Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy.

Journal: Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Published Date:

Abstract

PURPOSE: To (1) develop and validate a machine learning algorithm to predict clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing patients with individual risk profiles to determine their propensity to gain clinically significant improvements in function.

Authors

  • Kyle N Kunze
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
  • Evan M Polce
    Department of Orthopaedic Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Benedict U Nwachukwu
    Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A.
  • Jorge Chahla
    Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.. Electronic address: jachahla@msn.com.
  • Shane J Nho
    Departments of Sports Medicine and Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA.