Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

ABSTRACT:

Background

Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thromboembolism (VTE) risk. This study explored the VTE risk factors and established a machine-learning model to predict a failure of postoperative thromboprophylaxis.

Methods

This retrospective study included patients with NSCLC who underwent surgery between January 2018 and November 2022. The patients were randomized 7:3 to the training and test sets. Nine machine learning models were constructed. The three most predictive machine-learning classifiers were chosen as the first layer of the stacking machine-learning model, and logistic regression was the second layer of the meta-learning model.

Results

This study included 362 patients, including 58 (16.0%) with VTE. Based o

SUBMITTER: Hao L 

PROVIDER: S-EPMC11960935 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature