Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Peripheral Artery Disease III: Interprofessional Care01:27

Peripheral Artery Disease III: Interprofessional Care

659
Peripheral Artery Disease (PAD) is characterized by narrowed arteries that diminish blood flow to the extremities. Effective management of PAD requires an interprofessional approach involving various healthcare professionals. The critical aspects of interprofessional care for PAD patients focus on risk factor modification, drug therapy, exercise therapy, nutrition therapy, critical limb ischemia care, and interventional radiology and surgical procedures.The primary treatment goal for PAD...
659
Peripheral Artery Disease V: Postoperative Nursing Management01:23

Peripheral Artery Disease V: Postoperative Nursing Management

637
During the postoperative period, it is crucial to focus on maintaining circulation, identifying and managing potential complications, and planning for discharge.Nursing AssessmentVital signs monitoring: Regularly monitor vital signs, including blood pressure, heart rate, respiratory rate, and temperature, to detect early signs of complications such as bleeding and infection.Circulation assessment: Monitor pulses, perform Doppler assessments, and check capillary refill, color, temperature, and...
637

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Prospective Head-to-Head Comparison of HER2-Targeted and 18F-FDG PET/CT for Detecting Axillary Lymph Node Metastases Among Newly Diagnosed HER2-Positive and HER2-Low Breast Cancer.

Clinical nuclear medicine·2026
Same author

Dual-Metal MOF-Derived Carbon Fibers Achieve Efficient Polysulfide Anchoring and Conversion Simultaneously in Li-S Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Trefoil Factor 3 as a Biomarker for Peripheral Artery Disease.

Biomolecules·2026
Same author

The Association Between Abnormal Electrocardiogram Findings and the Ankle Brachial Index.

Medicina (Kaunas, Lithuania)·2026
Same author

Navigating the Evolution of Urothelial Carcinoma Treatment: From Chemotherapy to Immunotherapy.

Cancer medicine·2026
Same author

Variations in Sexual Size Dimorphism in Two Anurans Along an Urbanization Gradient in Shanghai: Assessment of Rensch's Rule.

Ecology and evolution·2026

相关实验视频

Updated: May 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

使用机器学习预测下肢开放重血管化后的结果.

Ben Li1,2,3,4, Raj Verma5, Derek Beaton6

  • 1Department of Surgery, University of Toronto, Toronto, Canada.

Scientific reports
|February 5, 2024
PubMed
概括
此摘要是机器生成的。

机器学习准确地预测了30天内主要不良四肢事件的风险或在外周动脉疾病下肢开放再血管化后的死亡. 这种工具可以指导患者的护理,并改善手术结果.

更多相关视频

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

6.8K
Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.4K

相关实验视频

Last Updated: May 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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

6.8K
Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.4K

科学领域:

  • 血管外科 血管外科
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 周围动脉疾病需要下肢开放的再血管化,这是一个具有相当大的外科风险的程序.
  • 目前用于预测这些程序后结果的工具有限,阻碍了有效的风险分层.

研究的目的:

  • 开发和验证机器学习算法,用于预测下肢开放再血管化后的30天不良结果.
  • 确定影响患者结果的关键手术前变量.

主要方法:

  • 利用国家外科质量改进计划的血管数据库 (2011-2021) 来识别患者.
  • 训练了6个机器学习模型,使用37个手术前变量,数据分为训练 (70%) 和测试 (30%) 集.
  • 采用十倍交叉验证来评估模型性能,重点关注30天主要不良肢体事件 (MALE) 或死亡.

主要成果:

  • 包括24,309名患者;9.3%的人经历了30天的MALE或死亡.
  • XGBoost模型表现出卓越的性能,接收器操作特征曲线下的面积为0.93 (0.92-0.94).
  • 该模型表现出良好的校准,布赖尔分数为0.08,表明预测和观察概率之间存在很强的一致性.

结论:

  • 开发了一种高性能机器学习算法,用于预测30天的MALE或下肢开放再血管化后的死亡.
  • 该算法显示出在指导风险缓解策略方面具有显著的临床实用性潜力.
  • 实施可能会改善血管外科手术患者的治疗结果.