Open AccessWhat Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?
Author(s) -
Vikas Kumar,
Christopher P. Roche,
Steven Overman,
Ryan W. Simovitch,
Pierre-Henri Flurin,
Thomas W. Wright,
Joseph D. Zuckerman,
Howard Routman,
Ankur Teredesai
Publication year - 2020
Publication title -
clinical orthopaedics and related research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.178
H-Index - 204
eISSN - 1528-1132
pISSN - 0009-921X
DOI - 10.1097/corr.0000000000001263
Subject(s) - medicine , arthroplasty , minimal clinically important difference , machine learning , physical therapy , elbow , artificial intelligence , physical medicine and rehabilitation , surgery , randomized controlled trial , computer science
Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder arthroplasty, machine learning appears to have the potential to anticipate patients' results after surgery, but this has not been well explored.