机器学习和名图是预测食道癌患者手术后营养不良风险的准确工具

Zhenmeng Lin1,2, Hao He1, Mingfang Yan2

  • 1Department of Thoracic Oncology Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital, Fuzhou, China.

PubMed
概括

食道癌手术后的术后营养不良是常见的. 机器学习和名图有效预测这种风险,帮助患者护理.

相关概念视频

Enteral Nutrition II: Nasointestinal and Gastrostomy Feeding01:15

Enteral Nutrition II: Nasointestinal and Gastrostomy Feeding

Enteral nutrition encompasses various methods of delivering nutrition directly to the gastrointestinal (GI) tract, bypassing traditional oral intake. It is particularly beneficial for patients who cannot eat by mouth but have a functioning digestive system. Key methods include nasointestinal feeding, gastrostomy, and jejunostomy, each suited to different clinical scenarios based on the patient's needs and condition.
Nasointestinal Feeding
Nasointestinal feeding involves placing a tube...
303
Enteral Nutrition I: Orogastric and Nasogastric Feeding01:26

Enteral Nutrition I: Orogastric and Nasogastric Feeding

Enteral nutrition delivers nutrients directly to the stomach or small intestine through a tube. This method is appropriate for patients who cannot eat but still have a functioning digestive system. It is also beneficial for individuals with swallowing difficulties, anorexia, malabsorption, or those who have undergone gastrointestinal (GI) surgery.
Orogastric (OG) and nasogastric (NG) feeding are two standard methods used for enteral nutrition. Enteral nutrition is often preferred over...
479
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
457