Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative

INTRODUCTION: Several factors affect the survival of End Stage Kidney Disease (ESKD) patients on dialysis. Machine learning (ML) models may help tackle multivariable and complex, often non‐linear predictors of adverse clinical events in ESKD patients. In this study, we used advanced ML method as wel...

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Detalles Bibliográficos
Autores principales: Chaudhuri, Sheetal, Larkin, John, Guedes, Murilo, Jiao, Yue, Kotanko, Peter, Wang, Yuedong, Usvyat, Len, Kooman, Jeroen P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
ORIGINAL ARTICLES
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10100028/
https://www.ncbi.nlm.nih.gov/pubmed/36403633
http://dx.doi.org/10.1111/hdi.13053

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10100028/
https://www.ncbi.nlm.nih.gov/pubmed/36403633
http://dx.doi.org/10.1111/hdi.13053

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