Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy.

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

PMID: 33359160

Abstract

PURPOSE: To develop machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy.

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.
Jonathan Rasio

Department of Orthopedic Surgery, Division of Sports Medicine, Section of Young Adult Hip Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.
Shane J Nho

Departments of Sports Medicine and Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA.

Keywords

Adult Algorithms Arthroscopy Calibration Case-Control Studies Female Hip Humans Machine Learning Male Neural Networks, Computer Patient Satisfaction Postoperative Period Risk ROC Curve Treatment Outcome Young Adult

External Resources

View on PubMed Access via DOI PubMed (33359160)

Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy.

Abstract

Authors

Keywords

External Resources

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