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

Pulse rhythm01:30

Pulse rhythm

770
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
770

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

Deep learning-based estimation of lung collapse in electrical impedance tomography: a simulation and phantom study.

Physiological measurement·2026
Same author

Novel Wearable-Based Real-Time Temperature Monitoring in Hospitals for Febrile Adverse Events in Patients with Cancer: A Prospective Feasibility Study.

Sensors (Basel, Switzerland)·2025
Same author

Correction: Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.

Journal of medical Internet research·2024
Same author

Efficacy of Wearable Single-Lead ECG Monitoring during Exercise Stress Testing: A Comparative Study.

Sensors (Basel, Switzerland)·2024
Same author

Correction: Three-Day Monitoring of Adhesive Single-Lead Electrocardiogram Patch for Premature Ventricular Complex: Prospective Study for Diagnosis Validation and Evaluation of Burden Fluctuation.

Journal of medical Internet research·2024
Same author

Three-Day Monitoring of Adhesive Single-Lead Electrocardiogram Patch for Premature Ventricular Complex: Prospective Study for Diagnosis Validation and Evaluation of Burden Fluctuation.

Journal of medical Internet research·2024

相关实验视频

Updated: Jun 12, 2025

Author Spotlight: A Unique Mouse Model of Asphyxia-Induced Cardiac Arrest
07:18

Author Spotlight: A Unique Mouse Model of Asphyxia-Induced Cardiac Arrest

Published on: April 14, 2023

1.7K

使用可解释的机器学习在重症监护室早期预测心脏骤停:回顾性研究.

Yun Kwan Kim1,2, Won-Doo Seo1, Sun Jung Lee1

  • 1Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea.

Journal of medical Internet research
|September 17, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的整体方法,用于预测重症监护室 (ICU) 中的心脏骤停 (CA),提高了跨不同患者群体和ICU类型的准确性和概括性. 可解释模型有助于临床医生在早期干预中获得更好的患者结果.

关键词:
具有成本敏感性的学习.早期心脏骤停预警系统电气医疗记录 电气医疗记录组合学习组合学习可解释的临床决策支持系统.伪实时评估伪实时评估

更多相关视频

Standardized Model of Ventricular Fibrillation and Advanced Cardiac Life Support in Swine
05:36

Standardized Model of Ventricular Fibrillation and Advanced Cardiac Life Support in Swine

Published on: January 30, 2020

7.6K
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

相关实验视频

Last Updated: Jun 12, 2025

Author Spotlight: A Unique Mouse Model of Asphyxia-Induced Cardiac Arrest
07:18

Author Spotlight: A Unique Mouse Model of Asphyxia-Induced Cardiac Arrest

Published on: April 14, 2023

1.7K
Standardized Model of Ventricular Fibrillation and Advanced Cardiac Life Support in Swine
05:36

Standardized Model of Ventricular Fibrillation and Advanced Cardiac Life Support in Swine

Published on: January 30, 2020

7.6K
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

科学领域:

  • 关键护理医学 关键护理医学
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 心脏骤停 (CA) 是重症监护室 (ICU) 中死亡的一个重要原因.
  • 现有的CA预测模型经常在不同患者群体和ICU亚型中进行概括和验证.
  • 患者异质性对准确和及时的CA预测构成了挑战.

研究的目的:

  • 开发一种临床上可解释的整体方法,用于在24小时内预测CA.
  • 确保模型在各种患者群体和ICU亚型中的准确性和通用性.
  • 为实时临床采用和患者独立评估提供可解释的结果.

主要方法:

  • 来自MIMIC-IV和eICU-CRD数据库的数据的回顾性分析.
  • 在12小时窗口内使用生命体征,多分辨率统计分析和吉尼指数进行特征提取.
  • 开发一个TabNet模型,使用特征选和成本敏感学习,通过交叉验证和交叉数据集方法验证.

主要成果:

  • 拟议的组合方法在不同人群和ICU亚型中表现出比传统方法更好的性能.
  • 该模型在MIMIC-IV和eICU-CRD数据集中实现了比基线模型更高的准确性.
  • 外部验证证实了该模型强大的概括能力,优于基线模型.

结论:

  • 这种新的框架为CA在各种ICU环境中提供了稳定的预测能力.
  • 可解释的结果突出了CA和非CA组之间的统计差异,有助于临床决策.
  • 经过验证的CA预测系统支持早期临床干预,并有可能进行数字健康临床试验.