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: Oct 30, 2025

Reverse Total Shoulder Arthroplasty
10:10

Reverse Total Shoulder Arthroplasty

Published on: July 5, 2011

43.5K

Using machine learning methods to predict nonhome discharge after elective total shoulder arthroplasty.

Cesar D Lopez1, Michael Constant1, Matthew J J Anderson1

  • 1New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.

JSES International
|July 5, 2021
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

Return to Play, Performance, and Career Longevity After Shoulder Labral Repair in the National Football League: A Matched Cohort Analysis.

Orthopaedic journal of sports medicine·2026
Same author

Outcomes of reverse total shoulder arthroplasty in patients ≤65 years old.

Journal of shoulder and elbow arthroplasty·2026
Same author

Hip Arthroscopy for Femoroacetabular Impingement Syndrome With Periportal Capsulotomy Yields Shorter Procedure Times and Shorter Recovery Room Length of Stay Compared With Interportal Capsulotomy.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
Same author

Reverse total shoulder arthroplasty is safe and effective in patients ≥90 Years old.

JSES international·2026
Same author

Reply to Letter Regarding "Can a Large Language Model Interpret Data in the Electronic Health Record to Infer Minimum Clinically Important Difference Achievement of Knee Osteoarthritis Outcome Score Joint Replacement Score Following Total Total Knee Arthroplasty?"

The Journal of arthroplasty·2026
Same author

Optimizing Outcomes in Revision Anterior Cruciate Ligament Reconstruction.

Instructional course lectures·2025

Machine learning models accurately predict non-home discharge after total shoulder arthroplasty (TSA). Artificial neural networks (ANN) showed higher accuracy, aiding surgeons in preoperative planning and improving cost-efficiency.

Area of Science:

  • Orthopedic Surgery
  • Machine Learning
  • Health Informatics

Background:

  • Machine learning (ML) demonstrates promise in predicting orthopedic surgery outcomes.
  • Accurate prediction facilitates improved patient selection, risk stratification, and preoperative planning.

Purpose of the Study:

  • Develop and evaluate ML models for predicting non-home discharge after total shoulder arthroplasty (TSA).
  • Identify key factors associated with non-home discharge in TSA patients.

Main Methods:

  • Utilized the American College of Surgeons National Surgical Quality Improvement Program database (2012-2018).
  • Developed boosted decision tree and artificial neural network (ANN) models to predict non-home discharge and 30-day complications.
  • Assessed model performance using Area Under the Receiver Operating Characteristic Curve (AUC) and overall accuracy.
Keywords:
ACS-NSQIPArtificial intelligenceArtificial neural networkDeep learningDischarge dispositionMachine learningNonhome dischargeTotal shoulder arthroplasty

More Related Videos

Rat Model of Adhesive Capsulitis of the Shoulder
04:46

Rat Model of Adhesive Capsulitis of the Shoulder

Published on: September 28, 2018

7.6K

Related Experiment Videos

Last Updated: Oct 30, 2025

Reverse Total Shoulder Arthroplasty
10:10

Reverse Total Shoulder Arthroplasty

Published on: July 5, 2011

43.5K
Rat Model of Adhesive Capsulitis of the Shoulder
04:46

Rat Model of Adhesive Capsulitis of the Shoulder

Published on: September 28, 2018

7.6K

Main Results:

  • Analysis included 21,544 elective TSA cases.
  • Multivariate logistic regression identified significant predictors of non-home discharge (e.g., female sex, age >70, ASA class ≥3, comorbidities, operative time).
  • ANN model achieved an AUC of 0.851 for predicting non-home discharge, outperforming the boosted decision tree model (AUC 0.788).

Conclusions:

  • Both ML models effectively predicted non-home discharge after TSA.
  • ANN models demonstrated superior discriminative ability.
  • ML tools can enhance preoperative discharge planning, optimize patient expectations, reduce hospital stays, and improve cost-efficiency.