The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests

The aim of this study was to develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning. SUMMARY BACKGROUND DATA: For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This...

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Detalles Bibliográficos
Autores principales: Rahman, Saqib A., Walker, Robert C., Maynard, Nick, Nigel Trudgill, Crosby, Tom, Cromwell, David A., Underwood, Timothy J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831040/
https://www.ncbi.nlm.nih.gov/pubmed/33630434
http://dx.doi.org/10.1097/SLA.0000000000004794

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831040/
https://www.ncbi.nlm.nih.gov/pubmed/33630434
http://dx.doi.org/10.1097/SLA.0000000000004794

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