Interpretable Machine Learning Model for Predicting Prolonged Postoperative Length of Stay in Lung Cancer Patients Undergoing Day Surgery: A Retrospective Cohort Study
The developed machine learning model accurately predicts prolonged p-LOS risk in lung cancer patients following day surgery, with high discriminative ability and interpretability. It shows potential for supporting early clinical intervention and optimizing postoperative care.
Authors
- Li Liu (ORCID: https://orcid.org/0000-0002-9731-6145)
- Hui Yuan (ORCID: https://orcid.org/0000-0001-9739-3645)
- Leiwen Tang (ORCID: https://orcid.org/0000-0003-4058-0161)
- Meijuan Lan (ORCID: https://orcid.org/0000-0001-9601-4068)
- Liyan Gao (ORCID: https://orcid.org/0000-0001-9007-3825)
- Ning Han (ORCID: https://orcid.org/0000-0001-8480-138X)
- Xin Dong (ORCID: https://orcid.org/0000-0003-3487-4892)
- Jie Cao (ORCID: https://orcid.org/0000-0001-9988-478X)
- Xiaoyan Wu
Institutions
- Second Affiliated Hospital of Zhejiang University (CN)
Publication Details
- Journal
- Journal of Craniofacial Surgery
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1097/scs.0000000000012152
- Primary Topic
- Enhanced Recovery After Surgery
- Type
- article
- Field-Weighted Citation Impact
- 0.00