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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

15.4K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
15.4K
Time-Series Graph00:54

Time-Series Graph

4.7K
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...
4.7K
Treatment Strategies for Psychological Disorders01:24

Treatment Strategies for Psychological Disorders

337
Treatment approaches for psychological disorders fall into three main categories: psychological, biological, and sociocultural. Each approach targets different aspects of mental health, requiring varying levels of education and training.
Psychological therapies focus on modifying emotions, thoughts, and behaviors through talking, interpreting, listening, rewarding, challenging, and modeling. Clinical psychologists, counselors, and social workers commonly practice psychotherapy. Clinical...
337
Vertical Curve: Problem Solving01:23

Vertical Curve: Problem Solving

211
Vertical curves provide the transition between two roadway grades, ensuring safety, comfort, and functionality. Calculating elevations at specific stations along the curve involves several systematic steps based on the curve's geometry and provided design parameters.The vertical curve is defined by its length, grades, Point of Vertical Intersection (P.V.I.) location, and P.V.I. elevation. The stations of the Point of Vertical Curvature (P.V.C.), where the curve begins, and the Point of Vertical...
211
Ogive Graph01:07

Ogive Graph

6.1K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.1K
Survival Curves01:18

Survival Curves

374
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
374

You might also read

Related Articles

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

Sort by
Same author

The Proteomic and Peptidomic Response of Wheat (<i>Triticum aestivum</i> L.) to Drought Stress.

Plants (Basel, Switzerland)·2025
Same author

Multi-agent norm perception and induction in distributed healthcare.

Journal of biomedical informatics·2025
Same author

Modelling diversity in hospital strategies in city-scale ambulance dispatching with coupled game-theoretic model and discrete-event simulation.

Journal of biomedical informatics·2025
Same author

Mesenchymal stem cells-derived extracellular vesicles for therapeutics of renal tuberculosis.

Scientific reports·2024
Same author

Machine Learning Methods for Pregnancy and Childbirth Risk Management.

Journal of personalized medicine·2023
Same author

Conjugated Dienoic Acid Peroxides as Substrates in <i>Chaetopterus</i> Bioluminescence System.

International journal of molecular sciences·2023

Related Experiment Video

Updated: Oct 14, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.6K

Treatment Trajectories Graph Compression Algorithm Based on Cliques.

Svetozar Milykh1, Sergey Kovalchuk1

  • 1ITMO University, Saint Petersburg, Russia.

Studies in Health Technology and Informatics
|November 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a graph compression method to improve understanding of medical data, specifically for acute coronary syndrome treatment. The technique enhances data interpretability for better medical insights.

Keywords:
Graph compressiongraph entropytreatment trajectory

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions
08:12

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

Published on: July 11, 2017

7.5K

Related Experiment Videos

Last Updated: Oct 14, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.6K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions
08:12

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

Published on: July 11, 2017

7.5K

Area of Science:

  • Medical Informatics
  • Graph Theory
  • Data Visualization

Background:

  • Understanding disease progression and treatment efficacy is crucial in medicine.
  • Graph representations offer powerful tools for visualizing and optimizing complex data structures.
  • Interpreting intricate medical datasets remains a challenge.

Purpose of the Study:

  • To propose a novel data processing method for enhancing information interpretability in medical data.
  • To apply graph compression techniques to analyze patient treatment trajectories.
  • To evaluate the effectiveness of the proposed method using graph entropy measures.

Main Methods:

  • A graph compression algorithm utilizing maximum clique search was developed.
  • The algorithm was applied to a dataset of acute coronary syndrome (ACS) treatment trajectories.
  • Graph entropy measures were employed to assess the results of data compression.

Main Results:

  • The graph compression algorithm successfully reduced data complexity.
  • Enhanced interpretability of acute coronary syndrome treatment pathways was observed.
  • Graph entropy analysis confirmed the effectiveness of the compression technique.

Conclusions:

  • The proposed graph compression method significantly improves the interpretability of medical data.
  • This approach offers a valuable tool for analyzing complex patient treatment trajectories.
  • Further research can explore applications in other complex diseases and treatment regimens.