Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations.

ABSTRACT:

Background

Postoperative pulmonary complications (PPCs) are common and have a negative impact on postoperative morbidity and mortality, with associated medical resource use and cost care plan. Management of preoperative and intraoperative risk factors has been shown to reduce the occurrence of PPCs. Therefore, this study aimed to develop a risk prediction model for PPCs based on explainable machine learning (ML) methods and evaluate its predictive performance in order to enhance the prevention and intervention for PPCs.

Methods

In this study, the medical records of 1,629 patients who underwent thoracoscopic surgery were collected from two clinical groups at the Affiliated Hospital of Guangdong Medical University between August 2018 and October 2021. Five categories of data w

SUBMITTER: Wang S 

PROVIDER: S-EPMC12268710 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature