Reducing Bias in the Evaluation of Robotic Surgery for Lung Cancer Through Machine Learning.

ABSTRACT:

Background

Robot-assisted surgery (RAS) is a major innovation in the treatment of lung cancer, offering advantages in surgical precision and reducing postoperative complications. However, its impact on 90-day mortality remains controversial due to methodological biases in comparative studies. This study uses machine learning methods to improve propensity score estimation and reduce selection bias.

Methods

We used the French national hospital database (PMSI) to identify patients who underwent lung resection for cancer between 2019 and 2023. Four models were applied for propensity score estimation: logistic regression, Random Forest, Gradient Boosting Machine (GBM), and XGBoost. Group balancing was achieved through propensity score weighting and matching, followed by logistic

SUBMITTER: Bernard A 

PROVIDER: S-EPMC12563986 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature