Jove
Visualize
联系我们

相关概念视频

Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...

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

The Quest for Automated Pediatric Sleep Scoring: Are We There Yet?

Sleep·2026
Same author

Clinical trials for continuously monitored and updated AI systems.

Nature medicine·2026
Same author

Analysis of differential photoplethysmography signal patterns in apnea and hypopnea.

Physiological measurement·2026
Same author

KTaO<sub>3</sub>-Based Supercurrent Diode.

Nano letters·2026
Same author

Ophthalmology foundation models for clinically significant age macular degeneration detection.

Physiological measurement·2026
Same author

The challenge in finding a simple, accurate, reliable, and affordable tool for the objective assessment of excessive daytime sleepiness (EDS).

Physiological measurement·2025
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

相关实验视频

Updated: Jun 16, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

DUDE:使用变量nEighbors进行生理时间序列分析的深度无监督域适应.

Jeremy Levy1, Noam Ben-Moshe2, Uri Shalit3

  • 1Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and the Faculty of Biomedical Engineering, Technion, Israel Institute of Technology (Technion-IIT), Haifa, Israel.

Physiological measurement
|November 20, 2025
PubMed
概括

使用变量nEighbors (DUDE) 的深度无监督域适应通过解决数据分布转移来改善生理信号的深度学习. 这种新的框架提高了模型在现实世界医疗应用中的通用性.

关键词:
在NNCLR中,它是最重要的.相反的学习学习学习.深域适应的深域适应生理学的时间序列.

更多相关视频

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

相关实验视频

Last Updated: Jun 16, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 医疗信息学 医疗信息学

背景情况:

  • 深度学习的优势在于对心电图等生理信号的数据分布一致.
  • 由于培训和部署数据之间的分布转移,现实世界的部署面临挑战.
  • 不重叠的源代码和目标域支持对模型概括构成了重大障碍.

研究的目的:

  • 引入一个新的框架,使用变量nEighbors (DUDE) 进行深度无监督域适应,用于连续的生理信号分析.
  • 为了应对源和目标数据分布显著不同的地方域调整的挑战.
  • 提高深度学习模型在医疗应用中的通用性.

主要方法:

  • 在源域和目标域之间开发了一种新的对比损失函数.
  • 实施了动态邻居选择策略,根据潜在空间密度适应性地确定邻居.
  • 利用了多个现实世界数据集,具有多种目标领域特征 (人口统计,种族,地理,共同疾病).

主要成果:

  • 与现有的基线方法相比,DUDE 显示出更高的性能.
  • 与视觉表示的近邻对比学习策略相比,实现了高达16%的改进.
  • 在现实世界数据集中验证了有效性,具有显著的域名转移.

结论:

  • DUDE有效地弥合了医疗应用领域的领域适应差距.
  • 该框架显示了提高诊断工具的精度和适应性的潜力.
  • 这项工作通过改进模型通用性,为更强大的患者护理人工智能铺平了道路.