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 Experiment Video

Updated: Jan 18, 2026

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain
08:03

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain

Published on: February 22, 2019

8.9K

Developing and validating a machine learning model to predict chronic pain following total knee arthroplasty.

Ziliang Cheng1, Jingjing Li1, Weishan Wu2

  • 1Shandong University of Traditional Chinese Medicine, Jinan, Shangdong, China.

The Knee
|May 22, 2025
PubMed
Summary

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

A comparative study of the '3 + 2' model 'seven-step method' programmed robotic distal gastric cancer radical resection versus conventional robotic surgical techniques.

World journal of surgical oncology·2026
Same author

Effects of Blood Pressure and Antihypertensive Drugs on Rheumatoid Arthritis: A Mendelian Randomization, Network Pharmacology, and Experimental Validation.

International journal of general medicine·2026
Same author

Integrated Analysis Reveals Tubal- and Ovarian-Originated Serous Ovarian Cancer and Predicts Differential Therapeutic Responses.

Clinical cancer research : an official journal of the American Association for Cancer Research·2017
Same author

Inverse Association between Ambient Sulfur Dioxide Exposure and Semen Quality in Wuhan, China.

Environmental science & technology·2017
Same author

Bacterial and Archaeal Lipids Recovered from Subsurface Evaporites of Dalangtan Playa on the Tibetan Plateau and Their Astrobiological Implications.

Astrobiology·2017
Same author

Genistein suppresses aerobic glycolysis and induces hepatocellular carcinoma cell death.

British journal of cancer·2017

Machine learning models can predict chronic pain after total knee arthroplasty (CPSP). The Random Forest model demonstrated the best performance in identifying high-risk patients, aiding early intervention.

Area of Science:

  • Orthopedics
  • Medical Informatics
  • Pain Management

Background:

  • Chronic pain after total knee arthroplasty (CPSP) significantly impacts patient function.
  • Early identification of patients at risk for CPSP is crucial for effective management.
  • Machine learning (ML) offers potential for developing predictive models for CPSP.

Purpose of the Study:

  • To compare the performance of various ML algorithms in predicting CPSP.
  • To identify key risk factors associated with CPSP development.
  • To determine the most effective ML model for CPSP prediction.

Main Methods:

  • A retrospective cohort of 785 TKA patients was analyzed.
  • Nine high-risk factors were identified using LASSO regression.
Keywords:
Chronic postsurgical painMachine learningTotal knee arthroplasty

More Related Videos

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
14:56

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

Published on: January 27, 2010

21.9K

Related Experiment Videos

Last Updated: Jan 18, 2026

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain
08:03

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain

Published on: February 22, 2019

8.9K
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
14:56

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

Published on: January 27, 2010

21.9K
  • Five ML models (DT, LGBM, SVM, RF, XGBoost) were trained and evaluated using AUC and Brier scores.

    • Main Results:

    • The overall incidence of CPSP was 39.6%.
    • Nine significant risk factors for CPSP were identified.
    • The Random Forest (RF) model achieved the highest AUC (0.918) and lowest Brier score (0.111), indicating superior predictive performance.

    Conclusions:

    • CPSP is a prevalent complication following TKA, necessitating clinical attention.
    • The identified nine risk factors provide insights into CPSP etiology.
    • The RF model effectively predicts CPSP, enabling early identification and intervention for at-risk patients.