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

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

Pediatric Mitral Valve Surgery: Current Practice from the European Congenital Heart Surgeons Association Congenital Database Analysis.

The Journal of thoracic and cardiovascular surgery·2026
Same author

Towards optimal valve prescription for transcatheter aortic valve replacement (TAVR) surgery: a machine learning approach.

Health care management science·2026
Same author

The Society of Thoracic Surgeons (STS)/World Society for Pediatric and Congenital Heart Surgery (WSPCHS)/European Congenital Heart Surgeons Association (ECHSA) Expert Opinion on the Role of Exercise Testing in Determining Optimal Timing of Pulmonary Valve Replacement in Tetralogy of Fallot.

The Annals of thoracic surgery·2026
Same author

Effectiveness of MIDCAB vs. OPCAB in LAD revascularization procedures in octogenerians: Results from the KROK registry.

Kardiologia polska·2026
Same author

Current availability and status of paediatric cardiac transplantation and mechanical circulatory support in twenty-eight European countries.

European journal of pediatrics·2026
Same author

Adding the Missing Link-Integration of Anesthesia and Perfusion Variables into Europe's Largest Congenital Cardiac Surgery Outcomes Database: Methods and First List of Candidate Variables by an ECHSA-EACTAIC-EBCP Collaboration.

Journal of cardiothoracic and vascular anesthesia·2026

相关实验视频

Updated: Jul 9, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

3.5K

生产性心脏外科 机器学习衍生深度基准测试工具

George E Sarris1, Daisy Zhuo2, Luca Mingardi2

  • 1Athens Heart Surgery Institute, Athens, Greece.

The Annals of thoracic surgery
|December 8, 2023
PubMed
概括

使用最佳分类树 (OCT) 的机器学习现在为先天性心脏手术提供了可操作的医院性能分析. 这个工具评估个体医院的结果与虚拟医院的基准,帮助自我改进.

更多相关视频

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.3K
Author Spotlight: Simulating Pediatric Cardiac Surgery Using a Neonatal Piglet Model
04:55

Author Spotlight: Simulating Pediatric Cardiac Surgery Using a Neonatal Piglet Model

Published on: May 26, 2023

802

相关实验视频

Last Updated: Jul 9, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

3.5K
Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.3K
Author Spotlight: Simulating Pediatric Cardiac Surgery Using a Neonatal Piglet Model
04:55

Author Spotlight: Simulating Pediatric Cardiac Surgery Using a Neonatal Piglet Model

Published on: May 26, 2023

802

科学领域:

  • 心血管外科心血管外科
  • 机器学习 机器学习
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 最佳分类树 (OCT) 之前在预测风险和评估先天性心脏手术中的表现方面表现精确.
  • 这种方法被扩展,在所有程序中提供全面,可解释和可操作的医院绩效分析.

研究的目的:

  • 扩展基于机器学习的OCT方法,用于在先天性心脏手术中进行可解释和可操作的医院绩效分析.
  • 通过使用"虚拟医院"概念,建立个案调整的比较标准.

主要方法:

  • 从欧洲先天性心脏外科医生协会数据库 (1989-2022) 中分析了172,888例先天性心脏手术程序.
  • 开发用于预测医院死亡率 (AUC,0.866),延长机械通风 (AUC,0.851) 和停留时间 (AUC,0.818) 的海外国家和地区模型.
  • 创建一个在线互动工具,通过风险匹配的患者队伍进行医院自我评估.

主要成果:

  • 海外国家和地区的模型建立了与"虚拟医院"总和相对应的个案调整的基准.
  • 在146个中心中,有20.5%的中心在统计学上表现过高,而20.5%的中心表现低于预测的医院死亡率基准.
  • 一个交互式工具揭示了14个医院特定的患者队列,用于绩效评估.

结论:

  • 基于机器学习的OCT基准测试为进行先天性心脏手术的医院提供了自动,个案调整的绩效评估.
  • 分析超越了整体表现,包括特定的,风险匹配的患者队列.
  • 用户可访问的在线平台促进了医院的自我评估和绩效改进.