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

Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
Survival Tree01:19

Survival Tree

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 survival tree begins...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

You might also read

Related Articles

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

Sort by
Same author

NR4A1 limits CD8<sup>+</sup> T Cell effector responses and protection in tuberculosis.

Research square·2026
Same author

NR4A1 limits CD8⁺ T Cell effector responses and protection in tuberculosis.

bioRxiv : the preprint server for biology·2026
Same author

Effect of LPS on In Vitro Embryo Development in Bubalus bubalis: A Model for Studying Inflammatory Pathways.

Reproduction in domestic animals = Zuchthygiene·2026
Same author

Divergent synthesis of 3-formylpyrazolo[1,5-<i>a</i>]pyrimidines and methylene-bridged bis(pyrazolo[1,5-<i>a</i>]pyrimidines) using DMSO as a single carbon synthon.

Organic & biomolecular chemistry·2025
Same author

Leishmania donovani infection-driven high levels of IL-10 causes hypoalbuminemia in human visceral leishmaniasis.

Cytokine·2025
Same author

Fabry Disease Podocytes Reveal Ferroptosis as a Potential Regulator of Cell Pathology.

Kidney international reports·2025
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: May 9, 2026

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

Temporal pattern mining for multivariate clinical decision support.

Sheetal Saini1, Sumeet Dua

  • 1Data Mining Research Laboratory, Computer Science, Louisiana Tech University, Ruston, LA, USA.

Studies in Health Technology and Informatics
|August 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to find hidden patterns in complex temporal data, improving healthcare decision support. The technique accurately detects localized changes, such as epileptic seizures from iEEG data.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: May 9, 2026

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

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Biomedical Informatics
  • Data Science
  • Signal Processing

Background:

  • High-throughput technologies generate vast amounts of multivariate temporal data, crucial for healthcare informatics and decision support.
  • Existing data analytics often miss localized behavioral changes in large datasets, limiting their effectiveness.
  • Electroencephalography (EEG) and intracranial EEG (iEEG) data are key examples of such complex temporal data.

Purpose of the Study:

  • To develop a novel two-step algorithmic methodology for uncovering complex temporal patterns.
  • To enhance patient-specific clinical decision support systems using discovered temporal patterns.
  • To improve the accuracy and efficiency of analyzing multivariate time series data.

Main Methods:

  • A two-step algorithmic approach was designed to identify localized temporal patterns.
  • The methodology focuses on discovering nontrivial and potentially useful temporal patterns.
  • The technique was applied to multivariate time series iEEG data for epileptic seizure detection.

Main Results:

  • The methodology successfully uncovered domain class-specific temporal patterns.
  • Classification results demonstrated high degrees of sensitivity and specificity.
  • The technique proved effective and accurate in detecting epileptic seizures from iEEG data.

Conclusions:

  • The proposed methodology enhances clinical decision support by uncovering previously unknown temporal patterns.
  • This approach offers a more sensitive and specific way to analyze complex temporal data.
  • The findings highlight the potential for improved patient-specific healthcare informatics.