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

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

904
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
904

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

Beyond Water, Rest, and Shade: Advancing Farmworker Heat Protection Through Partnership.

American journal of industrial medicine·2026
Same author

Impact of Imaging Protocols on Thermal Detection of Pressure Injuries: Threshold versus Deep Learning Across Skin Tones.

medRxiv : the preprint server for health sciences·2026
Same author

End-of-Life Care of Young Adult Solid Organ Transplant Recipients Compared to Nontransplant Recipients.

Progress in transplantation (Aliso Viejo, Calif.)·2026
Same author

From Biosignals to Bedside: A Review of Real-Time Edge Machine Learning for Wearable Health Monitoring.

Bioengineering (Basel, Switzerland)·2026
Same author

Sweating the Details: A Reflective Pause in our Procedural Workflow.

Cardiac electrophysiology clinics·2026
Same author

Chief Complaint: Anxiety/Palpitations: The Insidious and Steadfast Problem of Supraventricular Tachyarrhythmias.

JACC. Case reports·2026

相关实验视频

Updated: Jun 5, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

1.6K

基于机器学习的高风险模式的识别在心房移除结果中的心房动.

Mustapha Oloko-Oba1, Yijun Liu2,3, Kathryn Wood1

  • 1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA 30322.

medRxiv : the preprint server for health sciences
|December 9, 2024
PubMed
概括

机器学习识别了患者子组和诊断代码,影响了心房动 (AF) 除成功. 这促进了个性化风险评估,以改善治疗结果.

关键词:
AF 消去 消去 消去在心房动的情况下,心房动.集群集成是指集群集成.诊断码是指诊断码中的一个.电子健康记录电子健康记录机器学习 机器学习

更多相关视频

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

411
High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
09:17

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

14.7K

相关实验视频

Last Updated: Jun 5, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

1.6K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

411
High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
09:17

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

14.7K

科学领域:

  • 心脏病学 心脏病学
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 心房动 (AF) 是一种常见的心律失常症,其全球影响越来越大.
  • 目前的AF切除成功率各不相同,需要改进预测方法.
  • 现有的预测器缺乏细粒度以捕捉患者异质性.

研究的目的:

  • 根据AF切除结果确定患者子组.
  • 为了发现与AF切除失败相关的诊断代码.
  • 利用数据驱动的方法来提高程序成功预测.

主要方法:

  • 应用机器学习集群与EHR数据的必须链接/不能链接约束.
  • 利用统计分析,包括千平方测试,以确定重要的诊断代码.
  • 发现了影响程序成功或失败的特定患者因素.

主要成果:

  • 在检查的145个中,确定了13个重要的诊断代码.
  • 根据对程序结果的影响,将代码分为四个风险组.
  • 突出了心血管,全身,抗凝血和一般健康因素的影响.

结论:

  • 强调了心血管和非心血管因素在AF切除结果中的重要性.
  • 建议进行全面的程序前评估,以进行个性化风险评估.
  • 证明了机器学习在推进AF切除个性化护理方面的实用性.