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

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

592
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
592
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

150
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
150
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.5K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

129
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
129

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

Trends in the Proportion of Korean Adolescents Engaged in Strength Training, 2016-2025.

Public health weekly report·2026
Same author

Time to Recovery from Long COVID: A Longitudinal Analysis of Symptom Duration and Risk Factors Using Accelerated Failure Time Models.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

Innovative Facial Contouring Using a Monopolar Radiofrequency Device with Continuous Water Cooling: An Integrated Clinical and Preclinical Study.

International journal of molecular sciences·2026
Same author

Small-molecule modulation of β-arrestins.

Nature·2026
Same author

Solvent-triggered reconfiguration of optical physical unclonable functions.

Nature communications·2026
Same author

Translational Evaluation of a Disodium Adenosine Monophosphate (AMP2Na)-Based Topical Formulation for Physiology-Aligned Skin Rejuvenation: Integrated In Vitro, Ex Vivo, and Clinical Evidence.

International journal of molecular sciences·2026

相关实验视频

Updated: Sep 15, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

913

通过双重多任务高斯过程来预测COVID-19.

Sooyon Kim1, Yongtaek Lim2, Sungjun Lim3

  • 1Department of Statistics, Ohio State University, 1958 Neil Ave, Columbus, 43210, OH, United States.

Journal of biomedical informatics
|July 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的双重多任务高斯过程 (DMTGP) 模型,用于预测COVID-19病例和死亡. 该DMTGP模型有效地捕捉了跨国相关性,在多任务时间序列预测中表现优于其他方法.

关键词:
这是高斯过程.多任务学习是多任务学习.多变量时间序列预测.

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

相关实验视频

Last Updated: Sep 15, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

913
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

科学领域:

  • 计算流行病学计算流行病学
  • 机器学习用于公共卫生
  • 时间序列分析分析时间序列分析

背景情况:

  • 准确预测COVID-19病例和死亡人数对于公共卫生反应至关重要.
  • 现有的模型经常与多个国家,多任务时间序列数据的复杂,相关性质作斗争.
  • 了解国家间的动态对于有效的流行病管理至关重要.

研究的目的:

  • 提出一种新的双重多任务高斯过程 (DMTGP) 模型,用于同时预测COVID-19确诊病例和死亡.
  • 结合任务智能相关性,利用个人 (任务特定) 和共享 (跨任务) 信息.
  • 通过注意力机制,模拟和分析多个国家之间的动态关系.

主要方法:

  • 开发双重多任务高斯过程 (DMTGP) 模型.
  • 使用变压器编码器层进行交叉注意,以建模国家间的互动.
  • 为日本,韩国和台湾建立数据库,重点关注确诊病例和死亡.
  • 对注意力得分图的定性分析用于解释模型行为.

主要成果:

  • 与基线模型相比,DMTGP模型在处理双重多重任务方面表现优越.
  • 该模型成功预测了在选定的东亚国家确诊病例和死亡人数.
  • 注意分数分析证实了该模型能够捕捉各国之间的动态,时间变化的关系.

结论:

  • 拟议的DMTGP模型对于多任务,时间序列预测问题与相关数据有效.
  • 结合交叉任务相关性和注意力机制,可以提高流行病学建模中的预测准确性.
  • 该框架为了解和预测不同地区传播的疾病提供了强有力的方法.