Development and external validation of an explainable machine learning model using routine perioperative clinical variables for postoperative pulmonary complication risk stratification after hepatectomy for primary liver cancer
- DOI
- 10.21037/jgo-2026-0702
- Published
- 2026-10
- Container
- Journal of Gastrointestinal Oncology
- Publisher
- AME Publishing Company
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.21037/jgo-2026-0702,
title = {Development and external validation of an explainable machine learning model using routine perioperative clinical variables for postoperative pulmonary complication risk stratification after hepatectomy for primary liver cancer},
author = {Lijia Lu and Yu Ma and Qiuli Li},
year = {2026},
journal = {Journal of Gastrointestinal Oncology},
doi = {10.21037/jgo-2026-0702},
url = {https://doi.org/10.21037/jgo-2026-0702}
}RIS
TY - JOUR TI - Development and external validation of an explainable machine learning model using routine perioperative clinical variables for postoperative pulmonary complication risk stratification after hepatectomy for primary liver cancer AU - Lijia Lu AU - Yu Ma AU - Qiuli Li PY - 2026 JO - Journal of Gastrointestinal Oncology DO - 10.21037/jgo-2026-0702 UR - https://doi.org/10.21037/jgo-2026-0702 ER -
APA
Lu, L., Ma, Y., & Li, Q. (2026). Development and external validation of an explainable machine learning model using routine perioperative clinical variables for postoperative pulmonary complication risk stratification after hepatectomy for primary liver cancer. Journal of Gastrointestinal Oncology. https://doi.org/10.21037/jgo-2026-0702
Source records
- crossref · retrieved 2026-11-09T07:31:39.022Z