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

Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
Causality in Epidemiology01:21

Causality in Epidemiology

227
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
227
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

234
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...
234
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

22
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
22
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K

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

Measuring Breast Density at Scale With AI: Insights From BreastScreen NSW.

Journal of medical imaging and radiation oncology·2026
Same author

Build fair machine learning models to predict adverse outcomes for heart failure patients with preserved ejection fraction and with reduced ejection fraction.

JAMIA open·2026
Same author

Knowledge Graph Augmented Large Language Models for Disease Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Why Empirical Risk Minimization Performs Well for Open Set Domain Adaptation: A Theoretical Analysis From Causal View.

IEEE transactions on neural networks and learning systems·2026

相关实验视频

Updated: May 24, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

意识到因果关系的时空图 神经网络用于时空时间序列推算.

Baoyu Jing1, Dawei Zhou2, Kan Ren3

  • 1University of Illinois, Urbana-Champaign, IL, USA.

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management
|March 5, 2025
PubMed
概括

这项研究引入了Casper,这是一种用于空间时空时间序列归算的新方法,它使用因果关系来避免过拟合. 卡斯珀通过专注于因果关系,有效地归因缺失的数据,优于现有技术.

关键词:
引起注意的因果关系.时间空间图 神经网络 神经网络时空空间时间序列推算.

更多相关视频

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.8K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K

相关实验视频

Last Updated: May 24, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.8K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 因果推理因果推理

背景情况:

  • 时空时间序列数据经常因传感器故障而缺失值.
  • 现有的归算方法可能会因使用混因子引入的非因果相关性而过度适应.

研究的目的:

  • 提出一种基于因果关系的方法,用于空间时空时间序列的归算.
  • 开发一种新的神经网络模型,以解释因果关系.

主要方法:

  • 从因果角度重新审视时空归因,使用前门调整.
  • 引入因果意识的时空图神经网络 (Casper) 带有即时基于解码器 (PBD) 和时空因果注意 (SCA).

主要成果:

  • 卡斯珀有效地减少了混因素的影响,并识别了稀疏的因果关系.
  • 理论分析表明,SCA通过梯度值发现因果关系.
  • 实验结果表明,Casper在现实数据集上的基线方法中表现优越.

结论:

  • 卡斯珀通过利用因果推理,为时空时间序列归算提供了一种有效和强大的解决方案.
  • 该模型成功地缓解了由非因果相关性引起的过拟合问题.