Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty? Academic Article uri icon

Overview

MeSH Major

  • Arthroplasty, Replacement, Knee
  • Knee Prosthesis
  • Prosthesis-Related Infections

abstract

  • Machine learning has the potential to improve clinical decision-making and patient care by helping to prioritize resources for postsurgical monitoring and informing presurgical discussions of likely outcomes of TJA. Applied to presurgical registry data, such models can predict, with fair-to-good ability, 2-year postsurgical MCIDs. Although we report all parameters of our best-performing models, they cannot simply be applied off-the-shelf without proper testing. Our analyses indicate that machine learning holds much promise for predicting orthopaedic outcomes.  LEVEL OF EVIDENCE: Level III, diagnostic study.

publication date

  • June 2019

Research

keywords

  • Academic Article

Identity

Language

  • eng

Digital Object Identifier (DOI)

  • 10.1097/CORR.0000000000000687

PubMed ID

  • 31094833

Additional Document Info

start page

  • 1267

end page

  • 1279

volume

  • 477

number

  • 6