Identification of risk factors for infection after mitral valve surgery through machine learning approaches.

ABSTRACT:

Background

Selecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.

Methods

Participants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vec

SUBMITTER: Zhang N 

PROVIDER: S-EPMC10294678 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature