Machine learning of intraoperative variables to test feasibility of multivariable prediction modelling for postoperative complications in thoracic surgery: a prospective cohort study.
- DOI
- 10.21037/jtd-2025-1-2513
- Published
- 2026 Apr 30
- Container
- Journal of thoracic disease
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.21037/jtd-2025-1-2513,
title = {Machine learning of intraoperative variables to test feasibility of multivariable prediction modelling for postoperative complications in thoracic surgery: a prospective cohort study.},
author = {Kidane B and Ul Aftab A and Peters EJ and Srinathan S and Buduhan G and Tan L and Poole E and Domaratzki M},
year = {2026},
journal = {Journal of thoracic disease},
doi = {10.21037/jtd-2025-1-2513},
url = {https://doi.org/10.21037/jtd-2025-1-2513}
}RIS
TY - JOUR TI - Machine learning of intraoperative variables to test feasibility of multivariable prediction modelling for postoperative complications in thoracic surgery: a prospective cohort study. AU - Kidane B AU - Ul Aftab A AU - Peters EJ AU - Srinathan S AU - Buduhan G AU - Tan L AU - Poole E AU - Domaratzki M PY - 2026 JO - Journal of thoracic disease DO - 10.21037/jtd-2025-1-2513 UR - https://doi.org/10.21037/jtd-2025-1-2513 ER -
APA
B, K., A, U. A., EJ, P., S, S., G, B., L, T., E, P., & M, D. (2026). Machine learning of intraoperative variables to test feasibility of multivariable prediction modelling for postoperative complications in thoracic surgery: a prospective cohort study.. Journal of thoracic disease. https://doi.org/10.21037/jtd-2025-1-2513
Source records
- pubmed · retrieved 2026-09-26T21:53:47.609Z