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

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

358
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
358
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

621
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
621
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

822
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
822
Classification of Illness01:17

Classification of Illness

8.9K
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...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Impact of harmonization on predicting complications in head and neck cancer after radiotherapy using MRI radiomics and machine learning techniques.

Medical physics·2025
Same author

Factors influencing accessibility of palliative care: a systematic review and meta-analysis.

BMC palliative care·2025
Same author

Effects of fat emulsion-based early parenteral nutrition for patients after hemihepatectomy.

The British journal of nutrition·2025
Same author

Electrochemically Assisted Calcium Silicate Utilization for Phosphate Recovery.

Environmental science & technology·2025
Same author

Effects of psychosocial factors on postpartum depression: a half-longitudinal mediation analysis of cognitive reactivity.

BMC pregnancy and childbirth·2025
Same author

Combination autologous stem cell transplantation with chimeric antigen receptor T-cell therapy for refractory/relapsed B-cell lymphoma: a single-arm clinical study.

Frontiers in immunology·2025

Related Experiment Video

Updated: Feb 17, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

Yang Lei1, Dai Yu2, Zhang Bin1

  • 1College of Computer Science and Technology, Northeastern University, Shenyang, China.

Computational and Mathematical Methods in Medicine
|December 12, 2017
PubMed
Summary

This study introduces an interactive K-means clustering method for medical data analysis. It enhances clustering results by incorporating user feedback and optimizing parameters using particle swarm optimization for better business goal alignment.

More Related Videos

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

11.9K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Related Experiment Videos

Last Updated: Feb 17, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K
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

11.9K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Area of Science:

  • Data Science
  • Medical Informatics
  • Machine Learning

Background:

  • Clustering algorithms are vital for data analysis but struggle with high-dimensional data, particularly in medicine.
  • Standard clustering may overlook crucial business relationships, leading to results misaligned with user objectives.
  • Integrating user knowledge, such as physician expertise, is key to improving clustering relevance.

Purpose of the Study:

  • To propose an interactive K-means clustering method that enhances user satisfaction with analysis results.
  • To address the limitations of traditional clustering in high-dimensional medical data.
  • To align clustering outcomes with specific user business goals and analytical intents.

Main Methods:

  • An interactive K-means clustering approach incorporating user feedback for iterative refinement.
  • Utilizing particle swarm optimization to tune algorithm parameters, focusing on weight settings.
  • Optimizing parameters to reflect user business preferences and analytical requirements.
  • Validation using a breast cancer dataset to demonstrate practical application.

Main Results:

  • The proposed interactive method significantly improves user satisfaction with clustering outcomes.
  • Parameter optimization via particle swarm optimization effectively incorporates user preferences.
  • The method demonstrates superior performance compared to standard clustering techniques in the breast cancer case study.
  • Enhanced clustering results are more aligned with user-defined business goals.

Conclusions:

  • Interactive K-means clustering, enhanced by user feedback and particle swarm optimization, offers a powerful solution for medical data analysis.
  • This approach effectively bridges the gap between algorithmic results and user-specific business needs.
  • The method shows promise for improving the utility and relevance of clustering in clinical and research settings.