Cargando…
Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods
BACKGROUND: Predictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF. METHODS: 497 kidney transplantations from deceased donors at the Ghent Univ...
Autores principales: | Decruyenaere, Alexander, Decruyenaere, Philippe, Peeters, Patrick, Vermassen, Frank, Dhaene, Tom, Couckuyt, Ivo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607098/ https://www.ncbi.nlm.nih.gov/pubmed/26466993 http://dx.doi.org/10.1186/s12911-015-0206-y |
Ejemplares similares
-
Prediction of hospital mortality by support vector machine versus logistic regression in patients with a haematological malignancy admitted to the ICU
por: Verplancke, T, et al.
Publicado: (2008) -
Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies
por: Verplancke, T, et al.
Publicado: (2008) -
Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit
por: Ruyssinck, Joeri, et al.
Publicado: (2016) -
Split BiRNN for real-time activity recognition using radar and deep learning
por: Werthen-Brabants, Lorin, et al.
Publicado: (2022) -
Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model
por: Lee, Hyung-Chul, et al.
Publicado: (2018)