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

You might also read

Related Articles

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

Sort by
Same author

Process-dependent niches of rpf-harboring microorganisms regulate nitrogen and carbon functional networks in full-scale activated sludge.

Environmental research·2026
Same author

Debt as a blessing: A capital screening mechanism.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Bifunctional Photochemical Performance Based on Pt-TiO<sub>2</sub> Hollow Sphere Schottky Junction: From Photocatalytic Hydrogen Production to Highly Sensitive Glucose Sensing.

Inorganic chemistry·2026
Same author

Amphiphilic Bonding Intercalation Reshapes Active Sites and Interlayer Microenvironment for Selective and Stable Seawater Oxidation.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

PAIRMAP: A Unified Geometry-Aware Pairwise-Map Framework for Molecular Representation Learning.

Journal of chemical information and modeling·2026
Same author

Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education.

BMC medical education·2026

Related Experiment Video

Updated: Jun 1, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K

A recurrence model for non-puerperal mastitis patients based on machine learning.

Gaosha Li1,2, Qian Yu1, Feng Dong3

  • 1Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.

Plos One
|January 17, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately predicts non-puerperal mastitis (NPM) recurrence. This tool helps personalize treatment, improving outcomes and reducing relapse risk for patients with this inflammatory breast condition.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-&kappa;B Reporter Transgenic
08:51

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-κB Reporter Transgenic

Published on: September 4, 2012

19.2K

Related Experiment Videos

Last Updated: Jun 1, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-&kappa;B Reporter Transgenic
08:51

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-κB Reporter Transgenic

Published on: September 4, 2012

19.2K

Area of Science:

  • Oncology
  • Infectious Diseases
  • Immunology

Background:

  • Non-puerperal mastitis (NPM) is an inflammatory breast condition affecting women outside of lactation.
  • NPM has a high propensity for recurrence, necessitating effective prediction strategies.
  • Current diagnostic methods lack predictive models for NPM recurrence.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting non-puerperal mastitis recurrence.
  • To identify key predictive factors for NPM recurrence using data analysis.

Main Methods:

  • Retrospective analysis of laboratory data from 120 NPM patients.
  • Development of a predictive model using logistic regression, XGBoost, Random Forest, and AdaBoost algorithms.
  • Validation of the model using internal test and external validation cohorts.

Main Results:

  • A logistic regression model was selected as the optimal predictor, utilizing FIB, bacterial infection, and CD4+ T cell count.
  • The model achieved an AUC of 0.846 in the training set and 0.833 in the test set.
  • External validation demonstrated strong performance with an AUC of 0.825.

Conclusions:

  • The developed machine learning model effectively predicts NPM recurrence.
  • This tool supports personalized adjuvant therapy decisions for NPM patients.
  • The model aids in enhancing therapeutic efficacy and minimizing recurrence rates.