Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis

BACKGROUND: We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a model that combined deep learning with a multilayer neural network (the DeepSurv model) for pr...

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Detalles Bibliográficos
Autores principales: Yu, Haohui, Huang, Tao, Feng, Bin, Lyu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8881858/
https://www.ncbi.nlm.nih.gov/pubmed/35216571
http://dx.doi.org/10.1186/s12885-022-09217-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8881858/
https://www.ncbi.nlm.nih.gov/pubmed/35216571
http://dx.doi.org/10.1186/s12885-022-09217-9

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