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相关概念视频

Pathophysiology of Vomiting01:22

Pathophysiology of Vomiting

270
Vomiting is a complex physiological response to expel harmful or irritating substances from the body. It's a defensive mechanism triggered by stimuli like poisons, microbial toxins, cytotoxic drugs, and mechanical abdominal distension. The process is centrally coordinated by the vomiting (or emetic) center located in the medulla of the brainstem. This area, rich in muscarinic M1, histamine H1, neurokinin 1 (NK1), and serotonin 5-HT3 receptors, coordinates the act of vomiting through...
270
Chemotherapy-Induced Nausea and Vomiting: 5-HT3 Receptor Antagonists01:27

Chemotherapy-Induced Nausea and Vomiting: 5-HT3 Receptor Antagonists

170
5-HT3 receptor antagonists, such as dolasetron, granisetron (Kytril), ondansetron (Zofran), and palonosetron (Axoli), are crucial in managing chemotherapy-induced nausea and vomiting (CINV) and postoperative nausea. These drugs selectively block 5-HT3 receptors in the visceral vagal and spinal afferent nerves, chemoreceptor trigger zone, and the vomiting center. They have a rapid onset of action and can be given as a single dose before chemotherapy. Ondansetron and granisetron, in particular,...
170
Chemotherapy-Induced Nausea and Vomiting: Neurokinin-1 Receptor Antagonists01:28

Chemotherapy-Induced Nausea and Vomiting: Neurokinin-1 Receptor Antagonists

145
Neurokinin 1 (NK1) receptors are distributed across the GI tract, vagal afferents, and key CNS regions including the central vomiting center and chemoreceptor trigger zone (CTZ) Chemotherapy agents stimulate enterochromaffin cells in the gastrointestinal (GI) tract to release large amounts of substance P (SP). SP is a neuropeptide released by specific sensory nerves in response to many different stressors, including those in the GI mucosa affected by chemotherapy.  SP binds and activates...
145

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Updated: Jun 20, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

使用机器学习预测术后恶心和吐:一个模型开发和验证研究.

Maxim Glebov1, Teddy Lazebnik2,3, Maksim Katsin4

  • 1Department of Anesthesiology, Sheba Medical Center, Derech Sheba 2, Ramat Gan, 52621, Israel. hlebau@gmail.com.

BMC anesthesiology
|March 21, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型显著改善了术后恶心和吐 (PONV) 的预测. 与传统方法相比,这些先进的工具提供了更好的患者护理和结果.

关键词:
临床机器学习 临床机器学习个性化药物是个性化的药物.手术后恶心和吐的预测.

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Last Updated: Jun 20, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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Published on: August 16, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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科学领域:

  • 麻醉学和外科手术期间的医学.
  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能

背景情况:

  • 手术后恶心和吐 (PONV) 是全身麻醉后的常见并发症,导致患者痛苦.
  • 现有的PONV预测得分缺乏令人满意的准确性.
  • 需要改善早期和晚期PONV的预后模型.

研究的目的:

  • 开发和验证基于机器学习的预后模型,用于预测早期和延迟PONV.
  • 为了实现比目前的临床分数更高的预测性能.
  • 增强个性化的患者护理和术后期的结果.

主要方法:

  • 对35,003名在全身麻醉下接受手术的成年患者进行了回顾性分析.
  • 使用k-fold交叉验证开发一个整体机器学习模型.
  • 数据分为培训和测试集,保持社会人口统计特征.

主要成果:

  • 早期PONV发生在3.82%的患者中,延迟PONV发生在18.80%的患者中.
  • 提出的模型在早期的PONV中达到83.6%的准确性,在延迟的PONV中达到74.8%.
  • 性能比Koivuranta得分高出13.0% (早期) 和10.4% (延迟),通过特征重要性分析验证.

结论:

  • 机器学习模型提供了对PONV的卓越预测.
  • 这些模型促进了个性化的术后护理策略.
  • 改善PONV预测导致更好的患者结果和满意度.