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

807
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...
807
Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

569
Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
569

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

County-Level Association Between Social Vulnerability and Rheumatoid Arthritis-Related Mortality in the United States.

Medical sciences (Basel, Switzerland)·2026
Same author

Discriminative Index: A Novel Indicator for Evaluating Machine Learning Algorithms in Laboratory Medicine.

Diagnostics (Basel, Switzerland)·2026
Same author

Challenges of Intra-Aortic Balloon Pump and Extracorporeal Membrane Oxygenation in Cardiogenic Shock: Real-World Outcomes.

Acta Cardiologica Sinica·2026
Same author

Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement.

Heart (British Cardiac Society)·2026
Same author

Distinct gut microbiota signatures and predicted lipid metabolism pathways in Taiwanese patients with acute versus chronic coronary syndromes.

Journal of the Formosan Medical Association = Taiwan yi zhi·2026
Same author

The First JenaValve Trilogy System Transcatheter Aortic Valve Replacement for Pure Severe Native Aortic Valve Regurgitation in Taiwan: A Case Report.

Acta Cardiologica Sinica·2026
Same journal

The Need for Demonstrated Clinical Translational Evidence in Submissions to the IEEE Journal of Translational Engineering in Health and Medicine.

IEEE journal of translational engineering in health and medicine·2026
Same journal

Accuracy of Quantifying Hypotension During Surgery Using Physiological Sensor Data.

IEEE journal of translational engineering in health and medicine·2026
Same journal

Analyzing Gait Pattern Associated With Neuropsychiatric Symptoms in Parkinson's Disease by a Comprehensive Approach.

IEEE journal of translational engineering in health and medicine·2026
Same journal

Multimodal Patient-Specific Identification of Atrial Flutter Circuits From ECG Time Series Using Explainable Machine Learning.

IEEE journal of translational engineering in health and medicine·2026
Same journal

Innovative Wearable Platform for Synchronized Biosignals Acquisition: A Proof of Concept in a Cuff-Less Blood Pressure Monitoring Case Study.

IEEE journal of translational engineering in health and medicine·2026
Same journal

Development of a Realistic Physical Phantom for Laparoscopic and Robotic-Assisted Sacrocolpopexy Training and Associated.

IEEE journal of translational engineering in health and medicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 9, 2025

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

自主监督的基于学习的一般实验室进展预训练模型用于心血管事件检测.

Li-Chin Chen1, Kuo-Hsuan Hung1, Yi-Ju Tseng2

  • 1Research Center for Information Technology InnovationAcademia Sinica Taipei 11529 Taiwan.

IEEE journal of translational engineering in health and medicine
|December 7, 2023
PubMed
概括
此摘要是机器生成的。

这项研究使用自我监督学习来创建心血管疾病的概括实验室进展模型. 转移该模型显著改善了特定心血管事件的检测.

关键词:
心血管疾病的心血管疾病.心脏代谢疾病心脏代谢疾病疾病的进展 疾病的进展实验室检查 实验室检查列车前模型模型代表性学习学习学习自主监督学习学习时间序列数据数据时间序列数据转移学习转移学习

更多相关视频

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

469
Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.5K

相关实验视频

Last Updated: Jul 9, 2025

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
Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

469
Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.5K

科学领域:

  • 机器学习在医疗保健中的应用.
  • 生物医学数据分析
  • 翻译性的生物信息学

背景情况:

  • 在疾病护理中的患者数据带来了诸如数据不规则性和稀疏性等挑战,特别是在纵向研究中.
  • 罕见疾病病例通常具有有限的患者数据和情节性观察,阻碍了预测建模.
  • 机器学习 (ML) 提供了利用患者数据的潜力,但需要强大的模型来处理数据的复杂性.

研究的目的:

  • 为心血管 (CV) 实验室标志物开发一种使用自我监督学习 (SSL) 的通用实验室进度 (GLP) 模型.
  • 从GLP模型中转移知识,对流行CV病例进行培训,以帮助检测特定的CV事件.
  • 通过知识转移,克服罕见或特定疾病病例的数据限制.

主要方法:

  • 采用GLP模型的两阶段培训方法,结合插入数据以提高SSL性能.
  • 利用自主监督学习 (SSL) 预训练GLP模型在流行心血管病例中的六个常见实验室标记.
  • 转移了预训练的GLP模型,用于目标血管再血管化 (TVR) 检测的具体任务.

主要成果:

  • 两阶段的培训方法在纯SSL性能上显著改善.
  • 一般化实验室进度 (GLP) 模型显示出强大的下游任务的可转移性.
  • 在GLP处理后,心血管事件检测的分类准确性从0.63提高到0.90,在所有指标上都有明显的优势.

结论:

  • 该研究成功地通过在队列之间传输患者进展数据来证明翻译工程.
  • 疾病进展模型的可转移性优化了检查和治疗策略,改善了患者的预后.
  • 这种方法有望应用于其他疾病,提高使用常见实验室参数的诊断和预后能力.