Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

5.6K
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...
5.6K
Survival Tree01:19

Survival Tree

497
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
497
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

3.0K
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...
3.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction.

Transactions on machine learning research·2026
Same author

Transferability of Quiet Eye Training Improvements to Novel Aiming Tasks.

Journal of sport & exercise psychology·2026
Same author

The deacetylase NagA mediates the remodeling and recycling of peptidoglycan-derived amino sugars in mycobacteria.

The Journal of biological chemistry·2025
Same author

Hierarchical Active Learning with Label Proportions on Data Regions.

IEEE transactions on knowledge and data engineering·2025
Same author

Augmentation-Free Contrastive Learning for EKG Classification.

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )·2025
Same author

Anti-PF4 mediated thrombocytopenia and thrombosis associated with acute cytomegalovirus infection displays both HIT-like and VITT-like characteristics.

British journal of haematology·2025
Same journal

A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2026
Same journal

Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
Same journal

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining·2025
See all related articles

Related Experiment Video

Updated: Apr 13, 2026

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

6.6K

Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data.

Iyad Batal1, Dmitriy Fradkin2, James Harrison3

  • 1Dept. of Computer Science, University of Pittsburgh, iyad@cs.pitt.edu.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|May 5, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a temporal pattern mining framework to detect adverse conditions in diabetic patients. The method efficiently finds predictive patterns in complex time series data for improved health monitoring.

Keywords:
Event DetectionPatient ClassificationTemporal AbstractionsTemporal Pattern MiningTime-interval Patterns

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.7K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K

Related Experiment Videos

Last Updated: Apr 13, 2026

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

6.6K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.7K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K

Area of Science:

  • Data Mining
  • Machine Learning
  • Health Informatics

Background:

  • Classifier performance enhancement via pattern mining is a key research area.
  • Monitoring and event detection in multivariate time series data present significant challenges.

Purpose of the Study:

  • To introduce a novel temporal pattern mining framework for time series analysis.
  • To apply this framework for detecting and diagnosing adverse medical conditions in diabetic patients.

Main Methods:

  • Converting time series data into time-interval sequences using temporal abstractions.
  • Constructing complex temporal patterns by analyzing data backward in time with temporal operators.
  • Applying the framework to a large dataset of 13,558 diabetic patients.

Main Results:

  • The framework efficiently identifies useful patterns for diabetes-associated adverse condition detection.
  • Demonstrated benefits in analyzing complex multivariate time series data.
  • Successfully applied to a real-world healthcare dataset.

Conclusions:

  • The temporal pattern mining framework is effective for monitoring and event detection in complex time series.
  • The approach offers significant advantages for identifying health risks in diabetic populations.
  • This method can improve the diagnosis and management of diabetes-related complications.