Using machine learning methods to predict prolonged operative time in elective total shoulder arthroplasty

Lopez, C. D., Constant, M., Anderson, M. JJ., Confino, J. E., Lanham, N. S., & Jobin, C. M. (2022). Using machine learning methods to predict prolonged operative time in elective total shoulder arthroplasty. Seminars in Arthroplasty: JSES, 32(3), 452–461. https://doi.org/10.1053/j.sart.2022.01.003
Authors:
Cesar D. Lopez
Michael Constant
Matthew JJ. Anderson
Jamie E. Confino
Nathan S. Lanham
Charles M. Jobin
Affiliated Authors:
Cesar D. Lopez
Michael Constant
Matthew JJ. Anderson
Jamie E. Confino
Nathan S. Lanham
Charles M. Jobin
Columbia Affiliation:
Author Keywords:
acs-nsqip
artificial intelligence
artificial neural network
boosted decision tree
machine learning
operative time
regression model
total shoulder arthroplasty
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Publication Type:
Article
Unique ID:
10.1053/j.sart.2022.01.003
DOI:
10.1053/j.sart.2022.01.003
Journal:
Seminars in Arthroplasty JSES
Publication Date:
Data Source:
Scopus
Source Link:
View Scopus Record

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