Autores principales: Roller, Roland, Mayrdorfer, Manuel, Duettmann, Wiebke, Naik, Marcel G., Schmidt, Danilo, Halleck, Fabian, Hummel, Patrik, Burchardt, Aljoscha, Möller, Sebastian, Dabrock, Peter, Osmanodja, Bilgin, Budde, Klemens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Public Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641169/
https://www.ncbi.nlm.nih.gov/pubmed/36388342
http://dx.doi.org/10.3389/fpubh.2022.979448

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641169/
https://www.ncbi.nlm.nih.gov/pubmed/36388342
http://dx.doi.org/10.3389/fpubh.2022.979448

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