Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients

Kim, D. K., Corpuz, G. S., Ta, C. N., Weng, C., & Rohde, C. H. (2024). Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients. Journal of Plastic, Reconstructive & Aesthetic Surgery, 88, 330–339. https://doi.org/10.1016/j.bjps.2023.11.016
Authors:
Dylan K Kim
George S Corpuz
Casey N Ta
Chunhua Weng
Christine H Rohde
Affiliated Authors:
Dylan K Kim
George S Corpuz
Casey N Ta
Chunhua Weng
Christine H Rohde
Columbia Affiliation:
Subjects:
Mammaplasty (MeSH)
Free Tissue Flaps (MeSH)
Breast Neoplasms (MeSH)
Author Keywords:
autologous
breast reconstruction
clustering
machine learning
unsupervised
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Publication Type:
Article
Unique ID:
10.1016/j.bjps.2023.11.016
PMID:
38061257
DOI:
10.1016/j.bjps.2023.11.016
Journal:
Journal of Plastic, Reconstructive and Aesthetic Surgery
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record
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Record Created: