Time-to-event ensemble machine learning approach for predicting long-term survival of abdominal aortic aneurysm patients undergoing endovascular aneurysm repair
- DOI
- 10.1371/journal.pone.0349122
- Published
- 2026-06-12
- Container
- PLOS One
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0349122,
title = {Time-to-event ensemble machine learning approach for predicting long-term survival of abdominal aortic aneurysm patients undergoing endovascular aneurysm repair},
author = {Hong-Jae Choi and Changhee Lee and Joon Seo Lim and You Jung Ok and Jae-Sung Choi and Jae Hwa Jeong and Yong Won Seong and Hyeon Jong Moon and Se Jin Oh},
year = {2026},
journal = {PLOS One},
doi = {10.1371/journal.pone.0349122},
url = {https://doi.org/10.1371/journal.pone.0349122}
}RIS
TY - JOUR TI - Time-to-event ensemble machine learning approach for predicting long-term survival of abdominal aortic aneurysm patients undergoing endovascular aneurysm repair AU - Hong-Jae Choi AU - Changhee Lee AU - Joon Seo Lim AU - You Jung Ok AU - Jae-Sung Choi AU - Jae Hwa Jeong AU - Yong Won Seong AU - Hyeon Jong Moon AU - Se Jin Oh PY - 2026 JO - PLOS One DO - 10.1371/journal.pone.0349122 UR - https://doi.org/10.1371/journal.pone.0349122 ER -
APA
Choi, H., Lee, C., Lim, J. S., Ok, Y. J., Choi, J., Jeong, J. H., Seong, Y. W., Moon, H. J., & Oh, S. J. (2026). Time-to-event ensemble machine learning approach for predicting long-term survival of abdominal aortic aneurysm patients undergoing endovascular aneurysm repair. PLOS One. https://doi.org/10.1371/journal.pone.0349122
Source records
- crossref · retrieved 2026-09-25T21:52:26.713Z