Machine learning-guided prevention and management of low anterior resection syndrome: Development of an XGBoost prediction model and validation via SHAP

Objective: Colorectal cancer is among the top three cancers in terms of incidence and mortality worldwide. Although laparoscopic and robotic-assisted sphincter-preserving surgeries reduce permanent colostomy rates to under 20%, 60%–80% of patients develop postoperative low anterior resection syndrom...

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Principais autores:	Yangtao Pan, Chaojie Huang, Xinjie Zhang, Zhentian Xu, Bingjun Bai, Min Chen, Weifeng Lao
Formato:	Artigo
Idioma:	Inglês
Publicado:	KeAi Communications Co., Ltd. 2025-12-01
Series:	Laparoscopic, Endoscopic and Robotic Surgery
Assuntos:	Low anterior resection syndrome Machine learning SHapley additive exPlanations
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S2468900925000659
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