Machine learning-based prediction of prolonged air leak after uniportal video-assisted thoracic surgery segmentectomy
Liang Chen, Ting Yu, Yanqing Pan, Guodong Ma · 2026
Read Machine learning-based prediction of prolonged air leak after uniportal video-assisted thoracic surgery segmentectomy by Liang Chen, Ting Yu, Yanqing Pan, Guodong Ma (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13327852.txt.
Description
Liang Chen, Ting Yu, and Yanqing Pan build machine learning forecasts for a lung surgery complication in a 2026 study, working with Guodong Ma. Prolonged air leak, lasting over five days, follows keyhole lung segment removal in many patients and stretches hospital stays. Records covered 610 patients treated between January 2019 and September 2025.
Five learning methods were trained on 70 percent of cases and tested on the rest, using health, lung function, surgery, and tumor details. Air leak occurred in 76 patients, 12.46 percent of the group, and the XGBoost model forecast it best, reaching an AUC of 0.874 on the test set, ahead of standard regression. Explanation tools ranked which factors drove each forecast.
Catalog record
| CREATOR | Liang Chen, Ting Yu, Yanqing Pan, Guodong Ma |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 22638 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |