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Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients
Research article (Journal of Plastic Reconstructive & Aesthetic Surgery, 2023) · cited 10× · AI/ML
Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients
Summary
Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients is a scholarly article[1].
Key Facts
Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients. Retrieved May 24, 2026, from https://4ort.xyz/entity/applying-unsupervised-machine-learning-approaches-to-characterize-autologous-breast-reconstruction-patient-subgroups-an-
MLA“Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/applying-unsupervised-machine-learning-approaches-to-characterize-autologous-breast-reconstruction-patient-subgroups-an-.
BibTeX@misc{4ortxyz_applying-unsupervised-machine-learning-approaches-to-characterize-autologous-breast-reconstruction-patient-subgroups-an-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients}}, year = {2026}, url = {https://4ort.xyz/entity/applying-unsupervised-machine-learning-approaches-to-characterize-autologous-breast-reconstruction-patient-subgroups-an-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients — https://4ort.xyz/entity/applying-unsupervised-machine-learning-approaches-to-characterize-autologous-breast-reconstruction-patient-subgroups-an- (retrieved 2026-05-24)
