Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME

OBJECTIVE: The purpose of this study was to develop a machine learning model to identify preoperative and intraoperative high-risk factors and to predict the occurrence of permanent stoma in patients after total mesorectal excision (TME). METHODS: A total of 1,163 patients with rectal cancer were in...

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Detalles Bibliográficos
Autores principales: Liu, Yuan, Zhao, Songyun, Du, Wenyi, Tian, Zhiqiang, Chi, Hao, Chao, Cheng, Shen, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Surgery
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079943/
https://www.ncbi.nlm.nih.gov/pubmed/37035560
http://dx.doi.org/10.3389/fsurg.2023.1125875

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079943/
https://www.ncbi.nlm.nih.gov/pubmed/37035560
http://dx.doi.org/10.3389/fsurg.2023.1125875

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