Machine learning-based assessments of perioperative features in determining long-term conduct block post-transcatheter closure of ventricular septal defect
- DOI
- 10.1017/s1047951126123506
- Published
- 2026-09-21
- Container
- Cardiology in the Young
- Publisher
- Cambridge University Press (CUP)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1017/s1047951126123506,
title = {Machine learning-based assessments of perioperative features in determining long-term conduct block post-transcatheter closure of ventricular septal defect},
author = {Qirun Wang and Yuting Xia and Li Wei and Penghui Yang and Kaiyu Zhou and Yimin Hua and Weikai Li and Yifei Li},
year = {2026},
journal = {Cardiology in the Young},
doi = {10.1017/s1047951126123506},
url = {https://doi.org/10.1017/s1047951126123506}
}RIS
TY - JOUR TI - Machine learning-based assessments of perioperative features in determining long-term conduct block post-transcatheter closure of ventricular septal defect AU - Qirun Wang AU - Yuting Xia AU - Li Wei AU - Penghui Yang AU - Kaiyu Zhou AU - Yimin Hua AU - Weikai Li AU - Yifei Li PY - 2026 JO - Cardiology in the Young DO - 10.1017/s1047951126123506 UR - https://doi.org/10.1017/s1047951126123506 ER -
APA
Wang, Q., Xia, Y., Wei, L., Yang, P., Zhou, K., Hua, Y., Li, W., & Li, Y. (2026). Machine learning-based assessments of perioperative features in determining long-term conduct block post-transcatheter closure of ventricular septal defect. Cardiology in the Young. https://doi.org/10.1017/s1047951126123506
Source records
- crossref · retrieved 2026-09-25T09:15:39.522Z