Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.

ABSTRACT:

Background

Postoperative pulmonary infection (POI) is strongly associated with a poor prognosis and has a high incidence in elderly patients undergoing major surgery. Machine learning (ML) algorithms are increasingly being used in medicine, but the predictive role of logistic regression (LR) and ML algorithms for POI in high-risk populations remains unclear.

Methods

We conducted a retrospective cohort study of older adults undergoing major surgery over a period of six years. The included patients were randomly divided into training and validation sets at a ratio of 7:3. The features selected by the least absolute shrinkage and selection operator regression algorithm were used as the input variables of the ML and LR models. The random forest of multiple interpretable methods

SUBMITTER: Liu J 

PROVIDER: S-EPMC11921591 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature